Novakovic et al 2019 reanalysis
The methylation in ART meta-analysis group
2022-06-15
source("meth_functions.R")Obtaining array annotations
anno <- getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b4.hg19)
myann <- data.frame(anno[,c("UCSC_RefGene_Name","Regulatory_Feature_Group","Islands_Name","Relation_to_Island")])
promoters <- grep("Prom",myann$Regulatory_Feature_Group)Introduction
This report is the reanalysis of first analysed DNA methylation data by [Novakovic et al (2019)] (https://doi.org/10.1038/s41467-019-11929-9)
In this study, DNA methylation status was generated for 149 neonatal (84♀ 65♂) and 158 adult (87♀ 71♂) ART-conceived individuals and for 58 neonatal (37♀, 21♂) and 75 adult (51♀, 24♂) non-ART conceived individuals.
WORKING_DIR="GSE131433"
ARRAY_DATA="GSE131433_RAW.tar"
DEST=paste(WORKING_DIR,"/",ARRAY_DATA,sep="")
if(!dir.exists(WORKING_DIR)){
dir.create(WORKING_DIR)
download.file("ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131433/suppl/GSE131433_RAW.tar",
destfile=DEST)
untar(exdir = "IDAT", tarfile = WORKING_DIR)
}
SERIES_MATRIX=paste(WORKING_DIR,"/","GSE131433_series_matrix.txt.gz",sep="")
download.file("ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131433/matrix/GSE131433_series_matrix.txt.gz",
destfile=SERIES_MATRIX)
gse <- getGEO(filename=SERIES_MATRIX)## Using locally cached version of GPL21145 found here:
## /tmp/Rtmpp8fd2J/GPL21145.soft.gzbaseDir <- "."
sample_metadata <- pData(phenoData(gse))
targets <- sample_metadata
files <- list.files(WORKING_DIR,pattern = "GSM",recursive = TRUE)
mybase <- unique(gsub("_Red.idat.gz" ,"", gsub("_Grn.idat.gz", "" ,files)))
mybase <- paste(WORKING_DIR,"/",mybase,sep="")
targets$Basename <- mybase
head(targets)## title geo_accession status
## GSM3780106 neonate_ART_Donor_1 GSM3780106 Public on Jul 29 2019
## GSM3780107 neonate_ART_Donor_2 GSM3780107 Public on Jul 29 2019
## GSM3780108 neonate_ART_Donor_3 GSM3780108 Public on Jul 29 2019
## GSM3780109 neonate_ART_Donor_4 GSM3780109 Public on Jul 29 2019
## GSM3780110 neonate_ART_Donor_5 GSM3780110 Public on Jul 29 2019
## GSM3780111 neonate_ART_Donor_6 GSM3780111 Public on Jul 29 2019
## submission_date last_update_date type channel_count
## GSM3780106 May 17 2019 Jul 31 2019 genomic 1
## GSM3780107 May 17 2019 Jul 31 2019 genomic 1
## GSM3780108 May 17 2019 Jul 31 2019 genomic 1
## GSM3780109 May 17 2019 Jul 31 2019 genomic 1
## GSM3780110 May 17 2019 Jul 31 2019 genomic 1
## GSM3780111 May 17 2019 Jul 31 2019 genomic 1
## source_name_ch1 organism_ch1 characteristics_ch1
## GSM3780106 ART_Donor_1 Homo sapiens gender: F
## GSM3780107 ART_Donor_2 Homo sapiens gender: M
## GSM3780108 ART_Donor_3 Homo sapiens gender: F
## GSM3780109 ART_Donor_4 Homo sapiens gender: M
## GSM3780110 ART_Donor_5 Homo sapiens gender: F
## GSM3780111 ART_Donor_6 Homo sapiens gender: F
## characteristics_ch1.1 characteristics_ch1.2
## GSM3780106 sample type: Guthrie blood spot art status: ART
## GSM3780107 sample type: Guthrie blood spot art status: ART
## GSM3780108 sample type: Guthrie blood spot art status: ART
## GSM3780109 sample type: Guthrie blood spot art status: ART
## GSM3780110 sample type: Guthrie blood spot art status: ART
## GSM3780111 sample type: Guthrie blood spot art status: ART
## characteristics_ch1.3 characteristics_ch1.4 molecule_ch1
## GSM3780106 art subtype: Frozen embryo time of collection: birth genomic DNA
## GSM3780107 art subtype: NA time of collection: birth genomic DNA
## GSM3780108 art subtype: Fresh embryo time of collection: birth genomic DNA
## GSM3780109 art subtype: Frozen embryo time of collection: birth genomic DNA
## GSM3780110 art subtype: Fresh embryo time of collection: birth genomic DNA
## GSM3780111 art subtype: Fresh embryo time of collection: birth genomic DNA
## extract_protocol_ch1
## GSM3780106 DNA was extracted using the Zymo Research ZR DNA-Card Extraction Kit following manufacturer protocol
## GSM3780107 DNA was extracted using the Zymo Research ZR DNA-Card Extraction Kit following manufacturer protocol
## GSM3780108 DNA was extracted using the Zymo Research ZR DNA-Card Extraction Kit following manufacturer protocol
## GSM3780109 DNA was extracted using the Zymo Research ZR DNA-Card Extraction Kit following manufacturer protocol
## GSM3780110 DNA was extracted using the Zymo Research ZR DNA-Card Extraction Kit following manufacturer protocol
## GSM3780111 DNA was extracted using the Zymo Research ZR DNA-Card Extraction Kit following manufacturer protocol
## label_ch1 label_protocol_ch1 taxid_ch1
## GSM3780106 Cy5 and Cy3 Standard Illumina Protocol 9606
## GSM3780107 Cy5 and Cy3 Standard Illumina Protocol 9606
## GSM3780108 Cy5 and Cy3 Standard Illumina Protocol 9606
## GSM3780109 Cy5 and Cy3 Standard Illumina Protocol 9606
## GSM3780110 Cy5 and Cy3 Standard Illumina Protocol 9606
## GSM3780111 Cy5 and Cy3 Standard Illumina Protocol 9606
## hyb_protocol
## GSM3780106 bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium MethylationEPIC Beadchip using standard Illumina protocol
## GSM3780107 bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium MethylationEPIC Beadchip using standard Illumina protocol
## GSM3780108 bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium MethylationEPIC Beadchip using standard Illumina protocol
## GSM3780109 bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium MethylationEPIC Beadchip using standard Illumina protocol
## GSM3780110 bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium MethylationEPIC Beadchip using standard Illumina protocol
## GSM3780111 bisulphite converted DNA was amplified, fragmented and hybridised to Illumina Infinium MethylationEPIC Beadchip using standard Illumina protocol
## scan_protocol
## GSM3780106 Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting
## GSM3780107 Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting
## GSM3780108 Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting
## GSM3780109 Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting
## GSM3780110 Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting
## GSM3780111 Arrays were imaged using BeadArray Reader using standard recommended Illumina scanner setting
## description
## GSM3780106 guthrie blood spot from ART conceived individual
## GSM3780107 guthrie blood spot from ART conceived individual
## GSM3780108 guthrie blood spot from ART conceived individual
## GSM3780109 guthrie blood spot from ART conceived individual
## GSM3780110 guthrie blood spot from ART conceived individual
## GSM3780111 guthrie blood spot from ART conceived individual
## data_processing
## GSM3780106 BeadStudio software v3.2
## GSM3780107 BeadStudio software v3.2
## GSM3780108 BeadStudio software v3.2
## GSM3780109 BeadStudio software v3.2
## GSM3780110 BeadStudio software v3.2
## GSM3780111 BeadStudio software v3.2
## data_processing.1
## GSM3780106 matrix processed includes: Normalized (SWAN) Average Beta and detP
## GSM3780107 matrix processed includes: Normalized (SWAN) Average Beta and detP
## GSM3780108 matrix processed includes: Normalized (SWAN) Average Beta and detP
## GSM3780109 matrix processed includes: Normalized (SWAN) Average Beta and detP
## GSM3780110 matrix processed includes: Normalized (SWAN) Average Beta and detP
## GSM3780111 matrix processed includes: Normalized (SWAN) Average Beta and detP
## data_processing.2
## GSM3780106 matrix signal intensities includes: The Methylated and unmethylated signal intensities and detP
## GSM3780107 matrix signal intensities includes: The Methylated and unmethylated signal intensities and detP
## GSM3780108 matrix signal intensities includes: The Methylated and unmethylated signal intensities and detP
## GSM3780109 matrix signal intensities includes: The Methylated and unmethylated signal intensities and detP
## GSM3780110 matrix signal intensities includes: The Methylated and unmethylated signal intensities and detP
## GSM3780111 matrix signal intensities includes: The Methylated and unmethylated signal intensities and detP
## platform_id contact_name contact_email
## GSM3780106 GPL21145 Boris,,Novakovic boris.novakovic@mcri.edu.au
## GSM3780107 GPL21145 Boris,,Novakovic boris.novakovic@mcri.edu.au
## GSM3780108 GPL21145 Boris,,Novakovic boris.novakovic@mcri.edu.au
## GSM3780109 GPL21145 Boris,,Novakovic boris.novakovic@mcri.edu.au
## GSM3780110 GPL21145 Boris,,Novakovic boris.novakovic@mcri.edu.au
## GSM3780111 GPL21145 Boris,,Novakovic boris.novakovic@mcri.edu.au
## contact_phone contact_laboratory contact_department
## GSM3780106 +610434980073 Epigenetics Cell Biology
## GSM3780107 +610434980073 Epigenetics Cell Biology
## GSM3780108 +610434980073 Epigenetics Cell Biology
## GSM3780109 +610434980073 Epigenetics Cell Biology
## GSM3780110 +610434980073 Epigenetics Cell Biology
## GSM3780111 +610434980073 Epigenetics Cell Biology
## contact_institute contact_address contact_city contact_state
## GSM3780106 MCRI RCH, Flemington Road Parkville Victoria
## GSM3780107 MCRI RCH, Flemington Road Parkville Victoria
## GSM3780108 MCRI RCH, Flemington Road Parkville Victoria
## GSM3780109 MCRI RCH, Flemington Road Parkville Victoria
## GSM3780110 MCRI RCH, Flemington Road Parkville Victoria
## GSM3780111 MCRI RCH, Flemington Road Parkville Victoria
## contact_zip/postal_code contact_country
## GSM3780106 3052 Australia
## GSM3780107 3052 Australia
## GSM3780108 3052 Australia
## GSM3780109 3052 Australia
## GSM3780110 3052 Australia
## GSM3780111 3052 Australia
## supplementary_file
## GSM3780106 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780106/suppl/GSM3780106_202818860092_R07C01_Grn.idat.gz
## GSM3780107 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780107/suppl/GSM3780107_202822930094_R07C01_Grn.idat.gz
## GSM3780108 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780108/suppl/GSM3780108_202818860162_R01C01_Grn.idat.gz
## GSM3780109 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780109/suppl/GSM3780109_202818860162_R03C01_Grn.idat.gz
## GSM3780110 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780110/suppl/GSM3780110_202822930046_R01C01_Grn.idat.gz
## GSM3780111 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780111/suppl/GSM3780111_202818860100_R01C01_Grn.idat.gz
## supplementary_file.1
## GSM3780106 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780106/suppl/GSM3780106_202818860092_R07C01_Red.idat.gz
## GSM3780107 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780107/suppl/GSM3780107_202822930094_R07C01_Red.idat.gz
## GSM3780108 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780108/suppl/GSM3780108_202818860162_R01C01_Red.idat.gz
## GSM3780109 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780109/suppl/GSM3780109_202818860162_R03C01_Red.idat.gz
## GSM3780110 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780110/suppl/GSM3780110_202822930046_R01C01_Red.idat.gz
## GSM3780111 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3780nnn/GSM3780111/suppl/GSM3780111_202818860100_R01C01_Red.idat.gz
## data_row_count art status:ch1 art subtype:ch1 gender:ch1
## GSM3780106 0 ART Frozen embryo F
## GSM3780107 0 ART NA M
## GSM3780108 0 ART Fresh embryo F
## GSM3780109 0 ART Frozen embryo M
## GSM3780110 0 ART Fresh embryo F
## GSM3780111 0 ART Fresh embryo F
## sample type:ch1 time of collection:ch1
## GSM3780106 Guthrie blood spot birth
## GSM3780107 Guthrie blood spot birth
## GSM3780108 Guthrie blood spot birth
## GSM3780109 Guthrie blood spot birth
## GSM3780110 Guthrie blood spot birth
## GSM3780111 Guthrie blood spot birth
## Basename
## GSM3780106 GSE131433/GSM3780106_202818860092_R07C01
## GSM3780107 GSE131433/GSM3780107_202822930094_R07C01
## GSM3780108 GSE131433/GSM3780108_202818860162_R01C01
## GSM3780109 GSE131433/GSM3780109_202818860162_R03C01
## GSM3780110 GSE131433/GSM3780110_202822930046_R01C01
## GSM3780111 GSE131433/GSM3780111_202818860100_R01C01# filter time of birth as were not interested in follow up samples
targets <- subset(targets,`time of collection:ch1`=="birth")
rgSet <- read.metharray.exp(targets = targets)## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
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## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nuls
## Warning in readChar(con, nchars = n): truncating string with embedded nulsrgSet## class: RGChannelSet
## dim: 1051815 207
## metadata(0):
## assays(2): Green Red
## rownames(1051815): 1600101 1600111 ... 99810990 99810992
## rowData names(0):
## colnames(207): GSM3780106_202818860092_R07C01
## GSM3780107_202822930094_R07C01 ... GSM3780311_202822930170_R07C01
## GSM3780312_202822930058_R05C01
## colData names(47): title geo_accession ... Basename filenames
## Annotation
## array: IlluminaHumanMethylationEPIC
## annotation: ilm10b4.hg19mSet <- preprocessRaw(rgSet)## Loading required package: IlluminaHumanMethylationEPICmanifestmSetSw <- SWAN(mSet,verbose=FALSE)## [SWAN] Preparing normalization subset
## EPIC
## [SWAN] Normalizing methylated channel
## [SWAN] Normalizing unmethylated channelpar(mfrow=c(1,2), cex=0.8)
densityByProbeType(mSet[,1], main = "Raw")
densityByProbeType(mSetSw[,1], main = "SWAN")
Figure 1. Normalisation of bead-array data with SWAN.
# include sex chromosomes
detP <- detectionP(rgSet)
keep <- rowSums(detP < 0.01) == ncol(rgSet)
mSetSw <- mSetSw[keep,]
# exclude SNP probes
mSetSw <- mapToGenome(mSetSw)
mSetSw_nosnp <- dropLociWithSnps(mSetSw)
# exclude sex chromosomes
xyprobes <- anno$Name[anno$chr %in% c("chrX","chrY")]
mSetFlt <- mSetSw[which(!rownames(mSetSw) %in% xyprobes),]# include sex chromosomes
meth <- getMeth(mSetSw)
unmeth <- getUnmeth(mSetSw)
Mval <- log2((meth + 100)/(unmeth + 100))
beta <- getBeta(mSetSw)
dim(Mval)## [1] 810518 207# exclude sex chromosomes
meth <- getMeth(mSetFlt)
unmeth <- getUnmeth(mSetFlt)
Mval_flt <- log2((meth + 100)/(unmeth + 100))
beta_flt <- getBeta(mSetFlt)
dim(Mval_flt)## [1] 793844 207MDS analysis
par(mfrow=c(2,1))
myscree(Mval,main="incl sex chr")
myscree(Mval_flt,main="excl sex chr")
Figure 2. Screeplot shows contribution of top principal components to the overall variation in the experiment.
#MDS plot 1
colnames(targets) <- gsub(" ","_",colnames(targets))
colnames(targets) <- gsub(":","_",colnames(targets))
targets$sex<-factor(targets$`gender_ch1`)
targets$art<-factor(targets$`art_subtype`)
sample_groups <- factor(targets$art)
colour_palette=brewer.pal(n = length(levels(sample_groups)), name = "Paired")
colours <- colour_palette[as.integer(factor(targets$art))]
plot(1,axes = FALSE,xlab="",ylab="",main="MDS by ART type")
legend("center",legend=levels(sample_groups),pch=16,cex=1.2,col=colour_palette)
Figure 3. MDS plot coloured by ART classification.
mydist <- plotMDS(Mval, labels=targets$Sample_Name,col=colours,main="sex chromosomes included")
Figure 3. MDS plot coloured by ART classification.
mydist_flt <- plotMDS(Mval_flt, labels=targets$Sample_Name,col=colours,main="sex chromosomes excluded")
Figure 3. MDS plot coloured by ART classification.
colnames(mydist_flt@.Data[[5]]) <- sapply(strsplit(colnames(mydist_flt@.Data[[5]]),"_"),"[[",1)
plotMDS(mydist_flt, labels=targets$Sample_Name,col=colours,main="sex chromosomes excluded")
Figure 3. MDS plot coloured by ART classification.
#save data object
save.image("Novakovic.Rdata")#Differential analysis
non-ART Vs Fresh embryo
non-ART Vs Frozen embryo
non-ART Vs GIFT
non-ART Vs FX
GIFT Vs FX
non-ART Vs ART
Fresh Vs Frozen
non-ART Vs Fresh embryo
# sex chromosomes excluded
beta <- beta_flt
Mval <- Mval_flt
colnames(beta) <- sapply(strsplit(colnames(beta),"_"),"[[",1)
colnames(Mval) <- sapply(strsplit(colnames(Mval),"_"),"[[",1)
#targets$Basename <- gsub("IDAT/","",targets$Basename)
birth<-targets[which(targets$`time_of_collection`=="birth"),]
birth$Basename <- sapply(strsplit(birth$Basename,"/"),"[[",2)
birth$Basename <- sapply(strsplit(birth$Basename,"_"),"[[",1)
samplesheet<-subset(birth,art_subtype_ch1=="non-ART"|art_subtype_ch1=="Fresh embryo")
groups <- factor(samplesheet$art_subtype,levels=c("non-ART","Fresh embryo"))
sex <- factor(samplesheet$sex,levels=c("M","F"))
Mvals<-Mval[,colnames(Mval)%in% samplesheet$Basename]
betas<-beta[,colnames(beta)%in% samplesheet$Basename]
dim(Mvals)## [1] 793844 133dim(betas)## [1] 793844 133top_nat_vs_fh <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mvals,name="top_nat_vs_fh",
myann=myann ,beta= betas) ## Your contrast returned 6292 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.
head(top_nat_vs_fh$dma,10)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 743573 cg25867694 SELM Promoter_Associated
## 510348 cg16794867 AADACL2;MIR548H2
## 11452 cg00352360 RPL23;SNORA21
## 564284 cg18789663 PLD5
## 213076 cg06826756 NUP153;NUP153;NUP153
## 533323 cg17602885 CLSTN1;CLSTN1
## 656385 cg22481606 SUPT4H1 Promoter_Associated
## 457675 cg14985987 TAF1A;TAF1A Promoter_Associated
## 666703 cg22883995 CXXC5
## 200896 cg06438056 AK2;AK2;AK2;AK2 Promoter_Associated
## Islands_Name Relation_to_Island logFC AveExpr
## 743573 chr22:31503236-31503871 Island -0.3170932 -3.1637864
## 510348 OpenSea -0.3194548 -0.8937958
## 11452 chr17:37009471-37010118 N_Shore -0.3840541 -2.6531641
## 564284 chr1:242687922-242688682 Island -0.3323787 -2.7624449
## 213076 chr6:17706292-17707339 S_Shore -0.2449454 -2.0577696
## 533323 OpenSea 0.3437550 5.0682947
## 656385 chr17:56429455-56429906 Island -0.3552362 -2.7157295
## 457675 chr1:222763021-222763270 S_Shore -0.2840357 -3.0323278
## 666703 chr5:139060555-139060934 N_Shore 0.5241212 3.9920277
## 200896 chr1:33502201-33502719 Island -0.5592441 -1.8461297
## t P.Value adj.P.Val B
## 743573 -6.740188 4.001417e-10 0.0002526207 12.260124
## 510348 -6.650166 6.364493e-10 0.0002526207 11.843891
## 11452 -6.328140 3.265725e-09 0.0006264789 10.377097
## 564284 -6.326298 3.296038e-09 0.0006264789 10.368810
## 213076 -6.248221 4.869770e-09 0.0006264789 10.018736
## 533323 6.240780 5.053675e-09 0.0006264789 9.985491
## 656385 -6.217291 5.680221e-09 0.0006264789 9.880674
## 457675 -6.156272 7.686986e-09 0.0006264789 9.609362
## 666703 6.112320 9.549404e-09 0.0006264789 9.414819
## 200896 -6.108507 9.730500e-09 0.0006264789 9.397973head(top_nat_vs_fh$dmr,10)## GRanges object with 10 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr17 4803684-4805392 * | 6 2.04703e-11
## [2] chr19 20348926-20349303 * | 5 9.93835e-13
## [3] chr13 27295928-27296010 * | 3 1.73882e-09
## [4] chr17 19265610-19266474 * | 12 1.42466e-13
## [5] chr21 40759534-40759694 * | 5 2.76602e-10
## [6] chr19 21949968-21950478 * | 8 2.14391e-11
## [7] chr15 94218934-94218949 * | 2 1.74203e-07
## [8] chr1 247453113-247453569 * | 2 1.29806e-06
## [9] chr4 69215305-69215675 * | 3 6.15497e-13
## [10] chr11 130298267-130298435 * | 2 3.83144e-09
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.70522e-08 0.00749017 3.12842e-07 -0.1121621 -0.06749122
## [2] 2.84043e-05 0.00604191 4.34660e-05 -0.0444664 -0.02807448
## [3] 2.18305e-05 0.00893351 8.67309e-05 0.1002679 0.09675393
## [4] 3.73948e-04 0.00665419 1.67791e-04 -0.0195797 -0.00910014
## [5] 3.11609e-05 0.02876306 2.30971e-04 -0.0681944 -0.04943169
## [6] 2.66012e-03 0.01446882 2.88773e-04 -0.0247105 -0.01386096
## [7] 1.86089e-04 0.00530867 3.86888e-04 0.0316250 0.02886711
## [8] 2.72796e-04 0.00468855 5.19405e-04 0.0276802 0.02063989
## [9] 1.45125e-03 0.00270115 5.28181e-04 -0.0191426 -0.00900630
## [10] 3.44430e-04 0.00257109 5.28491e-04 -0.0390076 -0.02393158
## overlapping.genes
## <character>
## [1] C17orf107, CHRNE
## [2] CTC-260E6.6
## [3] <NA>
## [4] B9D1
## [5] WRB
## [6] ZNF100
## [7] RP11-739G5.1
## [8] <NA>
## [9] YTHDC1
## [10] ADAMTS8
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengths# Allele
top_nat_vs_fh$dma$unmeth <- "T"
top_nat_vs_fh$dma$meth <- "C"
top_nat_vs_fh$fit$SE <- sqrt(top_nat_vs_fh$fit$s2.post) * top_nat_vs_fh$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_fh_metal <-top_nat_vs_fh$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_fh_metal)## Row.names meth unmeth AveExpr P.Value
## 743573 cg25867694 C T -3.1637864 4.001417e-10
## 510348 cg16794867 C T -0.8937958 6.364493e-10
## 11452 cg00352360 C T -2.6531641 3.265725e-09
## 564284 cg18789663 C T -2.7624449 3.296038e-09
## 213076 cg06826756 C T -2.0577696 4.869770e-09
## 533323 cg17602885 C T 5.0682947 5.053675e-09# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_fh$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c(4,3)]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_fh$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c(4,3)]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_fh_metal <- merge(top_nat_vs_fh_metal, fitCE)
top_nat_vs_fh_metal <- merge(top_nat_vs_fh_metal, fitSE)
# Number of effective participants
Neff = 4/sum(1/table(groups))
top_nat_vs_fh_metal$N <- Neff
# Output for Meta-analysis
write.table(top_nat_vs_fh_metal, file="novakovic_top_nat_vs_fh_metal.tsv",
sep="\t",quote=FALSE, row.names = FALSE)
head(top_nat_vs_fh$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 743573 cg25867694 SELM Promoter_Associated
## 510348 cg16794867 AADACL2;MIR548H2
## 11452 cg00352360 RPL23;SNORA21
## 564284 cg18789663 PLD5
## 213076 cg06826756 NUP153;NUP153;NUP153
## 533323 cg17602885 CLSTN1;CLSTN1
## Islands_Name Relation_to_Island logFC AveExpr
## 743573 chr22:31503236-31503871 Island -0.3170932 -3.1637864
## 510348 OpenSea -0.3194548 -0.8937958
## 11452 chr17:37009471-37010118 N_Shore -0.3840541 -2.6531641
## 564284 chr1:242687922-242688682 Island -0.3323787 -2.7624449
## 213076 chr6:17706292-17707339 S_Shore -0.2449454 -2.0577696
## 533323 OpenSea 0.3437550 5.0682947
## t P.Value adj.P.Val B unmeth meth
## 743573 -6.740188 4.001417e-10 0.0002526207 12.260124 T C
## 510348 -6.650166 6.364493e-10 0.0002526207 11.843891 T C
## 11452 -6.328140 3.265725e-09 0.0006264789 10.377097 T C
## 564284 -6.326298 3.296038e-09 0.0006264789 10.368810 T C
## 213076 -6.248221 4.869770e-09 0.0006264789 10.018736 T C
## 533323 6.240780 5.053675e-09 0.0006264789 9.985491 T Chead(top_nat_vs_fh$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr17 4803684-4805392 * | 6 2.04703e-11
## [2] chr19 20348926-20349303 * | 5 9.93835e-13
## [3] chr13 27295928-27296010 * | 3 1.73882e-09
## [4] chr17 19265610-19266474 * | 12 1.42466e-13
## [5] chr21 40759534-40759694 * | 5 2.76602e-10
## [6] chr19 21949968-21950478 * | 8 2.14391e-11
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.70522e-08 0.00749017 3.12842e-07 -0.1121621 -0.06749122
## [2] 2.84043e-05 0.00604191 4.34660e-05 -0.0444664 -0.02807448
## [3] 2.18305e-05 0.00893351 8.67309e-05 0.1002679 0.09675393
## [4] 3.73948e-04 0.00665419 1.67791e-04 -0.0195797 -0.00910014
## [5] 3.11609e-05 0.02876306 2.30971e-04 -0.0681944 -0.04943169
## [6] 2.66012e-03 0.01446882 2.88773e-04 -0.0247105 -0.01386096
## overlapping.genes
## <character>
## [1] C17orf107, CHRNE
## [2] CTC-260E6.6
## [3] <NA>
## [4] B9D1
## [5] WRB
## [6] ZNF100
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthshead(top_nat_vs_fh_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000103 C T 2.7941256 0.01707402 -0.21317088 0.08828895 130.8271
## 2 cg00000109 C T 1.7999550 0.48096235 -0.06095312 0.08625292 130.8271
## 3 cg00000155 C T 3.1170105 0.30944103 -0.07000145 0.06861755 130.8271
## 4 cg00000158 C T 3.1718838 0.51086363 0.05333879 0.08091315 130.8271
## 5 cg00000165 C T -2.1091998 0.50776928 0.04413382 0.06646136 130.8271
## 6 cg00000221 C T 0.9858254 0.86693019 -0.01957153 0.11658556 130.8271saveRDS(top_nat_vs_fh,file="novakovic_nat_vs_fh.rds")non-ART Vs Frozen embryo
samplesheet<-subset(birth,art_subtype_ch1=="non-ART"|art_subtype_ch1=="Frozen embryo")
groups <- factor(samplesheet$art_subtype,levels=c("non-ART","Frozen embryo"))
sex <- factor(samplesheet$sex,levels=c("M","F"))
Mvals<-Mval[,colnames(Mval)%in% samplesheet$Basename]
betas<-beta[,colnames(beta)%in% samplesheet$Basename]
top_nat_vs_fz <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mvals,name="top_nat_vs_fz",
myann=myann ,beta= betas)
head(top_nat_vs_fz$dma,10)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 256518 cg08242354
## 752334 cg26210521 SLC2A9;SLC2A9
## 338484 cg10992198 WDR62;WDR62 Gene_Associated
## 462440 cg15147113 SERPINE2;SERPINE2;SERPINE2;SERPINE2 Unclassified
## 510348 cg16794867 AADACL2;MIR548H2
## 216261 cg06924355 PAQR3 Promoter_Associated
## 682894 cg23516310
## 588078 cg19738653 EGR3;EGR3;EGR3
## 675978 cg23244910
## 745548 cg25950293 ADGRE5;ADGRE5;ADGRE5
## Islands_Name Relation_to_Island logFC AveExpr
## 256518 OpenSea -0.3138346 0.8773421
## 752334 OpenSea -0.3017469 1.7053825
## 338484 OpenSea -0.2225205 1.2244064
## 462440 OpenSea -1.7977189 -2.7771016
## 510348 OpenSea -0.2954307 -0.8113974
## 216261 chr4:79860213-79860745 S_Shore -0.4309532 -3.4925078
## 682894 OpenSea -0.3836119 -3.3768613
## 588078 chr8:22547486-22553427 Island -0.4294140 -3.8348854
## 675978 chr6:106433984-106434459 Island -0.5353793 -4.0623968
## 745548 OpenSea -0.3629615 -1.1301961
## t P.Value adj.P.Val B
## 256518 -6.452959 4.986651e-09 0.003958623 9.175902
## 752334 -5.883411 6.378324e-08 0.025316969 7.084233
## 338484 -5.753709 1.125016e-07 0.028051800 6.617274
## 462440 -5.701196 1.413467e-07 0.028051800 6.429353
## 510348 -5.558471 2.616479e-07 0.041541529 5.922101
## 216261 -5.489762 3.510678e-07 0.046448840 5.679816
## 682894 -5.422309 4.677691e-07 0.053047961 5.443222
## 588078 -5.219237 1.098778e-06 0.090784146 4.738920
## 675978 -5.139775 1.528205e-06 0.090784146 4.466763
## 745548 -5.100644 1.796128e-06 0.090784146 4.333486head(top_nat_vs_fz$dmr,10)## NULL# Allele
top_nat_vs_fz$dma$unmeth <- "T"
top_nat_vs_fz$dma$meth <- "C"
top_nat_vs_fz$fit$SE <- sqrt(top_nat_vs_fz$fit$s2.post) * top_nat_vs_fz$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_fz_metal <-top_nat_vs_fz$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value
## 256518 cg08242354 C T 0.8773421 4.986651e-09
## 752334 cg26210521 C T 1.7053825 6.378324e-08
## 338484 cg10992198 C T 1.2244064 1.125016e-07
## 462440 cg15147113 C T -2.7771016 1.413467e-07
## 510348 cg16794867 C T -0.8113974 2.616479e-07
## 216261 cg06924355 C T -3.4925078 3.510678e-07# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_fz$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c(4,3)]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_fz$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c(4,3)]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_fz_metal <- merge(top_nat_vs_fz_metal, fitCE)
top_nat_vs_fz_metal <- merge(top_nat_vs_fz_metal, fitSE)
# Number of effective participants
Neff = 4/sum(1/table(groups))
top_nat_vs_fz_metal$N <- Neff
# Output for Meta-analysis
write.table(top_nat_vs_fz_metal, file="novakovic_top_nat_vs_fz_metal.tsv",
sep="\t",quote=FALSE, row.names = FALSE)
head(top_nat_vs_fz$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 256518 cg08242354
## 752334 cg26210521 SLC2A9;SLC2A9
## 338484 cg10992198 WDR62;WDR62 Gene_Associated
## 462440 cg15147113 SERPINE2;SERPINE2;SERPINE2;SERPINE2 Unclassified
## 510348 cg16794867 AADACL2;MIR548H2
## 216261 cg06924355 PAQR3 Promoter_Associated
## Islands_Name Relation_to_Island logFC AveExpr
## 256518 OpenSea -0.3138346 0.8773421
## 752334 OpenSea -0.3017469 1.7053825
## 338484 OpenSea -0.2225205 1.2244064
## 462440 OpenSea -1.7977189 -2.7771016
## 510348 OpenSea -0.2954307 -0.8113974
## 216261 chr4:79860213-79860745 S_Shore -0.4309532 -3.4925078
## t P.Value adj.P.Val B unmeth meth
## 256518 -6.452959 4.986651e-09 0.003958623 9.175902 T C
## 752334 -5.883411 6.378324e-08 0.025316969 7.084233 T C
## 338484 -5.753709 1.125016e-07 0.028051800 6.617274 T C
## 462440 -5.701196 1.413467e-07 0.028051800 6.429353 T C
## 510348 -5.558471 2.616479e-07 0.041541529 5.922101 T C
## 216261 -5.489762 3.510678e-07 0.046448840 5.679816 T Chead(top_nat_vs_fz$dmr)## NULLhead(top_nat_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000103 C T 2.864475 0.17322851 -0.14651932 0.10675318 79.09091
## 2 cg00000109 C T 1.853818 0.66357334 0.04976370 0.11403549 79.09091
## 3 cg00000155 C T 3.156122 0.84994416 -0.01460600 0.07698685 79.09091
## 4 cg00000158 C T 3.208639 0.02739279 0.18323593 0.08174972 79.09091
## 5 cg00000165 C T -2.113859 0.35658757 0.07840474 0.08462248 79.09091
## 6 cg00000221 C T 1.069706 0.13036495 0.21043962 0.13788074 79.09091saveRDS(top_nat_vs_fz,file="novakovic_nat_vs_fz.rds")non-ART Vs GIFT
samplesheet<-subset(birth,art_subtype_ch1=="non-ART"|art_subtype_ch1=="GIFT")
groups <- factor(samplesheet$art_subtype,levels=c("non-ART","GIFT"))
sex <- factor(samplesheet$sex,levels=c("M","F"))
Mvals<-Mval[,colnames(Mval)%in% samplesheet$Basename]
betas<-beta[,colnames(beta)%in% samplesheet$Basename]
top_nat_vs_GIFT <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mvals,name="top_nat_vs_GIFT",
myann=myann ,beta= betas)
head(top_nat_vs_GIFT$dma,10)## Row.names UCSC_RefGene_Name
## 733585 cg25472804
## 272999 cg08792630 FOXO3;FOXO3
## 534372 cg17643276 LRRC8A;LRRC8A;LRRC8A;CCBL1;CCBL1;CCBL1;CCBL1;CCBL1
## 633406 cg21538355
## 24396 cg00744351 CHRNE;C17orf107
## 398954 cg13101990
## 452798 cg14817867 PRPSAP2
## 286154 cg09230221 SIK3
## 415481 cg13644640 NSMCE2
## 421788 cg13844500
## Regulatory_Feature_Group Islands_Name
## 733585 Unclassified
## 272999 Unclassified_Cell_type_specific chr6:108878830-108883404
## 534372 Promoter_Associated chr9:131643911-131644749
## 633406 chr12:38557328-38557693
## 24396 Promoter_Associated_Cell_type_specific chr17:4802265-4805402
## 398954
## 452798
## 286154
## 415481
## 421788
## Relation_to_Island logFC AveExpr t P.Value
## 733585 OpenSea 0.4356252 1.7479511 5.301601 7.164697e-07
## 272999 S_Shore 0.4881967 -0.2386603 5.194972 1.124579e-06
## 534372 Island -0.3645621 -2.5177243 -5.088019 1.759364e-06
## 633406 N_Shore 0.5043693 1.2883817 5.087971 1.759713e-06
## 24396 Island -1.0546937 -2.5461840 -5.030487 2.233807e-06
## 398954 OpenSea 0.3606594 2.2927293 5.022182 2.311865e-06
## 452798 OpenSea 0.3825603 1.3429254 5.014475 2.386682e-06
## 286154 OpenSea 0.3291795 3.1207657 5.003397 2.498367e-06
## 415481 OpenSea 0.3655282 2.5811810 4.994330 2.593555e-06
## 421788 OpenSea 0.5308018 1.7474254 4.963437 2.945164e-06
## adj.P.Val B
## 733585 0.1866203 5.134791
## 272999 0.1866203 4.760186
## 534372 0.1866203 4.388291
## 633406 0.1866203 4.388126
## 24396 0.1866203 4.189897
## 398954 0.1866203 4.161357
## 452798 0.1866203 4.134893
## 286154 0.1866203 4.096893
## 415481 0.1866203 4.065823
## 421788 0.1866203 3.960190head(top_nat_vs_GIFT$dmr,10)## NULL# Allele
top_nat_vs_GIFT$dma$unmeth <- "T"
top_nat_vs_GIFT$dma$meth <- "C"
top_nat_vs_GIFT$fit$SE <- sqrt(top_nat_vs_GIFT$fit$s2.post) * top_nat_vs_GIFT$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_GIFT_metal <-top_nat_vs_GIFT$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_GIFT_metal)## Row.names meth unmeth AveExpr P.Value
## 733585 cg25472804 C T 1.7479511 7.164697e-07
## 272999 cg08792630 C T -0.2386603 1.124579e-06
## 534372 cg17643276 C T -2.5177243 1.759364e-06
## 633406 cg21538355 C T 1.2883817 1.759713e-06
## 24396 cg00744351 C T -2.5461840 2.233807e-06
## 398954 cg13101990 C T 2.2927293 2.311865e-06# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_GIFT$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c(4,3)]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_GIFT$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c(4,3)]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_GIFT_metal <- merge(top_nat_vs_GIFT_metal, fitCE)
top_nat_vs_GIFT_metal <- merge(top_nat_vs_GIFT_metal, fitSE)
# Number of effective participants
Neff = 4/sum(1/table(groups))
top_nat_vs_GIFT_metal$N <- Neff
# Output for Meta-analysis
write.table(top_nat_vs_GIFT_metal, file="novakovic_top_nat_vs_GIFT_metal.tsv",
sep="\t",quote=FALSE, row.names = FALSE)
head(top_nat_vs_GIFT$dma)## Row.names UCSC_RefGene_Name
## 733585 cg25472804
## 272999 cg08792630 FOXO3;FOXO3
## 534372 cg17643276 LRRC8A;LRRC8A;LRRC8A;CCBL1;CCBL1;CCBL1;CCBL1;CCBL1
## 633406 cg21538355
## 24396 cg00744351 CHRNE;C17orf107
## 398954 cg13101990
## Regulatory_Feature_Group Islands_Name
## 733585 Unclassified
## 272999 Unclassified_Cell_type_specific chr6:108878830-108883404
## 534372 Promoter_Associated chr9:131643911-131644749
## 633406 chr12:38557328-38557693
## 24396 Promoter_Associated_Cell_type_specific chr17:4802265-4805402
## 398954
## Relation_to_Island logFC AveExpr t P.Value
## 733585 OpenSea 0.4356252 1.7479511 5.301601 7.164697e-07
## 272999 S_Shore 0.4881967 -0.2386603 5.194972 1.124579e-06
## 534372 Island -0.3645621 -2.5177243 -5.088019 1.759364e-06
## 633406 N_Shore 0.5043693 1.2883817 5.087971 1.759713e-06
## 24396 Island -1.0546937 -2.5461840 -5.030487 2.233807e-06
## 398954 OpenSea 0.3606594 2.2927293 5.022182 2.311865e-06
## adj.P.Val B unmeth meth
## 733585 0.1866203 5.134791 T C
## 272999 0.1866203 4.760186 T C
## 534372 0.1866203 4.388291 T C
## 633406 0.1866203 4.388126 T C
## 24396 0.1866203 4.189897 T C
## 398954 0.1866203 4.161357 T Chead(top_nat_vs_GIFT$dmr)## NULLhead(top_nat_vs_GIFT_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000103 C T 2.928434 0.54594002 0.05933121 0.09791007 87.31183
## 2 cg00000109 C T 1.917816 0.02799813 0.22695322 0.10174236 87.31183
## 3 cg00000155 C T 3.196455 0.21254799 0.09970849 0.07946058 87.31183
## 4 cg00000158 C T 3.206763 0.01713958 0.18061338 0.07447168 87.31183
## 5 cg00000165 C T -2.145394 0.71985416 -0.03213346 0.08933536 87.31183
## 6 cg00000221 C T 1.119977 0.00673560 0.34399602 0.12423815 87.31183saveRDS(top_nat_vs_GIFT,file="novakovic_nat_vs_GIFT.rds")non-ART Vs FX
samplesheet <- subset(birth,art_subtype_ch1=="non-ART"|art_subtype_ch1=="Frozen embryo" | art_subtype_ch1=="Fresh embryo")
samplesheet$art_subtype <- gsub("Fresh embryo","FX",samplesheet$art_subtype)
samplesheet$art_subtype <- gsub("Frozen embryo","FX",samplesheet$art_subtype)
groups <- factor(samplesheet$art_subtype,levels=c("non-ART","FX"))
sex <- factor(samplesheet$sex,levels=c("M","F"))
Mvals<-Mval[,colnames(Mval)%in% samplesheet$Basename]
betas<-beta[,colnames(beta)%in% samplesheet$Basename]
top_nat_vs_FX <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mvals,name="top_nat_vs_FX",
myann=myann ,beta= betas)## Your contrast returned 10345 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.
head(top_nat_vs_FX$dma,10)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 510348 cg16794867 AADACL2;MIR548H2
## 656385 cg22481606 SUPT4H1 Promoter_Associated
## 564284 cg18789663 PLD5
## 12931 cg00397324 MX1;MX1 Promoter_Associated_Cell_type_specific
## 256518 cg08242354
## 11452 cg00352360 RPL23;SNORA21
## 529461 cg17465697 TRIM11 Promoter_Associated
## 363209 cg11847929 TXNDC15 Promoter_Associated
## 774705 cg27039610
## 200896 cg06438056 AK2;AK2;AK2;AK2 Promoter_Associated
## Islands_Name Relation_to_Island logFC AveExpr
## 510348 OpenSea -0.3125414 -0.9119653
## 656385 chr17:56429455-56429906 Island -0.3530952 -2.7371318
## 564284 chr1:242687922-242688682 Island -0.3227375 -2.7791271
## 12931 chr21:42798146-42798884 Island -0.5905909 -4.3832792
## 256518 OpenSea -0.2737494 0.8079667
## 11452 chr17:37009471-37010118 N_Shore -0.3676741 -2.6696850
## 529461 chr1:228593811-228594713 Island -0.4843081 -2.3824655
## 363209 chr5:134209887-134210504 Island -0.4979404 -4.0450930
## 774705 chr17:19015446-19015696 S_Shore -0.3706330 -3.3733946
## 200896 chr1:33502201-33502719 Island -0.5274924 -1.8619758
## t P.Value adj.P.Val B
## 510348 -7.127362 2.904946e-11 2.306074e-05 14.72537
## 656385 -6.832775 1.473793e-10 4.880089e-05 13.25324
## 564284 -6.791587 1.844225e-10 4.880089e-05 13.05000
## 12931 -6.647830 4.010728e-10 6.412515e-05 12.34583
## 256518 -6.617196 4.727368e-10 6.412515e-05 12.19683
## 11452 -6.612545 4.846681e-10 6.412515e-05 12.17424
## 529461 -6.385911 1.613488e-09 1.668646e-04 11.08445
## 363209 -6.351908 1.928643e-09 1.668646e-04 10.92281
## 774705 -6.333653 2.122030e-09 1.668646e-04 10.83625
## 200896 -6.319919 2.279976e-09 1.668646e-04 10.77122head(top_nat_vs_FX$dmr,10)## GRanges object with 10 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr17 4804104-4805392 * | 5 1.40964e-13
## [2] chr19 21949968-21950478 * | 8 3.19202e-14
## [3] chr13 27295928-27296010 * | 3 3.71637e-11
## [4] chr20 32255052-32256071 * | 4 2.18229e-10
## [5] chr1 92949337-92950836 * | 29 1.58506e-23
## [6] chr17 19265610-19266474 * | 12 8.66653e-15
## [7] chr1 3567986-3569046 * | 14 1.78043e-18
## [8] chr22 38141814-38142561 * | 10 3.09090e-15
## [9] chr4 330865-332198 * | 14 1.13558e-13
## [10] chr12 105724039-105724818 * | 8 2.84113e-14
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.37601e-09 0.00240195 2.08525e-08 -0.1074677 -0.07196229
## [2] 1.32966e-04 0.00513093 6.24658e-06 -0.0256360 -0.01386635
## [3] 1.36793e-06 0.00337543 6.35969e-06 0.1029182 0.10085782
## [4] 1.43599e-06 0.00547058 9.05215e-06 -0.1365313 -0.08319813
## [5] 1.06137e-03 0.00208177 1.06585e-05 -0.0273865 -0.00748298
## [6] 2.91341e-05 0.00250399 1.12376e-05 -0.0182193 -0.00871875
## [7] 4.35303e-05 0.01212177 3.21754e-05 -0.0368901 -0.01325273
## [8] 3.24082e-05 0.02255293 3.50961e-05 -0.0196740 -0.01237661
## [9] 6.26856e-04 0.02059544 5.00050e-05 -0.0125315 -0.00659573
## [10] 7.31081e-04 0.00666399 5.06946e-05 -0.0215764 -0.00877279
## overlapping.genes
## <character>
## [1] C17orf107, CHRNE
## [2] ZNF100
## [3] <NA>
## [4] ACTL10, NECAB3
## [5] GFI1
## [6] B9D1
## [7] WRAP73
## [8] NOL12, TRIOBP
## [9] ZNF141, RP11-478C6.2
## [10] C12orf75
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengths# Allele
top_nat_vs_FX$dma$unmeth <- "T"
top_nat_vs_FX$dma$meth <- "C"
top_nat_vs_FX$fit$SE <- sqrt(top_nat_vs_FX$fit$s2.post) * top_nat_vs_FX$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_FX_metal <-top_nat_vs_FX$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_FX_metal)## Row.names meth unmeth AveExpr P.Value
## 510348 cg16794867 C T -0.9119653 2.904946e-11
## 656385 cg22481606 C T -2.7371318 1.473793e-10
## 564284 cg18789663 C T -2.7791271 1.844225e-10
## 12931 cg00397324 C T -4.3832792 4.010728e-10
## 256518 cg08242354 C T 0.8079667 4.727368e-10
## 11452 cg00352360 C T -2.6696850 4.846681e-10# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_FX$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c(4,3)]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_FX$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c(4,3)]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_FX_metal <- merge(top_nat_vs_FX_metal, fitCE)
top_nat_vs_FX_metal <- merge(top_nat_vs_FX_metal, fitSE)
# Number of effective participants
Neff = 4/sum(1/table(groups))
top_nat_vs_FX_metal$N <- Neff
# Output for Meta-analysis
write.table(top_nat_vs_FX_metal, file="novakovic_top_nat_vs_FX_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)
head(top_nat_vs_FX$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 510348 cg16794867 AADACL2;MIR548H2
## 656385 cg22481606 SUPT4H1 Promoter_Associated
## 564284 cg18789663 PLD5
## 12931 cg00397324 MX1;MX1 Promoter_Associated_Cell_type_specific
## 256518 cg08242354
## 11452 cg00352360 RPL23;SNORA21
## Islands_Name Relation_to_Island logFC AveExpr
## 510348 OpenSea -0.3125414 -0.9119653
## 656385 chr17:56429455-56429906 Island -0.3530952 -2.7371318
## 564284 chr1:242687922-242688682 Island -0.3227375 -2.7791271
## 12931 chr21:42798146-42798884 Island -0.5905909 -4.3832792
## 256518 OpenSea -0.2737494 0.8079667
## 11452 chr17:37009471-37010118 N_Shore -0.3676741 -2.6696850
## t P.Value adj.P.Val B unmeth meth
## 510348 -7.127362 2.904946e-11 2.306074e-05 14.72537 T C
## 656385 -6.832775 1.473793e-10 4.880089e-05 13.25324 T C
## 564284 -6.791587 1.844225e-10 4.880089e-05 13.05000 T C
## 12931 -6.647830 4.010728e-10 6.412515e-05 12.34583 T C
## 256518 -6.617196 4.727368e-10 6.412515e-05 12.19683 T C
## 11452 -6.612545 4.846681e-10 6.412515e-05 12.17424 T Chead(top_nat_vs_FX$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr17 4804104-4805392 * | 5 1.40964e-13
## [2] chr19 21949968-21950478 * | 8 3.19202e-14
## [3] chr13 27295928-27296010 * | 3 3.71637e-11
## [4] chr20 32255052-32256071 * | 4 2.18229e-10
## [5] chr1 92949337-92950836 * | 29 1.58506e-23
## [6] chr17 19265610-19266474 * | 12 8.66653e-15
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 1.37601e-09 0.00240195 2.08525e-08 -0.1074677 -0.07196229
## [2] 1.32966e-04 0.00513093 6.24658e-06 -0.0256360 -0.01386635
## [3] 1.36793e-06 0.00337543 6.35969e-06 0.1029182 0.10085782
## [4] 1.43599e-06 0.00547058 9.05215e-06 -0.1365313 -0.08319813
## [5] 1.06137e-03 0.00208177 1.06585e-05 -0.0273865 -0.00748298
## [6] 2.91341e-05 0.00250399 1.12376e-05 -0.0182193 -0.00871875
## overlapping.genes
## <character>
## [1] C17orf107, CHRNE
## [2] ZNF100
## [3] <NA>
## [4] ACTL10, NECAB3
## [5] GFI1
## [6] B9D1
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthshead(top_nat_vs_FX_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000103 C T 2.788711 0.01457338 -0.20117921 0.08149987 149.4479
## 2 cg00000109 C T 1.816570 0.69070778 -0.03233653 0.08112867 149.4479
## 3 cg00000155 C T 3.123791 0.38430129 -0.05258101 0.06027962 149.4479
## 4 cg00000158 C T 3.202209 0.21636225 0.08988066 0.07242942 149.4479
## 5 cg00000165 C T -2.102856 0.42851169 0.05103917 0.06430800 149.4479
## 6 cg00000221 C T 1.025903 0.69346438 0.04175624 0.10575527 149.4479saveRDS(top_nat_vs_FX,file="novakovic_nat_vs_FX.rds")GIFT Vs FX
samplesheet <- subset(birth,art_subtype_ch1=="GIFT"|art_subtype_ch1=="Frozen embryo" | art_subtype_ch1=="Fresh embryo")
samplesheet$art_subtype <- gsub("Fresh embryo","FX",samplesheet$art_subtype)
samplesheet$art_subtype <- gsub("Frozen embryo","FX",samplesheet$art_subtype)
groups <- factor(samplesheet$art_subtype,levels=c("GIFT","FX"))
sex <- factor(samplesheet$sex,levels=c("M","F"))
Mvals<-Mval[,colnames(Mval)%in% samplesheet$Basename]
betas<-beta[,colnames(beta)%in% samplesheet$Basename]
top_GIFT_vs_FX <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mvals,name="top_GIFT_vs_FX",
myann=myann ,beta= betas)
head(top_GIFT_vs_FX$dma,10)## Row.names UCSC_RefGene_Name
## 649276 cg22179564
## 253124 cg08129561 SLC4A8;SLC4A8;SLC4A8;SLC4A8;SLC4A8
## 599622 cg20185668 RBM6;RBM6
## 13056 cg00400743 PRMT10
## 268646 cg08646202 ALG9;ALG9;ALG9;ALG9
## 338870 cg11005961 LTN1
## 280404 cg09035199 RARB;RARB;RARB;RARB;RARB;RARB;RARB;RARB;RARB;RARB
## 430093 cg14099387 FAXDC2
## 179576 cg05723277
## 45900 cg01421264 TRIM72;PYDC1
## Regulatory_Feature_Group Islands_Name
## 649276 Unclassified_Cell_type_specific
## 253124
## 599622
## 13056 chr4:148604954-148605625
## 268646
## 338870 Promoter_Associated chr21:30364965-30365342
## 280404 NonGene_Associated
## 430093
## 179576
## 45900 chr16:31225956-31228264
## Relation_to_Island logFC AveExpr t P.Value
## 649276 OpenSea -0.3709180 -0.6268766 -5.250773 5.309413e-07
## 253124 OpenSea -0.5467845 2.5479203 -5.017068 1.517715e-06
## 599622 OpenSea -0.3859477 2.7935255 -5.000686 1.631815e-06
## 13056 N_Shore -0.4221099 1.7096600 -4.881273 2.755152e-06
## 268646 OpenSea -0.2876797 0.3169785 -4.873246 2.853048e-06
## 338870 S_Shore -0.3242807 0.3819812 -4.862545 2.988812e-06
## 280404 OpenSea -0.5665031 1.4683072 -4.842339 3.262435e-06
## 430093 OpenSea -0.5108562 2.1106915 -4.803883 3.851799e-06
## 179576 OpenSea -0.4766184 1.8207891 -4.786548 4.150035e-06
## 45900 Island -0.4286995 -3.1723902 -4.777763 4.309584e-06
## adj.P.Val B
## 649276 0.1787759 5.414453
## 253124 0.1787759 4.533066
## 599622 0.1787759 4.472272
## 13056 0.1787759 4.033151
## 268646 0.1787759 4.003891
## 338870 0.1787759 3.964934
## 280404 0.1787759 3.891535
## 430093 0.1787759 3.752414
## 179576 0.1787759 3.689951
## 45900 0.1787759 3.658357head(top_GIFT_vs_FX$dmr,10)## NULLwrite.table(top_GIFT_vs_FX$dma,file="novakovic_top_GIFT_vs_FX.tsv",sep="\t",quote=FALSE)
# Allele
top_GIFT_vs_FX$dma$unmeth <- "T"
top_GIFT_vs_FX$dma$meth <- "C"
top_GIFT_vs_FX$fit$SE <- sqrt(top_GIFT_vs_FX$fit$s2.post) * top_GIFT_vs_FX$fit$stdev.unscaled
# Extract required columns from dma
top_GIFT_vs_FX_metal <-top_GIFT_vs_FX$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_GIFT_vs_FX_metal)## Row.names meth unmeth AveExpr P.Value
## 649276 cg22179564 C T -0.6268766 5.309413e-07
## 253124 cg08129561 C T 2.5479203 1.517715e-06
## 599622 cg20185668 C T 2.7935255 1.631815e-06
## 13056 cg00400743 C T 1.7096600 2.755152e-06
## 268646 cg08646202 C T 0.3169785 2.853048e-06
## 338870 cg11005961 C T 0.3819812 2.988812e-06# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_GIFT_vs_FX$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c(4,3)]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_GIFT_vs_FX$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c(4,3)]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_GIFT_vs_FX_metal <- merge(top_GIFT_vs_FX_metal, fitCE)
top_GIFT_vs_FX_metal <- merge(top_GIFT_vs_FX_metal, fitSE)
# Number of effective participants
Neff = 4/sum(1/table(groups))
top_GIFT_vs_FX_metal$N <- Neff
# Output for Meta-analysis
write.table(top_GIFT_vs_FX_metal, file="novakovic_top_GIFT_vs_FX_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)
head(top_GIFT_vs_FX$dma)## Row.names UCSC_RefGene_Name
## 649276 cg22179564
## 253124 cg08129561 SLC4A8;SLC4A8;SLC4A8;SLC4A8;SLC4A8
## 599622 cg20185668 RBM6;RBM6
## 13056 cg00400743 PRMT10
## 268646 cg08646202 ALG9;ALG9;ALG9;ALG9
## 338870 cg11005961 LTN1
## Regulatory_Feature_Group Islands_Name
## 649276 Unclassified_Cell_type_specific
## 253124
## 599622
## 13056 chr4:148604954-148605625
## 268646
## 338870 Promoter_Associated chr21:30364965-30365342
## Relation_to_Island logFC AveExpr t P.Value
## 649276 OpenSea -0.3709180 -0.6268766 -5.250773 5.309413e-07
## 253124 OpenSea -0.5467845 2.5479203 -5.017068 1.517715e-06
## 599622 OpenSea -0.3859477 2.7935255 -5.000686 1.631815e-06
## 13056 N_Shore -0.4221099 1.7096600 -4.881273 2.755152e-06
## 268646 OpenSea -0.2876797 0.3169785 -4.873246 2.853048e-06
## 338870 S_Shore -0.3242807 0.3819812 -4.862545 2.988812e-06
## adj.P.Val B unmeth meth
## 649276 0.1787759 5.414453 T C
## 253124 0.1787759 4.533066 T C
## 599622 0.1787759 4.472272 T C
## 13056 0.1787759 4.033151 T C
## 268646 0.1787759 4.003891 T C
## 338870 0.1787759 3.964934 T Chead(top_GIFT_vs_FX$dmr)## NULLhead(top_GIFT_vs_FX_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000103 C T 2.775991 0.020682295 -0.22193837 0.09486855 105
## 2 cg00000109 C T 1.868567 0.008603958 -0.24286527 0.09117302 105
## 3 cg00000155 C T 3.144300 0.025332151 -0.15634435 0.06918670 105
## 4 cg00000158 C T 3.254767 0.359191538 -0.07767706 0.08444672 105
## 5 cg00000165 C T -2.105229 0.353503424 0.08144448 0.08749840 105
## 6 cg00000221 C T 1.109474 0.017068470 -0.27655479 0.11460397 105saveRDS(top_GIFT_vs_FX,file="novakovic_GIFT_vs_FX.rds")non-ART Vs ART
samplesheet<-birth
groups <- factor(samplesheet$art_status,levels=c("non-ART","ART"))
sex <- factor(samplesheet$sex,levels=c("M","F"))
Mvals<-Mval[,colnames(Mval)%in% samplesheet$Basename]
betas<-beta[,colnames(beta)%in% samplesheet$Basename]
top_nat_vs_ART <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mvals,name="top_nat_vs_ART",
myann=myann ,beta= betas)## Your contrast returned 13368 individually significant probes. We recommend the default setting of pcutoff in dmrcate().## Fitting chr1...## Fitting chr2...## Fitting chr3...## Fitting chr4...## Fitting chr5...## Fitting chr6...## Fitting chr7...## Fitting chr8...## Fitting chr9...## Fitting chr10...## Fitting chr11...## Fitting chr12...## Fitting chr13...## Fitting chr14...## Fitting chr15...## Fitting chr16...## Fitting chr17...## Fitting chr18...## Fitting chr19...## Fitting chr20...## Fitting chr21...## Fitting chr22...## Demarcating regions...## Done!## snapshotDate(): 2022-04-26## see ?DMRcatedata and browseVignettes('DMRcatedata') for documentation## loading from cache
##
## RCircos.Core.Components initialized.
## Type ?RCircos.Reset.Plot.Parameters to see how to modify the core components.
head(top_nat_vs_ART$dma,10)## Row.names UCSC_RefGene_Name
## 656385 cg22481606 SUPT4H1
## 153960 cg04891094 GGA1;GGA1;GGA1
## 11452 cg00352360 RPL23;SNORA21
## 413802 cg13592399 NID2;NID2
## 232993 cg07465122 DERL1;DERL1;DERL1;DERL1
## 52374 cg01627351
## 195777 cg06262436 ISM1
## 45699 cg01414687 FABP3
## 226991 cg07267166 ZNF323;ZNF323;ZKSCAN3
## 564284 cg18789663 PLD5
## Regulatory_Feature_Group Islands_Name
## 656385 Promoter_Associated chr17:56429455-56429906
## 153960 Promoter_Associated chr22:38004443-38005271
## 11452 chr17:37009471-37010118
## 413802 Unclassified_Cell_type_specific chr14:52534581-52536722
## 232993 Promoter_Associated chr8:124054057-124054733
## 52374 Unclassified_Cell_type_specific
## 195777 chr20:13200670-13202616
## 45699 Unclassified
## 226991 Promoter_Associated_Cell_type_specific
## 564284 chr1:242687922-242688682
## Relation_to_Island logFC AveExpr t P.Value
## 656385 Island -0.3339464 -2.749103 -6.877678 6.744491e-11
## 153960 N_Shore -0.2845107 -3.487678 -6.750162 1.395542e-10
## 11452 N_Shore -0.3472372 -2.680899 -6.684368 2.024335e-10
## 413802 Island -0.4446331 -3.667192 -6.606711 3.131111e-10
## 232993 Island -0.4421342 -4.561650 -6.600575 3.240447e-10
## 52374 OpenSea -0.3262284 -2.623784 -6.597397 3.298558e-10
## 195777 Island -0.3606570 -2.228232 -6.559690 4.071225e-10
## 45699 OpenSea -0.4031644 -1.283429 -6.536978 4.619780e-10
## 226991 OpenSea -0.4578230 -2.122062 -6.511942 5.308787e-10
## 564284 Island -0.2854060 -2.776226 -6.465623 6.859886e-10
## adj.P.Val B
## 656385 4.364235e-05 14.04220
## 153960 4.364235e-05 13.37721
## 11452 4.364235e-05 13.03712
## 413802 4.364235e-05 12.63840
## 232993 4.364235e-05 12.60703
## 52374 4.364235e-05 12.59078
## 195777 4.584231e-05 12.39842
## 45699 4.584231e-05 12.28289
## 226991 4.682610e-05 12.15584
## 564284 5.445680e-05 11.92161head(top_nat_vs_ART$dmr,10)## GRanges object with 10 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr17 4803684-4805392 * | 6 4.76415e-17
## [2] chr1 92949337-92950836 * | 29 3.93667e-23
## [3] chr6 44238228-44238905 * | 4 1.28097e-12
## [4] chr19 21949968-21950478 * | 8 3.86533e-14
## [5] chr22 38141814-38142561 * | 10 2.46143e-16
## [6] chr1 150551593-150552817 * | 14 8.74187e-22
## [7] chr22 38597691-38599166 * | 14 9.01588e-12
## [8] chr13 27295928-27296010 * | 3 1.57290e-10
## [9] chr17 19265610-19266474 * | 12 1.77821e-14
## [10] chr19 53465830-53466414 * | 13 7.50511e-16
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 7.43642e-13 0.000540807 1.70183e-11 -0.1053286 -0.06622385
## [2] 9.29272e-05 0.002278879 6.97756e-07 -0.0274507 -0.00744223
## [3] 4.85155e-07 0.001470802 1.62224e-06 -0.0230363 -0.02030476
## [4] 6.01004e-05 0.003365381 2.12967e-06 -0.0256890 -0.01330783
## [5] 4.39717e-06 0.012776526 3.65383e-06 -0.0197268 -0.01201911
## [6] 2.39984e-03 0.000478644 4.17578e-06 -0.0277339 -0.01009005
## [7] 2.40933e-06 0.009611601 5.27150e-06 -0.0265885 -0.01172901
## [8] 1.15075e-06 0.002976441 5.31738e-06 0.0945277 0.09405572
## [9] 8.37441e-05 0.001412716 5.82999e-06 -0.0179540 -0.00810600
## [10] 9.38077e-05 0.011161533 8.37236e-06 -0.0243631 -0.01190277
## overlapping.genes
## <character>
## [1] C17orf107, CHRNE
## [2] GFI1
## [3] TMEM151B
## [4] ZNF100
## [5] NOL12, TRIOBP
## [6] MCL1
## [7] MAFF, PLA2G6
## [8] <NA>
## [9] B9D1
## [10] ZNF816, ZNF321P, CTD..
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthswrite.table(top_nat_vs_ART$dma,file="novakovic_top_nat_vs_ART.tsv",sep="\t",quote=FALSE)
# Allele
top_nat_vs_ART$dma$unmeth <- "T"
top_nat_vs_ART$dma$meth <- "C"
top_nat_vs_ART$fit$SE <- sqrt(top_nat_vs_ART$fit$s2.post) * top_nat_vs_ART$fit$stdev.unscaled
# Extract required columns from dma
top_nat_vs_ART_metal <-top_nat_vs_ART$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_nat_vs_ART_metal)## Row.names meth unmeth AveExpr P.Value
## 656385 cg22481606 C T -2.749103 6.744491e-11
## 153960 cg04891094 C T -3.487678 1.395542e-10
## 11452 cg00352360 C T -2.680899 2.024335e-10
## 413802 cg13592399 C T -3.667192 3.131111e-10
## 232993 cg07465122 C T -4.561650 3.240447e-10
## 52374 cg01627351 C T -2.623784 3.298558e-10# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_nat_vs_ART$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c(4,3)]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_nat_vs_ART$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c(4,3)]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_nat_vs_ART_metal <- merge(top_nat_vs_ART_metal, fitCE)
top_nat_vs_ART_metal <- merge(top_nat_vs_ART_metal, fitSE)
# Number of effective participants
Neff = 4/sum(1/table(groups))
top_nat_vs_ART_metal$N <- Neff
# Output for Meta-analysis
write.table(top_nat_vs_ART_metal, file="novakovic_top_nat_vs_ART_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)
head(top_nat_vs_ART$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 656385 cg22481606 SUPT4H1 Promoter_Associated
## 153960 cg04891094 GGA1;GGA1;GGA1 Promoter_Associated
## 11452 cg00352360 RPL23;SNORA21
## 413802 cg13592399 NID2;NID2 Unclassified_Cell_type_specific
## 232993 cg07465122 DERL1;DERL1;DERL1;DERL1 Promoter_Associated
## 52374 cg01627351 Unclassified_Cell_type_specific
## Islands_Name Relation_to_Island logFC AveExpr
## 656385 chr17:56429455-56429906 Island -0.3339464 -2.749103
## 153960 chr22:38004443-38005271 N_Shore -0.2845107 -3.487678
## 11452 chr17:37009471-37010118 N_Shore -0.3472372 -2.680899
## 413802 chr14:52534581-52536722 Island -0.4446331 -3.667192
## 232993 chr8:124054057-124054733 Island -0.4421342 -4.561650
## 52374 OpenSea -0.3262284 -2.623784
## t P.Value adj.P.Val B unmeth meth
## 656385 -6.877678 6.744491e-11 4.364235e-05 14.04220 T C
## 153960 -6.750162 1.395542e-10 4.364235e-05 13.37721 T C
## 11452 -6.684368 2.024335e-10 4.364235e-05 13.03712 T C
## 413802 -6.606711 3.131111e-10 4.364235e-05 12.63840 T C
## 232993 -6.600575 3.240447e-10 4.364235e-05 12.60703 T C
## 52374 -6.597397 3.298558e-10 4.364235e-05 12.59078 T Chead(top_nat_vs_ART$dmr)## GRanges object with 6 ranges and 8 metadata columns:
## seqnames ranges strand | no.cpgs min_smoothed_fdr
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr17 4803684-4805392 * | 6 4.76415e-17
## [2] chr1 92949337-92950836 * | 29 3.93667e-23
## [3] chr6 44238228-44238905 * | 4 1.28097e-12
## [4] chr19 21949968-21950478 * | 8 3.86533e-14
## [5] chr22 38141814-38142561 * | 10 2.46143e-16
## [6] chr1 150551593-150552817 * | 14 8.74187e-22
## Stouffer HMFDR Fisher maxdiff meandiff
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 7.43642e-13 0.000540807 1.70183e-11 -0.1053286 -0.06622385
## [2] 9.29272e-05 0.002278879 6.97756e-07 -0.0274507 -0.00744223
## [3] 4.85155e-07 0.001470802 1.62224e-06 -0.0230363 -0.02030476
## [4] 6.01004e-05 0.003365381 2.12967e-06 -0.0256890 -0.01330783
## [5] 4.39717e-06 0.012776526 3.65383e-06 -0.0197268 -0.01201911
## [6] 2.39984e-03 0.000478644 4.17578e-06 -0.0277339 -0.01009005
## overlapping.genes
## <character>
## [1] C17orf107, CHRNE
## [2] GFI1
## [3] TMEM151B
## [4] ZNF100
## [5] NOL12, TRIOBP
## [6] MCL1
## -------
## seqinfo: 22 sequences from an unspecified genome; no seqlengthshead(top_nat_vs_ART_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000103 C T 2.804009 0.04755867 -0.158040608 0.07930231 166.9952
## 2 cg00000109 C T 1.851047 0.81633363 0.018094667 0.07780892 166.9952
## 3 cg00000155 C T 3.156911 0.96844098 -0.002294823 0.05793466 166.9952
## 4 cg00000158 C T 3.224772 0.07503602 0.113970693 0.06370433 166.9952
## 5 cg00000165 C T -2.104843 0.53035004 0.041074941 0.06535324 166.9952
## 6 cg00000221 C T 1.088065 0.19420162 0.126508047 0.09713588 166.9952saveRDS(top_nat_vs_ART,file="novakovic_nat_vs_ART.rds")Fresh embryo Vs Frozen embryo
samplesheet <- subset(birth,art_subtype_ch1=="Fresh embryo"|art_subtype_ch1=="Frozen embryo")
groups <- factor(samplesheet$art_subtype,levels=c("Fresh embryo","Frozen embryo"))
sex <- factor(samplesheet$sex,levels=c("M","F"))
Mvals<-Mval[,colnames(Mval)%in% samplesheet$Basename]
betas<-beta[,colnames(beta)%in% samplesheet$Basename]
top_fh_vs_fz <- dm_analysis(samplesheet=samplesheet,
sex=sex,groups=groups,mx=Mvals,name="top_fh_vs_fz",
myann=myann ,beta= betas)
head(top_fh_vs_fz$dma,10)## Row.names UCSC_RefGene_Name
## 84778 cg02660277 PTPRN2;PTPRN2;PTPRN2
## 741713 cg25789405
## 655472 cg22442197 RPS2;SNORA10
## 225683 cg07224221 RTP3
## 93377 cg02932204 PDZD7
## 594607 cg19983118
## 450713 cg14745383 TRAPPC9;TRAPPC9
## 297858 cg09616692 GFOD2;GFOD2
## 387340 cg12687426 KCNMB3;KCNMB3;KCNMB3;KCNMB3;KCNMB3
## 199370 cg06385817 KCTD21-AS1;KCTD21-AS1
## Regulatory_Feature_Group Islands_Name
## 84778 chr7:157440024-157442721
## 741713
## 655472 chr16:2014164-2015451
## 225683
## 93377 Unclassified_Cell_type_specific
## 594607 Promoter_Associated chr8:103822614-103823263
## 450713 Gene_Associated
## 297858
## 387340 chr3:178978948-178979364
## 199370
## Relation_to_Island logFC AveExpr t P.Value
## 84778 N_Shore -0.5795926 0.6172685 -5.062995 1.681695e-06
## 741713 OpenSea -0.4077803 3.5052246 -5.016527 2.047776e-06
## 655472 N_Shore -0.3887436 1.6464037 -4.874825 3.709926e-06
## 225683 OpenSea -0.3855097 2.7283251 -4.872687 3.743061e-06
## 93377 OpenSea 0.9753297 -0.8077276 4.849703 4.117929e-06
## 594607 Island -0.3887698 -3.1597680 -4.837678 4.328347e-06
## 450713 OpenSea -0.3503416 3.5933304 -4.799757 5.062525e-06
## 297858 OpenSea -0.4298725 2.8216829 -4.755132 6.081972e-06
## 387340 N_Shore -1.0536454 2.5230583 -4.662790 8.861886e-06
## 199370 OpenSea -0.2985104 1.6034168 -4.653318 9.208502e-06
## adj.P.Val B
## 84778 0.5726721 1.8239294
## 741713 0.5726721 1.7148261
## 655472 0.5726721 1.3850595
## 225683 0.5726721 1.3801189
## 93377 0.5726721 1.3270746
## 594607 0.5726721 1.2993706
## 450713 0.5741221 1.2122349
## 297858 0.6035171 1.1101359
## 387340 0.6767864 0.9004316
## 199370 0.6767864 0.8790417head(top_fh_vs_fz$dmr,10)## NULL# Allele
top_fh_vs_fz$dma$unmeth <- "T"
top_fh_vs_fz$dma$meth <- "C"
top_fh_vs_fz$fit$SE <- sqrt(top_fh_vs_fz$fit$s2.post) * top_fh_vs_fz$fit$stdev.unscaled
# Extract required columns from dma
top_fh_vs_fz_metal <-top_fh_vs_fz$dma[,c("Row.names", "meth", "unmeth", "AveExpr", "P.Value")]
head(top_fh_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value
## 84778 cg02660277 C T 0.6172685 1.681695e-06
## 741713 cg25789405 C T 3.5052246 2.047776e-06
## 655472 cg22442197 C T 1.6464037 3.709926e-06
## 225683 cg07224221 C T 2.7283251 3.743061e-06
## 93377 cg02932204 C T -0.8077276 4.117929e-06
## 594607 cg19983118 C T -3.1597680 4.328347e-06# Convert fit outputs to dataframes
fitCE <- as.data.frame(top_fh_vs_fz$fit$coefficients)
fitCE$Row.names <- row.names(fitCE)
fitCE <- fitCE[,c(4,3)]
names(fitCE)[2]<- "coefficient"
fitSE <- as.data.frame(top_fh_vs_fz$fit$SE)
fitSE$Row.names <- row.names(fitSE)
fitSE <- fitSE[,c(4,3)]
names(fitSE)[2]<- "SE"
# Merge Datasets
top_fh_vs_fz_metal <- merge(top_fh_vs_fz_metal, fitCE)
top_fh_vs_fz_metal <- merge(top_fh_vs_fz_metal, fitSE)
# Number of effective participants
Neff = 4/sum(1/table(groups))
top_fh_vs_fz_metal$N <- Neff
# Output for Meta-analysis
write.table(top_fh_vs_fz_metal, file="novakovic_top_fh_vs_fz_metal.tsv",sep="\t",quote=FALSE, row.names = FALSE)
head(top_fh_vs_fz$dma)## Row.names UCSC_RefGene_Name Regulatory_Feature_Group
## 84778 cg02660277 PTPRN2;PTPRN2;PTPRN2
## 741713 cg25789405
## 655472 cg22442197 RPS2;SNORA10
## 225683 cg07224221 RTP3
## 93377 cg02932204 PDZD7 Unclassified_Cell_type_specific
## 594607 cg19983118 Promoter_Associated
## Islands_Name Relation_to_Island logFC AveExpr
## 84778 chr7:157440024-157442721 N_Shore -0.5795926 0.6172685
## 741713 OpenSea -0.4077803 3.5052246
## 655472 chr16:2014164-2015451 N_Shore -0.3887436 1.6464037
## 225683 OpenSea -0.3855097 2.7283251
## 93377 OpenSea 0.9753297 -0.8077276
## 594607 chr8:103822614-103823263 Island -0.3887698 -3.1597680
## t P.Value adj.P.Val B unmeth meth
## 84778 -5.062995 1.681695e-06 0.5726721 1.823929 T C
## 741713 -5.016527 2.047776e-06 0.5726721 1.714826 T C
## 655472 -4.874825 3.709926e-06 0.5726721 1.385060 T C
## 225683 -4.872687 3.743061e-06 0.5726721 1.380119 T C
## 93377 4.849703 4.117929e-06 0.5726721 1.327075 T C
## 594607 -4.837678 4.328347e-06 0.5726721 1.299371 T Chead(top_fh_vs_fz$dmr)## NULLhead(top_fh_vs_fz_metal)## Row.names meth unmeth AveExpr P.Value coefficient SE N
## 1 cg00000103 C T 2.718354 0.6835324 0.04405606 0.10778622 85.71429
## 2 cg00000109 C T 1.806397 0.3133241 0.10598048 0.10462686 85.71429
## 3 cg00000155 C T 3.105284 0.3734648 0.06970295 0.07799718 85.71429
## 4 cg00000158 C T 3.235231 0.1800816 0.13702828 0.10156833 85.71429
## 5 cg00000165 C T -2.085600 0.8718349 0.01483428 0.09173564 85.71429
## 6 cg00000221 C T 1.039956 0.1013793 0.22321925 0.13511198 85.71429saveRDS(top_fh_vs_fz,file="novakovic_fh_vs_fz.rds")Venn diagrams of the differential methylated probes for each contrast
v1 <- list("FH up" = top_nat_vs_fh$dm_up ,
"FZ up" = top_nat_vs_fz$dm_up ,
"FH dn" = top_nat_vs_fh$dm_dn ,
"FZ dn" = top_nat_vs_fz$dm_dn )
plot(euler(v1, shape = "ellipse"), quantities = TRUE)## Warning in colSums(id & !empty) == 0 | merged_sets: longer object length is not
## a multiple of shorter object length
Comparison of probes altered by fresh and frozen IVF procedures
v2 <- list("FH up" = top_nat_vs_fh$dm_up ,
"FH dn" = top_nat_vs_fh$dm_dn ,
"GIFT up" = top_nat_vs_GIFT$dm_up ,
"GIFT dn" = top_nat_vs_GIFT$dm_dn )
plot(euler(v2, shape = "ellipse"), quantities = TRUE)
Comparison of probes altered by GIFT, fresh and frozen IVF procedures
v3 <- list("FZ up" = top_nat_vs_fz$dm_up ,
"FZ dn" = top_nat_vs_fz$dm_dn ,
"GIFT up" = top_nat_vs_GIFT$dm_up ,
"GIFT dn" = top_nat_vs_GIFT$dm_dn )
plot(euler(v3, shape = "ellipse"), quantities = FALSE)
Comparison of probes altered by GIFT, fresh and frozen IVF procedures
Spearman correlations of each contrast
mycontrasts <- list("top_nat_vs_fh"=top_nat_vs_fh, "top_nat_vs_fz"=top_nat_vs_fz, "top_nat_vs_FX"=top_nat_vs_FX ,
"top_nat_vs_GIFT"=top_nat_vs_GIFT, "top_fh_vs_fz"=top_fh_vs_fz, "top_GIFT_vs_FX"=top_GIFT_vs_FX, "top_nat_vs_ART"=top_nat_vs_ART)
myrnks <- lapply(X = mycontrasts, FUN = myranks)
df <- join_all(myrnks,by="rn")
rownames(df) <- df$rn
df$rn=NULL
colnames(df) <- names(mycontrasts)
head(df)## top_nat_vs_fh top_nat_vs_fz top_nat_vs_FX top_nat_vs_GIFT
## cg25867694 0.2779684 3.6763299 0.3011779 2.759463
## cg16794867 0.2779684 0.7238417 0.2156508 1.880424
## cg00352360 0.3121982 1.2842258 0.2384943 1.464365
## cg18789663 0.3121982 1.0390963 0.2319340 1.906319
## cg06826756 0.3121982 1.5535760 0.2660528 1.586588
## cg17602885 -0.3121982 -1.5672834 -0.2647158 -1.821118
## top_fh_vs_fz top_GIFT_vs_FX top_nat_vs_ART
## cg25867694 -5.898023 1.436621 0.2972941
## cg16794867 -39.704466 1.345870 0.2552747
## cg00352360 -14.842332 2.300222 0.2293530
## cg18789663 -34.365614 1.342284 0.2345244
## cg06826756 -7.176904 2.301847 0.2912689
## cg17602885 7.280737 -2.042617 -0.2781630mycors <- cor(df,method = "spearman")
my_palette <- colorRampPalette(c("darkred","red", "orange", "yellow","white"))(n = 25)
heatmap.2(mycors,scale="none",margin=c(10, 10),cexRow=0.8,trace="none",cexCol=0.8,
col=my_palette,main="Spearman correlations")
mycors## top_nat_vs_fh top_nat_vs_fz top_nat_vs_FX top_nat_vs_GIFT
## top_nat_vs_fh 1.00000000 0.045031346 0.52110291 0.03585764
## top_nat_vs_fz 0.04503135 1.000000000 0.13361141 0.11050972
## top_nat_vs_FX 0.52110291 0.133611413 1.00000000 0.03893099
## top_nat_vs_GIFT 0.03585764 0.110509724 0.03893099 1.00000000
## top_fh_vs_fz -0.09323598 0.154258825 -0.03726622 0.08111275
## top_GIFT_vs_FX 0.05932582 -0.005131548 0.02715909 -0.16774856
## top_nat_vs_ART 0.26887275 0.196212113 0.42314333 0.12099776
## top_fh_vs_fz top_GIFT_vs_FX top_nat_vs_ART
## top_nat_vs_fh -0.093235981 0.059325821 0.268872746
## top_nat_vs_fz 0.154258825 -0.005131548 0.196212113
## top_nat_vs_FX -0.037266217 0.027159091 0.423143327
## top_nat_vs_GIFT 0.081112749 -0.167748564 0.120997764
## top_fh_vs_fz 1.000000000 -0.039962971 0.004837958
## top_GIFT_vs_FX -0.039962971 1.000000000 -0.002127211
## top_nat_vs_ART 0.004837958 -0.002127211 1.000000000Mitch analysis
library (mitch) ## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2if (! file.exists("ReactomePathways.gmt") ) {
download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip",
destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip",overwrite = TRUE)
}
genesets <- gmt_import("ReactomePathways.gmt")
top_nat_vs_fh_mitch <- run_mitch_1d(dma=top_nat_vs_fh$dma,name="nat_vs_fh") ## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_fh_mitch,20)## set setSize pANOVA
## 885 Role of phospholipids in phagocytosis 16 0.001606699
## 367 Generation of second messenger molecules 15 0.003459456
## 534 Metabolism of carbohydrates 178 0.003529133
## 715 Pre-NOTCH Processing in Golgi 15 0.003830771
## 743 RAC1 GTPase cycle 109 0.004066208
## 766 RHOD GTPase cycle 36 0.004368299
## 485 Ion homeostasis 26 0.004817678
## 552 MicroRNA (miRNA) biogenesis 20 0.005152500
## 745 RAC3 GTPase cycle 59 0.005945712
## 698 Platelet calcium homeostasis 17 0.007069312
## 924 Semaphorin interactions 39 0.008846170
## 752 RHO GTPase cycle 287 0.009202805
## 760 RHOA GTPase cycle 90 0.009317369
## 11 APC/C:Cdc20 mediated degradation of Cyclin B 22 0.010671950
## 428 Immune System 1144 0.012941277
## 279 Elevation of cytosolic Ca2+ levels 10 0.013013159
## 721 Processing of Capped Intronless Pre-mRNA 22 0.014269355
## 684 Peptide ligand-binding receptors 19 0.014993847
## 714 Pre-NOTCH Expression and Processing 44 0.016434257
## 608 Negative regulation of FLT3 13 0.016842328
## s.dist p.adjustANOVA
## 885 -0.45561234 0.7677224
## 367 0.43608801 0.7677224
## 534 -0.12724500 0.7677224
## 715 -0.43133369 0.7677224
## 743 0.15963532 0.7677224
## 766 0.27468507 0.7677224
## 485 -0.31953292 0.7677224
## 552 -0.36143224 0.7677224
## 745 0.20728913 0.7694877
## 698 -0.37743883 0.7694877
## 924 0.24243706 0.7694877
## 752 0.08990242 0.7694877
## 760 0.15886900 0.7694877
## 11 0.31458006 0.7694877
## 428 0.04474548 0.7694877
## 279 -0.45359512 0.7694877
## 721 -0.30191155 0.7694877
## 684 0.32246043 0.7694877
## 714 -0.20922074 0.7694877
## 608 0.38294137 0.7694877top_nat_vs_fz_mitch <- run_mitch_1d(dma=top_nat_vs_fz$dma,name="nat_vs_fz")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_fz_mitch,20)## set
## 366 Gene expression (Transcription)
## 368 Generic Transcription Pathway
## 31 Activation of HOX genes during differentiation
## 35 Activation of anterior HOX genes in hindbrain development during early embryogenesis
## 787 RNA Polymerase II Transcription
## 286 Estrogen-dependent gene expression
## 48 Anchoring of the basal body to the plasma membrane
## 275 ESR-mediated signaling
## 1004 Signaling by TGFB family members
## 595 NOTCH4 Intracellular Domain Regulates Transcription
## 228 Disease
## 984 Signaling by NOTCH3
## 978 Signaling by NOTCH
## 242 Diseases of signal transduction by growth factor receptors and second messengers
## 249 Downregulation of TGF-beta receptor signaling
## 991 Signaling by Nuclear Receptors
## 985 Signaling by NOTCH4
## 167 Constitutive Signaling by NOTCH1 HD+PEST Domain Mutants
## 168 Constitutive Signaling by NOTCH1 PEST Domain Mutants
## 980 Signaling by NOTCH1 HD+PEST Domain Mutants in Cancer
## setSize pANOVA s.dist p.adjustANOVA
## 366 1023 0.0002113954 -0.07010984 0.1971999
## 368 814 0.0003362461 -0.07530573 0.1971999
## 31 40 0.0007584256 -0.30791434 0.1971999
## 35 40 0.0007584256 -0.30791434 0.1971999
## 787 912 0.0008271809 -0.06665507 0.1971999
## 286 68 0.0013503445 -0.22505007 0.2644566
## 48 78 0.0015530171 -0.20756934 0.2644566
## 275 113 0.0019447059 -0.16911944 0.2897612
## 1004 84 0.0029147379 -0.18819126 0.3860408
## 595 14 0.0037730684 -0.44718848 0.4451666
## 228 1075 0.0041080811 -0.05311535 0.4451666
## 984 30 0.0055376159 -0.29277340 0.4783713
## 978 133 0.0058120446 -0.13889337 0.4783713
## 242 295 0.0069470602 -0.09194000 0.4783713
## 249 26 0.0073826582 -0.30367958 0.4783713
## 991 149 0.0075289235 -0.12723914 0.4783713
## 985 68 0.0075486847 -0.18760622 0.4783713
## 167 41 0.0088290006 -0.23653086 0.4783713
## 168 41 0.0088290006 -0.23653086 0.4783713
## 980 41 0.0088290006 -0.23653086 0.4783713top_nat_vs_GIFT_mitch <- run_mitch_1d(dma=top_nat_vs_GIFT$dma,name="nat_vs_GIFT")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_GIFT_mitch,20)## set setSize
## 368 Generic Transcription Pathway 814
## 366 Gene expression (Transcription) 1023
## 1004 Signaling by TGFB family members 84
## 225 Deubiquitination 197
## 1103 Transcriptional Regulation by TP53 297
## 787 RNA Polymerase II Transcription 912
## 1142 UCH proteinases 74
## 941 Signaling by Activin 11
## 486 Ion transport by P-type ATPases 27
## 1059 TP53 Regulates Transcription of DNA Repair Genes 51
## 82 Bile acid and bile salt metabolism 15
## 1107 Transcriptional regulation by RUNX1 133
## 599 NRIF signals cell death from the nucleus 14
## 901 SMAD2/SMAD3:SMAD4 heterotrimer regulates transcription 27
## 249 Downregulation of TGF-beta receptor signaling 26
## 904 SUMO E3 ligases SUMOylate target proteins 132
## 684 Peptide ligand-binding receptors 19
## 1143 Ub-specific processing proteases 133
## 985 Signaling by NOTCH4 68
## 96 CLEC7A (Dectin-1) signaling 84
## pANOVA s.dist p.adjustANOVA
## 368 0.0002143287 -0.07773747 0.2189629
## 366 0.0003673875 -0.06741430 0.2189629
## 1004 0.0010568687 -0.20704514 0.3239407
## 225 0.0012945258 -0.13348884 0.3239407
## 1103 0.0013588116 -0.10873710 0.3239407
## 787 0.0017287908 -0.06246491 0.3434531
## 1142 0.0024102432 -0.20430490 0.3653802
## 941 0.0025219145 -0.52604410 0.3653802
## 486 0.0027767323 -0.33274940 0.3653802
## 1059 0.0030652703 -0.23991326 0.3653802
## 82 0.0049268131 0.41940659 0.5221744
## 1107 0.0054247835 -0.14002415 0.5221744
## 599 0.0056948546 -0.42686518 0.5221744
## 901 0.0065629896 -0.30237181 0.5587917
## 249 0.0077567597 -0.30179925 0.6164038
## 904 0.0095390316 -0.13101963 0.6507784
## 684 0.0097473452 0.34262236 0.6507784
## 1143 0.0110441548 -0.12797417 0.6507784
## 985 0.0120192679 -0.17637123 0.6507784
## 96 0.0120728323 -0.15870119 0.6507784top_nat_vs_FX_mitch <- run_mitch_1d(dma=top_nat_vs_FX$dma,name="nat_vs_FX")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_FX_mitch,20)## set
## 534 Metabolism of carbohydrates
## 485 Ion homeostasis
## 715 Pre-NOTCH Processing in Golgi
## 885 Role of phospholipids in phagocytosis
## 228 Disease
## 367 Generation of second messenger molecules
## 366 Gene expression (Transcription)
## 552 MicroRNA (miRNA) biogenesis
## 760 RHOA GTPase cycle
## 698 Platelet calcium homeostasis
## 684 Peptide ligand-binding receptors
## 743 RAC1 GTPase cycle
## 31 Activation of HOX genes during differentiation
## 35 Activation of anterior HOX genes in hindbrain development during early embryogenesis
## 48 Anchoring of the basal body to the plasma membrane
## 766 RHOD GTPase cycle
## 150 Class A/1 (Rhodopsin-like receptors)
## 1057 TP53 Regulates Transcription of Cell Cycle Genes
## 714 Pre-NOTCH Expression and Processing
## 787 RNA Polymerase II Transcription
## setSize pANOVA s.dist p.adjustANOVA
## 534 178 0.002535029 -0.13167825 0.5997978
## 485 26 0.002712972 -0.33987587 0.5997978
## 715 15 0.003252534 -0.43894296 0.5997978
## 885 16 0.003608389 -0.42036103 0.5997978
## 228 1075 0.003639521 -0.05381998 0.5997978
## 367 15 0.004183287 0.42719283 0.5997978
## 366 1023 0.004320860 -0.05400806 0.5997978
## 552 20 0.004479889 -0.36723024 0.5997978
## 760 90 0.005838662 0.16842928 0.5997978
## 698 17 0.006469335 -0.38156169 0.5997978
## 684 19 0.006666762 0.35963515 0.5997978
## 743 109 0.006853844 0.15024164 0.5997978
## 31 40 0.007044605 -0.24641749 0.5997978
## 35 40 0.007044605 -0.24641749 0.5997978
## 48 78 0.008671977 -0.17217062 0.6891331
## 766 36 0.009982619 0.24830344 0.6974241
## 150 47 0.010436442 0.21611886 0.6974241
## 1057 43 0.011229207 -0.22365517 0.6974241
## 714 44 0.011650227 -0.21998258 0.6974241
## 787 912 0.011701746 -0.05026303 0.6974241top_GIFT_vs_FX_mitch <- run_mitch_1d(dma=top_GIFT_vs_FX$dma,name="GIFT_vs_FX")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_GIFT_vs_FX_mitch,20)## set setSize
## 745 RAC3 GTPase cycle 59
## 721 Processing of Capped Intronless Pre-mRNA 22
## 367 Generation of second messenger molecules 15
## 850 Regulation of actin dynamics for phagocytic cup formation 44
## 373 Gluconeogenesis 24
## 1018 Sphingolipid de novo biosynthesis 22
## 1000 Signaling by Rho GTPases 429
## 769 RHOH GTPase cycle 29
## 428 Immune System 1144
## 756 RHO GTPases Activate WASPs and WAVEs 29
## 751 RHO GTPase Effectors 187
## 534 Metabolism of carbohydrates 178
## 561 Mitochondrial protein import 47
## 992 Signaling by PDGF 25
## 961 Signaling by FGFR2 in disease 24
## 1001 Signaling by Rho GTPases, Miro GTPases and RHOBTB3 441
## 1142 UCH proteinases 74
## 744 RAC2 GTPase cycle 56
## 1024 Sulfur amino acid metabolism 15
## 531 Metabolism 1235
## pANOVA s.dist p.adjustANOVA
## 745 0.001407298 0.24061233 0.9171940
## 721 0.001538916 -0.39019679 0.9171940
## 367 0.004263388 0.42629623 0.9449736
## 850 0.004816679 0.24582873 0.9449736
## 373 0.005516085 -0.32739308 0.9449736
## 1018 0.006802370 -0.33343797 0.9449736
## 1000 0.007413908 0.07609155 0.9449736
## 769 0.007438322 0.28731152 0.9449736
## 428 0.007852202 0.04785876 0.9449736
## 756 0.007985077 0.28475158 0.9449736
## 751 0.010120490 0.10948105 0.9449736
## 534 0.010804527 -0.11117836 0.9449736
## 561 0.010846794 -0.21498612 0.9449736
## 992 0.011098684 -0.29356004 0.9449736
## 961 0.012193246 -0.29569940 0.9689566
## 1001 0.013040917 0.06962344 0.9715483
## 1142 0.016145324 0.16198187 0.9976463
## 744 0.019194693 0.18111497 0.9976463
## 1024 0.021659485 -0.34253524 0.9976463
## 531 0.023316256 -0.03948031 0.9976463top_nat_vs_ART_mitch <- run_mitch_1d(dma=top_nat_vs_ART$dma,name="nat_vs_ART")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_nat_vs_ART_mitch,20)## set setSize pANOVA
## 366 Gene expression (Transcription) 1023 0.0007377169
## 885 Role of phospholipids in phagocytosis 16 0.0021797310
## 787 RNA Polymerase II Transcription 912 0.0027098866
## 228 Disease 1075 0.0028066524
## 684 Peptide ligand-binding receptors 19 0.0036651398
## 368 Generic Transcription Pathway 814 0.0042538245
## 534 Metabolism of carbohydrates 178 0.0054065940
## 485 Ion homeostasis 26 0.0054355926
## 715 Pre-NOTCH Processing in Golgi 15 0.0058173847
## 1057 TP53 Regulates Transcription of Cell Cycle Genes 43 0.0061574242
## 48 Anchoring of the basal body to the plasma membrane 78 0.0064636100
## 698 Platelet calcium homeostasis 17 0.0068993737
## 760 RHOA GTPase cycle 90 0.0092210271
## 390 HATs acetylate histones 60 0.0106030231
## 656 Other semaphorin interactions 10 0.0114313765
## 150 Class A/1 (Rhodopsin-like receptors) 47 0.0119299956
## 367 Generation of second messenger molecules 15 0.0119963524
## 128 Cell surface interactions at the vascular wall 57 0.0120937665
## 924 Semaphorin interactions 39 0.0121375269
## 552 MicroRNA (miRNA) biogenesis 20 0.0126059302
## s.dist p.adjustANOVA
## 366 -0.06387173 0.6853378
## 885 -0.44260464 0.6853378
## 787 -0.05978675 0.6853378
## 228 -0.05530624 0.6853378
## 684 0.38515434 0.6853378
## 368 -0.06004462 0.6853378
## 534 -0.12132895 0.6853378
## 485 -0.31511824 0.6853378
## 715 -0.41137262 0.6853378
## 1057 -0.24162613 0.6853378
## 48 -0.17863392 0.6853378
## 698 -0.37857366 0.6853378
## 760 0.15908673 0.7309770
## 390 -0.19097747 0.7309770
## 656 0.46196162 0.7309770
## 150 0.21216938 0.7309770
## 367 0.37474193 0.7309770
## 128 0.19237793 0.7309770
## 924 0.23226977 0.7309770
## 552 -0.32234221 0.7309770top_fh_vs_fz_mitch <- run_mitch_1d(dma=top_fh_vs_fz$dma,name="fh_vs_fz")## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(top_fh_vs_fz_mitch,20)## set
## 14 APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1
## 122 Cell Cycle
## 368 Generic Transcription Pathway
## 886 S Phase
## 124 Cell Cycle, Mitotic
## 985 Signaling by NOTCH4
## 957 Signaling by FGFR1
## 1030 Synthesis of DNA
## 984 Signaling by NOTCH3
## 10 APC/C-mediated degradation of cell cycle proteins
## 860 Regulation of mitotic cell cycle
## 962 Signaling by FGFR3
## 1136 Transport of vitamins, nucleosides, and related molecules
## 69 Autodegradation of Cdh1 by Cdh1:APC/C
## 932 Signal Transduction
## 12 APC/C:Cdc20 mediated degradation of Securin
## 175 Cyclin A:Cdk2-associated events at S phase entry
## 80 Beta-catenin independent WNT signaling
## 901 SMAD2/SMAD3:SMAD4 heterotrimer regulates transcription
## 366 Gene expression (Transcription)
## setSize pANOVA s.dist p.adjustANOVA
## 14 64 0.0006664414 -0.24625378 0.2628011
## 122 521 0.0008574973 -0.08631796 0.2628011
## 368 814 0.0009769290 -0.06924850 0.2628011
## 886 138 0.0012759637 -0.15927509 0.2628011
## 124 418 0.0018288559 -0.08967549 0.2628011
## 985 68 0.0019722228 -0.21728206 0.2628011
## 957 29 0.0023761430 -0.32616877 0.2628011
## 1030 103 0.0027884009 -0.17086825 0.2628011
## 984 30 0.0030976169 -0.31217625 0.2628011
## 10 77 0.0034594752 -0.19300504 0.2628011
## 860 77 0.0034594752 -0.19300504 0.2628011
## 962 27 0.0034979531 -0.32483023 0.2628011
## 1136 21 0.0036226410 0.36684709 0.2628011
## 69 56 0.0044041820 -0.22023228 0.2628011
## 932 1333 0.0050843283 -0.04716534 0.2628011
## 12 58 0.0055026970 -0.21097911 0.2628011
## 175 74 0.0055210755 -0.18684298 0.2628011
## 80 106 0.0055982195 -0.15608154 0.2628011
## 901 27 0.0057072186 -0.30747630 0.2628011
## 366 1023 0.0060404237 -0.05196186 0.2628011SessionInfo
sessionInfo()## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] mitch_1.8.0
## [2] IlluminaHumanMethylationEPICmanifest_0.3.0
## [3] ENmix_1.32.0
## [4] doParallel_1.0.17
## [5] qqman_0.1.8
## [6] RCircos_1.2.2
## [7] beeswarm_0.4.0
## [8] forestplot_2.0.1
## [9] checkmate_2.1.0
## [10] magrittr_2.0.3
## [11] reshape2_1.4.4
## [12] gplots_3.1.3
## [13] eulerr_6.1.1
## [14] GEOquery_2.64.2
## [15] RColorBrewer_1.1-3
## [16] IlluminaHumanMethylation450kmanifest_0.4.0
## [17] topconfects_1.12.0
## [18] DMRcatedata_2.14.0
## [19] ExperimentHub_2.4.0
## [20] AnnotationHub_3.4.0
## [21] BiocFileCache_2.4.0
## [22] dbplyr_2.1.1
## [23] DMRcate_2.10.0
## [24] limma_3.52.1
## [25] missMethyl_1.30.0
## [26] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [27] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [28] minfi_1.42.0
## [29] bumphunter_1.38.0
## [30] locfit_1.5-9.5
## [31] iterators_1.0.14
## [32] foreach_1.5.2
## [33] Biostrings_2.64.0
## [34] XVector_0.36.0
## [35] SummarizedExperiment_1.26.1
## [36] Biobase_2.56.0
## [37] MatrixGenerics_1.8.0
## [38] matrixStats_0.62.0
## [39] GenomicRanges_1.48.0
## [40] GenomeInfoDb_1.32.2
## [41] IRanges_2.30.0
## [42] S4Vectors_0.34.0
## [43] BiocGenerics_0.42.0
## [44] R.utils_2.11.0
## [45] R.oo_1.24.0
## [46] R.methodsS3_1.8.1
## [47] plyr_1.8.7
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 rtracklayer_1.56.0
## [3] GGally_2.1.2 tidyr_1.2.0
## [5] ggplot2_3.3.6 bit64_4.0.5
## [7] knitr_1.39 DelayedArray_0.22.0
## [9] data.table_1.14.2 rpart_4.1.16
## [11] KEGGREST_1.36.0 RCurl_1.98-1.6
## [13] AnnotationFilter_1.20.0 generics_0.1.2
## [15] GenomicFeatures_1.48.1 preprocessCore_1.58.0
## [17] RSQLite_2.2.14 bit_4.0.4
## [19] tzdb_0.3.0 xml2_1.3.3
## [21] httpuv_1.6.5 assertthat_0.2.1
## [23] xfun_0.31 hms_1.1.1
## [25] jquerylib_0.1.4 evaluate_0.15
## [27] promises_1.2.0.1 fansi_1.0.3
## [29] restfulr_0.0.13 scrime_1.3.5
## [31] progress_1.2.2 caTools_1.18.2
## [33] readxl_1.4.0 DBI_1.1.2
## [35] geneplotter_1.74.0 htmlwidgets_1.5.4
## [37] reshape_0.8.9 purrr_0.3.4
## [39] ellipsis_0.3.2 dplyr_1.0.9
## [41] backports_1.4.1 permute_0.9-7
## [43] calibrate_1.7.7 annotate_1.74.0
## [45] biomaRt_2.52.0 sparseMatrixStats_1.8.0
## [47] vctrs_0.4.1 ensembldb_2.20.1
## [49] cachem_1.0.6 withr_2.5.0
## [51] Gviz_1.40.1 BSgenome_1.64.0
## [53] GenomicAlignments_1.32.0 prettyunits_1.1.1
## [55] mclust_5.4.9 cluster_2.1.3
## [57] RPMM_1.25 lazyeval_0.2.2
## [59] crayon_1.5.1 genefilter_1.78.0
## [61] edgeR_3.38.1 pkgconfig_2.0.3
## [63] nlme_3.1-157 ProtGenerics_1.28.0
## [65] nnet_7.3-17 rlang_1.0.2
## [67] lifecycle_1.0.1 filelock_1.0.2
## [69] dichromat_2.0-0.1 polyclip_1.10-0
## [71] cellranger_1.1.0 rngtools_1.5.2
## [73] base64_2.0 Matrix_1.4-1
## [75] Rhdf5lib_1.18.2 base64enc_0.1-3
## [77] png_0.1-7 rjson_0.2.21
## [79] bitops_1.0-7 KernSmooth_2.23-20
## [81] rhdf5filters_1.8.0 blob_1.2.3
## [83] DelayedMatrixStats_1.18.0 doRNG_1.8.2
## [85] stringr_1.4.0 nor1mix_1.3-0
## [87] readr_2.1.2 jpeg_0.1-9
## [89] scales_1.2.0 memoise_2.0.1
## [91] zlibbioc_1.42.0 compiler_4.2.0
## [93] BiocIO_1.6.0 illuminaio_0.38.0
## [95] Rsamtools_2.12.0 cli_3.3.0
## [97] DSS_2.44.0 htmlTable_2.4.0
## [99] Formula_1.2-4 MASS_7.3-57
## [101] tidyselect_1.1.2 stringi_1.7.6
## [103] highr_0.9 yaml_2.3.5
## [105] askpass_1.1 latticeExtra_0.6-29
## [107] sass_0.4.1 VariantAnnotation_1.42.1
## [109] tools_4.2.0 rstudioapi_0.13
## [111] foreign_0.8-82 bsseq_1.32.0
## [113] gridExtra_2.3 digest_0.6.29
## [115] BiocManager_1.30.17 shiny_1.7.1
## [117] quadprog_1.5-8 Rcpp_1.0.8.3
## [119] siggenes_1.70.0 BiocVersion_3.15.2
## [121] later_1.3.0 org.Hs.eg.db_3.15.0
## [123] httr_1.4.3 AnnotationDbi_1.58.0
## [125] biovizBase_1.44.0 colorspace_2.0-3
## [127] polylabelr_0.2.0 XML_3.99-0.9
## [129] splines_4.2.0 statmod_1.4.36
## [131] multtest_2.52.0 xtable_1.8-4
## [133] jsonlite_1.8.0 dynamicTreeCut_1.63-1
## [135] R6_2.5.1 echarts4r_0.4.3
## [137] Hmisc_4.7-0 pillar_1.7.0
## [139] htmltools_0.5.2 mime_0.12
## [141] glue_1.6.2 fastmap_1.1.0
## [143] BiocParallel_1.30.2 interactiveDisplayBase_1.34.0
## [145] beanplot_1.3.1 codetools_0.2-18
## [147] utf8_1.2.2 lattice_0.20-45
## [149] bslib_0.3.1 tibble_3.1.7
## [151] curl_4.3.2 gtools_3.9.2
## [153] openssl_2.0.1 survival_3.3-1
## [155] rmarkdown_2.14 munsell_0.5.0
## [157] rhdf5_2.40.0 GenomeInfoDbData_1.2.8
## [159] HDF5Array_1.24.0 impute_1.70.0
## [161] gtable_0.3.0