Load packages

knitr::opts_chunk$set(dev = "png")

suppressPackageStartupMessages({
    library("missMethyl")
    library("GEOquery")
    library("limma")
    library("topconfects")
    library("minfi")
    library("IlluminaHumanMethylation450kmanifest")
    library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
    library("DMRcate")
    library("mitch")

    library("kableExtra")
    library("forestplot")
    library("RColorBrewer")
    library("plyr")
    library("R.utils")
    library("eulerr")
    library("gplots")
    library("reshape2")
    library("beeswarm")
  })
## Warning: package 'locfit' was built under R version 4.1.0
## Warning: replacing previous import 'minfi::getMeth' by 'bsseq::getMeth' when
## loading 'DMRcate'
# Annotation
ann450k <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
myann <- data.frame(ann450k[,c("UCSC_RefGene_Name","Regulatory_Feature_Group")])
promoters <- grep("Prom",myann$Regulatory_Feature_Group)

Loading functions

These functions provide shortcuts to help with charts and other analysis. They will eventually be shoved into another Rscript or package but can stay here for now.

source("https://raw.githubusercontent.com/markziemann/ART_methylation/master/meth_functions.R")
myranks<-function(x) {
  x$score <- sign(x$logFC)/log10(x$adj.P.Val)
  y <- x[,"score",drop=FALSE]
  y$rn <- x$Row.names
  return(y)
}

# heatmap for continuous data
heatmap_c<-function(dm,name,mx,n, groups) {
  my_palette <- colorRampPalette(c("blue", "white", "red"))(25)
  topgenes <-  rownames(head(dm[order(dm$P.Value),],n))
  ss <- mx[which(rownames(mx) %in% topgenes),]
  mycols <- colorRampPalette(c("white","yellow", "orange", "red","darkred"))(n = length(groups))
  colCols <- mycols[order(groups)]
  heatmap.2(ss,scale="row",margin=c(10, 10),cexRow=0.6,trace="none",cexCol=0.4,
  ColSideColors=colCols ,  col=my_palette, main=name)
}

# heatmap for continuous data - topconfects
make_heatmap2_c <- function(confects,name,mx,n, groups) {
  topgenes <-  head(confects$table$name,n)
  my_palette <- colorRampPalette(c("blue", "white", "red"))(25)
  ss <- mx[which(rownames(mx) %in% topgenes),]
  mycols <- colorRampPalette(c("white","yellow", "orange", "red","darkred"))(n = length(groups))
  colCols <- mycols[order(groups)]
  heatmap.2(ss,scale="row",margin=c(10, 10),cexRow=0.6,trace="none",cexCol=0.4,
  ColSideColors=colCols ,  col=my_palette, main=name)  
}

Data import

load("AccesionnumberGSE74548.Rdata")

Enrichment analysis

We will be using the recently published package mitch to perform enrichment analysis, using average promoter methylation change as an indicator of gene activity. Enrichment will be tested with the mitch package.

dma1a (placebo_vs_supplement_followup(sex,age))

dma2ba (placebo_vs_supplement_Baseline(sex,age))

dma3a (hcy_levels(sex,age))

dma4a (folate_levels(sex,age))

dma5a (vitb12 levels(sex,age))

library("mitch")
# gene sets
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")

One dimensional enrichment analysis with REACTOME gene sets using average promoter methylation t-statistic.

placebo_vs_supplement_followup_sex_age_mitch <- run_mitch_1d(dma= dma1a, name="placebo_vs_supplement_followup(sex,age)_mitch")
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
head(placebo_vs_supplement_followup_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
62 Assembly of collagen fibrils and other multimeric structures 10 0.0003459 0.6534943 0.3947064
582 Negative regulation of NMDA receptor-mediated neuronal transmission 11 0.0009702 0.5745218 0.4802622
1119 mRNA 3’-end processing 28 0.0016493 -0.3438167 0.4802622
686 Processing of Capped Intron-Containing Pre-mRNA 175 0.0017206 -0.1380200 0.4802622
689 Processing of Intronless Pre-mRNAs 14 0.0024286 -0.4681452 0.4802622
831 Removal of the Flap Intermediate from the C-strand 15 0.0027331 -0.4469522 0.4802622
153 Condensation of Prometaphase Chromosomes 10 0.0039265 -0.5267674 0.4802622
61 Assembly and cell surface presentation of NMDA receptors 12 0.0039475 0.4806398 0.4802622
832 Reproduction 43 0.0054452 -0.2452345 0.4802622
84 Butyrate Response Factor 1 (BRF1) binds and destabilizes mRNA 14 0.0054848 -0.4287889 0.4802622
962 Signaling by WNT in cancer 23 0.0055028 0.3345609 0.4802622
824 Regulation of mRNA stability by proteins that bind AU-rich elements 73 0.0055057 -0.1882597 0.4802622
1091 Tristetraprolin (TTP, ZFP36) binds and destabilizes mRNA 14 0.0056113 -0.4276437 0.4802622
690 Processive synthesis on the C-strand of the telomere 17 0.0070472 -0.3776241 0.4802622
497 Meiosis 39 0.0072387 -0.2487675 0.4802622
254 ER to Golgi Anterograde Transport 101 0.0078083 -0.1536073 0.4802622
552 Myogenesis 13 0.0084440 -0.4219999 0.4802622
504 Metabolism of RNA 498 0.0085719 -0.0697885 0.4802622
1121 mRNA Splicing 137 0.0091133 -0.1295283 0.4802622
400 IKK complex recruitment mediated by RIP1 17 0.0093644 -0.3641542 0.4802622
114 Cell Cycle 482 0.0095721 -0.0698591 0.4802622
561 NOD1/2 Signaling Pathway 22 0.0100526 -0.3171801 0.4802622
864 SMAD2/SMAD3:SMAD4 heterotrimer regulates transcription 23 0.0102618 -0.3093642 0.4802622
756 RNA Polymerase II Transcription Termination 35 0.0106228 -0.2497632 0.4802622
97 COPII-mediated vesicle transport 47 0.0112911 -0.2138664 0.4802622
1088 Transport to the Golgi and subsequent modification 124 0.0113651 -0.1320677 0.4802622
498 Meiotic recombination 19 0.0118861 -0.3334972 0.4802622
184 DNA Double-Strand Break Repair 106 0.0121241 -0.1414273 0.4802622
1124 mRNA decay by 3’ to 5’ exoribonuclease 13 0.0122065 -0.4015516 0.4802622
463 KSRP (KHSRP) binds and destabilizes mRNA 14 0.0131439 -0.3829093 0.4903408
981 Synaptic adhesion-like molecules 10 0.0133221 0.4520945 0.4903408
20 Aberrant regulation of mitotic cell cycle due to RB1 defects 29 0.0137798 0.2644379 0.4913365
21 Aberrant regulation of mitotic exit in cancer due to RB1 defects 15 0.0158453 0.3598945 0.5380612
1027 Telomere Maintenance 53 0.0170665 -0.1896711 0.5380612
1003 TICAM1, RIP1-mediated IKK complex recruitment 17 0.0170858 -0.3342289 0.5380612
1122 mRNA Splicing - Major Pathway 129 0.0171699 -0.1219346 0.5380612
633 PI3K/AKT Signaling in Cancer 50 0.0174481 -0.1945819 0.5380612
275 Extension of Telomeres 41 0.0183704 -0.2130597 0.5515947
407 Inactivation of APC/C via direct inhibition of the APC/C complex 16 0.0198398 0.3364838 0.5654945
416 Inhibition of the proteolytic activity of APC/C required for the onset of anaphase by mitotic spindle checkpoint components 16 0.0198398 0.3364838 0.5654945
530 Mitochondrial calcium ion transport 13 0.0203201 0.3718001 0.5654945
155 Constitutive Signaling by AKT1 E17K in Cancer 23 0.0211349 -0.2778793 0.5741641
396 Homology Directed Repair 84 0.0225500 -0.1443025 0.5983622
1025 Telomere C-strand (Lagging Strand) Synthesis 27 0.0249657 -0.2494449 0.6040367
795 Regulation of HSF1-mediated heat shock response 62 0.0250091 -0.1648559 0.6040367
616 Nucleotide-binding domain, leucine rich repeat containing receptor (NLR) signaling pathways 37 0.0253776 -0.2125932 0.6040367
910 Signaling by Erythropoietin 19 0.0254756 0.2962009 0.6040367
427 Integrin signaling 16 0.0261639 -0.3212346 0.6040367
662 Platelet Aggregation (Plug Formation) 16 0.0261639 -0.3212346 0.6040367
998 TBC/RABGAPs 39 0.0264696 -0.2055769 0.6040367
placebo_vs_supplement_Baseline_sex_age_mitch <- run_mitch_1d(dma= dma2a, name="placebo_vs_supplement_Baseline_(sex,age)_mitch")
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
head(placebo_vs_supplement_Baseline_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
769 RORA activates gene expression 10 0.0011338 0.5945025 0.4356347
336 GPVI-mediated activation cascade 23 0.0011549 0.3916311 0.4356347
73 BMAL1:CLOCK,NPAS2 activates circadian gene expression 14 0.0012542 0.4981083 0.4356347
390 Heme signaling 25 0.0015272 0.3663958 0.4356347
140 Circadian Clock 50 0.0022265 0.2503106 0.4555077
910 Signaling by Erythropoietin 19 0.0046361 0.3753460 0.4555077
710 RAF activation 27 0.0059071 0.3062762 0.4555077
1135 rRNA processing in the nucleus and cytosol 145 0.0073218 -0.1295323 0.4555077
1134 rRNA processing 149 0.0077501 -0.1268995 0.4555077
592 Neurotransmitter receptors and postsynaptic signal transmission 73 0.0080317 0.1797770 0.4555077
114 Cell Cycle 482 0.0081778 -0.0713072 0.4555077
812 Regulation of TP53 Activity through Phosphorylation 74 0.0088900 -0.1762455 0.4555077
1103 Vasopressin regulates renal water homeostasis via Aquaporins 18 0.0093115 0.3541772 0.4555077
504 Metabolism of RNA 498 0.0096577 -0.0687051 0.4555077
98 CREB1 phosphorylation through NMDA receptor-mediated activation of RAS signaling 15 0.0097217 0.3857114 0.4555077
32 Activation of NMDA receptors and postsynaptic events 43 0.0103306 0.2262815 0.4555077
639 PKA-mediated phosphorylation of CREB 11 0.0105203 0.4455634 0.4555077
324 G1/S DNA Damage Checkpoints 57 0.0114586 -0.1939051 0.4555077
381 HIV Transcription Initiation 37 0.0114733 -0.2403844 0.4555077
748 RNA Polymerase II HIV Promoter Escape 37 0.0114733 -0.2403844 0.4555077
750 RNA Polymerase II Promoter Escape 37 0.0114733 -0.2403844 0.4555077
753 RNA Polymerase II Transcription Initiation 37 0.0114733 -0.2403844 0.4555077
754 RNA Polymerase II Transcription Initiation And Promoter Clearance 37 0.0114733 -0.2403844 0.4555077
755 RNA Polymerase II Transcription Pre-Initiation And Promoter Opening 37 0.0114733 -0.2403844 0.4555077
811 Regulation of TP53 Activity through Methylation 16 0.0114831 -0.3651172 0.4555077
483 M Phase 272 0.0117768 -0.0894121 0.4555077
1127 p53-Dependent G1 DNA Damage Response 55 0.0128204 -0.1942828 0.4555077
1128 p53-Dependent G1/S DNA damage checkpoint 55 0.0128204 -0.1942828 0.4555077
57 Aquaporin-mediated transport 19 0.0128780 0.3297355 0.4555077
499 Meiotic synapsis 22 0.0130023 -0.3060554 0.4555077
768 RND3 GTPase cycle 28 0.0134530 -0.2700437 0.4555077
1030 The NLRP3 inflammasome 12 0.0137038 -0.4110615 0.4555077
97 COPII-mediated vesicle transport 47 0.0142987 -0.2067872 0.4555077
329 G2/M Transition 143 0.0145713 -0.1188035 0.4555077
1074 Translesion synthesis by Y family DNA polymerases bypasses lesions on DNA template 34 0.0148485 -0.2416161 0.4555077
766 RND1 GTPase cycle 29 0.0151270 -0.2608284 0.4555077
79 Bile acid and bile salt metabolism 14 0.0156561 0.3731826 0.4555077
349 Glucagon signaling in metabolic regulation 16 0.0162503 0.3471532 0.4555077
894 Signal Transduction 1263 0.0163476 0.0417704 0.4555077
303 Formation of TC-NER Pre-Incision Complex 45 0.0164619 -0.2068992 0.4555077
497 Meiosis 39 0.0166915 -0.2217075 0.4555077
116 Cell Cycle, Mitotic 390 0.0169410 -0.0712338 0.4555077
495 Major pathway of rRNA processing in the nucleolus and cytosol 140 0.0176070 -0.1166571 0.4555077
1111 Vpu mediated degradation of CD4 43 0.0178434 -0.2090183 0.4555077
723 RHO GTPases activate PKNs 26 0.0180000 0.2682031 0.4555077
951 Signaling by ROBO receptors 146 0.0186913 -0.1132043 0.4555077
992 Synthesis of bile acids and bile salts 13 0.0187632 0.3765765 0.4555077
541 Mitotic G2-G2/M phases 145 0.0203578 -0.1120456 0.4810867
672 Post NMDA receptor activation events 37 0.0206602 0.2200592 0.4810867
638 PKA activation 10 0.0216398 0.4195152 0.4844200
hcy_levels_sex_age_mitch <- run_mitch_1d(dma= dma3a, name="hcy_levels(sex,age)_mitch")
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
head(hcy_levels_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
114 Cell Cycle 482 0.0000307 0.1123523 0.0350588
1076 Transmission across Chemical Synapses 93 0.0001346 -0.2295945 0.0533443
591 Neuronal System 127 0.0001622 -0.1944369 0.0533443
593 Neurotransmitter release cycle 18 0.0001870 -0.5087470 0.0533443
116 Cell Cycle, Mitotic 390 0.0002692 0.1086585 0.0547750
504 Metabolism of RNA 498 0.0003756 0.0944120 0.0547750
171 Cytokine Signaling in Immune system 411 0.0004086 0.1028010 0.0547750
411 Infectious disease 507 0.0004106 0.0929967 0.0547750
405 Immune System 1091 0.0004321 0.0652212 0.0547750
483 M Phase 272 0.0011051 0.1157945 0.1260869
855 SARS-CoV Infections 109 0.0012608 0.1792976 0.1307817
427 Integrin signaling 16 0.0016644 0.4541637 0.1320768
662 Platelet Aggregation (Plug Formation) 16 0.0016644 0.4541637 0.1320768
58 Arachidonic acid metabolism 21 0.0016686 -0.3964353 0.1320768
32 Activation of NMDA receptors and postsynaptic events 43 0.0017363 -0.2763243 0.1320768
496 Maturation of nucleoprotein 10 0.0025905 0.5502384 0.1648188
792 Regulation of FZD by ubiquitination 12 0.0026060 -0.5020459 0.1648188
136 Chromatin modifying enzymes 137 0.0027446 0.1487530 0.1648188
137 Chromatin organization 137 0.0027446 0.1487530 0.1648188
329 G2/M Transition 143 0.0030462 0.1440907 0.1737864
369 HATs acetylate histones 53 0.0040229 0.2287063 0.2185770
354 Glutamate Neurotransmitter Release Cycle 11 0.0042456 -0.4979804 0.2185886
541 Mitotic G2-G2/M phases 145 0.0044063 0.1375310 0.2185886
129 Cellular responses to stress 425 0.0051997 0.0799945 0.2472028
756 RNA Polymerase II Transcription Termination 35 0.0057747 0.2698393 0.2635583
128 Cellular responses to external stimuli 430 0.0061600 0.0779768 0.2703291
592 Neurotransmitter receptors and postsynaptic signal transmission 73 0.0073119 -0.1819166 0.3036982
1063 Transcriptional regulation of granulopoiesis 24 0.0074844 -0.3155578 0.3036982
636 PIP3 activates AKT signaling 172 0.0077558 0.1182396 0.3036982
385 HSP90 chaperone cycle for steroid hormone receptors (SHR) 32 0.0081954 0.2702679 0.3036982
543 Mitotic Prometaphase 146 0.0083241 0.1270156 0.3036982
392 Heparan sulfate/heparin (HS-GAG) metabolism 19 0.0087367 -0.3476356 0.3036982
672 Post NMDA receptor activation events 37 0.0087836 -0.2491698 0.3036982
373 HDACs deacetylate histones 26 0.0102370 0.2911089 0.3280580
231 Dopamine Neurotransmitter Release Cycle 10 0.0104297 -0.4678365 0.3280580
1119 mRNA 3’-end processing 28 0.0106448 0.2790657 0.3280580
639 PKA-mediated phosphorylation of CREB 11 0.0107763 -0.4441063 0.3280580
819 Regulation of expression of SLITs and ROBOs 117 0.0114286 0.1358073 0.3280580
638 PKA activation 10 0.0117196 -0.4603915 0.3280580
687 Processing of Capped Intronless Pre-mRNA 21 0.0118759 0.3172895 0.3280580
686 Processing of Capped Intron-Containing Pre-mRNA 175 0.0119129 0.1107336 0.3280580
985 Synthesis of Leukotrienes (LT) and Eoxins (EX) 10 0.0120758 -0.4584643 0.3280580
976 Stimuli-sensing channels 35 0.0131532 -0.2424068 0.3455051
856 SARS-CoV-1 Infection 41 0.0133236 0.2236219 0.3455051
644 PTEN Regulation 105 0.0137762 0.1395174 0.3493033
371 HCMV Infection 67 0.0146450 0.1727610 0.3632600
927 Signaling by Interleukins 256 0.0170422 0.0872273 0.3923026
214 Disease 900 0.0172435 0.0480752 0.3923026
447 Interleukin-4 and Interleukin-13 signaling 47 0.0172547 0.2010121 0.3923026
222 Diseases of carbohydrate metabolism 22 0.0175578 -0.2926410 0.3923026
folate_levels_sex_age_mitch <- run_mitch_1d(dma= dma4a, name="folate_levels(sex,age)_mitch")
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
head(folate_levels_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
504 Metabolism of RNA 498 0.0000014 0.1282046 0.0015500
542 Mitotic Metaphase and Anaphase 186 0.0000803 0.1684906 0.0361717
539 Mitotic Anaphase 185 0.0001080 0.1658729 0.0361717
1066 Translation 225 0.0001268 0.1491903 0.0361717
564 NOTCH3 Activation and Transmission of Signal to the Nucleus 15 0.0002541 0.5456194 0.0438017
641 PLC beta mediated events 29 0.0002961 -0.3884567 0.0438017
882 Selenocysteine synthesis 65 0.0003846 0.2550419 0.0438017
174 DAG and IP3 signaling 26 0.0003847 -0.4024492 0.0438017
819 Regulation of expression of SLITs and ROBOs 117 0.0004269 0.1891245 0.0438017
922 Signaling by GPCR 195 0.0005234 -0.1448175 0.0438017
649 Peptide chain elongation 64 0.0006067 0.2482024 0.0438017
1134 rRNA processing 149 0.0006227 0.1630403 0.0438017
426 Integrin cell surface interactions 23 0.0006487 -0.4109725 0.0438017
467 L13a-mediated translational silencing of Ceruloplasmin expression 82 0.0006873 0.2172992 0.0438017
535 Mitochondrial translation elongation 81 0.0007401 0.2173183 0.0438017
601 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC) 69 0.0007444 0.2352026 0.0438017
1135 rRNA processing in the nucleus and cytosol 145 0.0007746 0.1623527 0.0438017
337 GTP hydrolysis and joining of the 60S ribosomal subunit 83 0.0007900 0.2135606 0.0438017
951 Signaling by ROBO receptors 146 0.0009514 0.1590537 0.0438017
865 SRP-dependent cotranslational protein targeting to membrane 81 0.0009549 0.2127624 0.0438017
334 GPCR downstream signalling 178 0.0009574 -0.1442089 0.0438017
892 Separation of Sister Chromatids 141 0.0009657 0.1616027 0.0438017
23 Activated NOTCH1 Transmits Signal to the Nucleus 19 0.0009712 0.4372856 0.0438017
495 Major pathway of rRNA processing in the nucleolus and cytosol 140 0.0009727 0.1620707 0.0438017
563 NOTCH2 Activation and Transmission of Signal to the Nucleus 12 0.0010013 0.5486118 0.0438017
881 Selenoamino acid metabolism 73 0.0011005 0.2213148 0.0438017
185 DNA Repair 232 0.0011390 0.1247902 0.0438017
534 Mitochondrial translation 86 0.0011442 0.2033475 0.0438017
1070 Translesion Synthesis by POLH 16 0.0011840 0.4683599 0.0438017
420 Inositol phosphate metabolism 29 0.0011871 -0.3480376 0.0438017
271 Eukaryotic Translation Elongation 67 0.0011901 0.2293684 0.0438017
537 Mitochondrial translation termination 80 0.0012921 0.2085223 0.0438223
894 Signal Transduction 1263 0.0012983 -0.0559473 0.0438223
600 Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC) 81 0.0013489 0.2064458 0.0438223
602 Nonsense-Mediated Decay (NMD) 81 0.0013489 0.2064458 0.0438223
633 PI3K/AKT Signaling in Cancer 50 0.0013963 -0.2615338 0.0438223
273 Eukaryotic Translation Termination 66 0.0014296 0.2273361 0.0438223
102 CaM pathway 20 0.0015756 -0.4083663 0.0438223
103 Calmodulin induced events 20 0.0015756 -0.4083663 0.0438223
277 Extracellular matrix organization 75 0.0016116 -0.2110299 0.0438223
9 APC/C-mediated degradation of cell cycle proteins 73 0.0016229 0.2137411 0.0438223
825 Regulation of mitotic cell cycle 73 0.0016229 0.2137411 0.0438223
536 Mitochondrial translation initiation 81 0.0017065 0.2020489 0.0438223
516 Metabolism of proteins 1217 0.0017562 0.0552935 0.0438223
47 Anti-inflammatory response favouring Leishmania parasite infection 51 0.0017892 -0.2531165 0.0438223
475 Leishmania parasite growth and survival 51 0.0017892 -0.2531165 0.0438223
547 Muscle contraction 64 0.0018661 -0.2252040 0.0438223
100 Ca-dependent events 21 0.0018735 -0.3921415 0.0438223
8 APC-Cdc20 mediated degradation of Nek2A 21 0.0018901 0.3918127 0.0438223
304 Formation of a pool of free 40S subunits 73 0.0019203 0.2103878 0.0438223
vitb12_levels_sex_age_mitch <- run_mitch_1d(dma= dma5a, name="vitb12 levels(sex,age)_mitch")
## Note: When prioritising by significance (ie: small
##             p-values), large effect sizes might be missed.
head(vitb12_levels_sex_age_mitch,50) %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pANOVA s.dist p.adjustANOVA
1134 rRNA processing 149 0.0000233 0.2015577 0.0266050
1135 rRNA processing in the nucleus and cytosol 145 0.0000546 0.1948467 0.0311454
495 Major pathway of rRNA processing in the nucleolus and cytosol 140 0.0001389 0.1872003 0.0528380
504 Metabolism of RNA 498 0.0002441 0.0973755 0.0696268
194 Defective CFTR causes cystic fibrosis 51 0.0012008 0.2624751 0.2187878
774 RUNX1 regulates transcription of genes involved in differentiation of HSCs 55 0.0012204 0.2524409 0.2187878
1066 Translation 225 0.0014064 0.1243209 0.2187878
171 Cytokine Signaling in Immune system 411 0.0015340 -0.0921528 0.2187878
811 Regulation of TP53 Activity through Methylation 16 0.0022985 0.4403481 0.2335804
522 Metalloprotease DUBs 16 0.0024063 0.4383563 0.2335804
193 Dectin-1 mediated noncanonical NF-kB signaling 50 0.0024961 0.2474957 0.2335804
951 Signaling by ROBO receptors 146 0.0027676 0.1440392 0.2335804
560 NIK–>noncanonical NF-kB signaling 49 0.0028198 0.2469305 0.2335804
189 Deactivation of the beta-catenin transactivating complex 27 0.0032455 0.3274703 0.2335804
204 Degradation of the extracellular matrix 26 0.0033008 -0.3330989 0.2335804
337 GTP hydrolysis and joining of the 60S ribosomal subunit 83 0.0035154 0.1857288 0.2335804
819 Regulation of expression of SLITs and ROBOs 117 0.0035735 0.1564299 0.2335804
881 Selenoamino acid metabolism 73 0.0042566 0.1938616 0.2335804
91 CDT1 association with the CDC6:ORC:origin complex 44 0.0043894 0.2484848 0.2335804
63 Assembly of the pre-replicative complex 53 0.0045714 0.2254809 0.2335804
882 Selenocysteine synthesis 65 0.0046133 0.2035213 0.2335804
394 Hh mutants are degraded by ERAD 46 0.0046841 0.2412793 0.2335804
641 PLC beta mediated events 29 0.0048176 -0.3026493 0.2335804
595 Nicotinamide salvaging 10 0.0049132 -0.5137438 0.2335804
304 Formation of a pool of free 40S subunits 73 0.0055667 0.1880164 0.2377549
131 Chaperone Mediated Autophagy 12 0.0058416 0.4596733 0.2377549
405 Immune System 1091 0.0059458 -0.0509788 0.2377549
649 Peptide chain elongation 64 0.0061192 0.1984702 0.2377549
1058 Transcriptional regulation by RUNX1 122 0.0065743 0.1429326 0.2377549
1070 Translesion Synthesis by POLH 16 0.0067669 0.3911753 0.2377549
467 L13a-mediated translational silencing of Ceruloplasmin expression 82 0.0069576 0.1727827 0.2377549
1093 UCH proteinases 69 0.0073759 0.1868737 0.2377549
976 Stimuli-sensing channels 35 0.0074324 0.2616810 0.2377549
472 Late endosomal microautophagy 21 0.0077462 0.3358381 0.2377549
187 DNA Replication Pre-Initiation 66 0.0077959 0.1897193 0.2377549
271 Eukaryotic Translation Elongation 67 0.0079325 0.1878934 0.2377549
199 Degradation of AXIN 46 0.0082584 0.2253584 0.2377549
1095 Ubiquitin Mediated Degradation of Phosphorylated Cdc25A 43 0.0083350 0.2327759 0.2377549
1129 p53-Independent DNA Damage Response 43 0.0083350 0.2327759 0.2377549
1130 p53-Independent G1/S DNA damage checkpoint 43 0.0083350 0.2327759 0.2377549
1033 The role of GTSE1 in G2/M progression after G2 checkpoint 51 0.0087507 0.2124869 0.2435265
894 Signal Transduction 1263 0.0092629 -0.0452683 0.2476525
690 Processive synthesis on the C-strand of the telomere 17 0.0094250 -0.3638437 0.2476525
859 SCF-beta-TrCP mediated degradation of Emi1 46 0.0095501 0.2211270 0.2476525
203 Degradation of beta-catenin by the destruction complex 67 0.0100529 0.1821681 0.2548977
564 NOTCH3 Activation and Transmission of Signal to the Nucleus 15 0.0103480 0.3824910 0.2551152
927 Signaling by Interleukins 256 0.0107838 -0.0932152 0.2551152
539 Mitotic Anaphase 185 0.0112013 0.1086819 0.2551152
47 Anti-inflammatory response favouring Leishmania parasite infection 51 0.0113647 -0.2051654 0.2551152
475 Leishmania parasite growth and survival 51 0.0113647 -0.2051654 0.2551152

Run multi-dimensional enrichment analysis

xl <- list("psf"=dma1a,"psb"=dma2a,"hcy"=dma3a,"folate"=dma4a,"vitb12"= dma5a)
xxl <- lapply(X = xl,run_mitch_rank)  
xxxl <- lapply(xxl,function(xxl) { xxl$genenames <- rownames(xxl) ; xxl} )
xxll <- join_all(xxxl,by="genenames")
rownames(xxll) <- xxll$genenames
xxll$genenames=NULL
colnames(xxll) <- names(xl)
head(xxll) %>% kbl() %>% kable_paper("hover", full_width = F)
psf psb hcy folate vitb12
A1BG -0.5657309 -1.4174791 -0.7558603 0.0645071 0.0892108
AAAS -0.6107363 -0.8349572 0.2709769 0.2348791 0.3931456
AACS -0.3554806 -0.6653036 -0.0617422 0.4633213 0.7147652
AAGAB -1.2687914 -1.5150322 1.3762765 -0.8286218 0.1746070
AAK1 -0.4346004 -1.0208703 -0.2475015 1.5236908 0.8921750
AAMP -1.5657801 -0.8688060 1.7589146 -0.6785549 -0.0254100
capture.output(
        res <- mitch_calc(xxll,genesets = genesets,priority = "significance"),
        file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)
## Note: When prioritising by significance (ie: small 
##             p-values), large effect sizes might be missed.
head(res$enrichment_result,50) %>% kbl() %>% kable_paper("hover", full_width = F) 
set setSize pMANOVA s.psf s.psb s.hcy s.folate s.vitb12 p.psf p.psb p.hcy p.folate p.vitb12 s.dist SD p.adjustMANOVA
504 Metabolism of RNA 498 0.0000000 -0.0697885 -0.0687051 0.0944120 0.1282046 0.0973755 0.0085719 0.0096577 0.0003756 0.0000014 0.0002441 0.2107673 0.0972555 0.0000000
114 Cell Cycle 482 0.0000014 -0.0698591 -0.0713072 0.1123523 0.0557405 0.0101540 0.0095721 0.0081778 0.0000307 0.0387190 0.7065268 0.1606181 0.0798799 0.0007757
116 Cell Cycle, Mitotic 390 0.0000020 -0.0490737 -0.0712338 0.1086585 0.0877162 0.0373771 0.0999738 0.0169410 0.0002692 0.0032742 0.2102537 0.1684646 0.0803219 0.0007757
819 Regulation of expression of SLITs and ROBOs 117 0.0000029 0.0316131 -0.1219789 0.1358073 0.1891245 0.1564299 0.5560760 0.0231048 0.0114286 0.0004269 0.0035735 0.3075065 0.1264760 0.0008228
1134 rRNA processing 149 0.0000043 0.0123926 -0.1268995 0.0626039 0.1630403 0.2015577 0.7948701 0.0077501 0.1890291 0.0006227 0.0000233 0.2956078 0.1302194 0.0008753
483 M Phase 272 0.0000051 -0.0600325 -0.0894121 0.1157945 0.1074240 0.0437637 0.0908399 0.0117768 0.0011051 0.0024750 0.2176937 0.1961173 0.0944708 0.0008753
1135 rRNA processing in the nucleus and cytosol 145 0.0000054 0.0219109 -0.1295323 0.0696364 0.1623527 0.1948467 0.6501424 0.0073218 0.1494244 0.0007746 0.0000546 0.2939925 0.1285029 0.0008753
686 Processing of Capped Intron-Containing Pre-mRNA 175 0.0000072 -0.1380200 -0.0158566 0.1107336 0.1234802 0.0463992 0.0017206 0.7188163 0.0119129 0.0050427 0.2920806 0.2212761 0.1069471 0.0010266
1066 Translation 225 0.0000083 -0.0171916 -0.0613936 0.0858546 0.1491903 0.1243209 0.6588687 0.1148726 0.0274549 0.0001268 0.0014064 0.2216962 0.0913532 0.0010522
32 Activation of NMDA receptors and postsynaptic events 43 0.0000107 0.1916652 0.2262815 -0.2763243 -0.2296518 -0.1577732 0.0298455 0.0103306 0.0017363 0.0092474 0.0737810 0.4918600 0.2397095 0.0012218
542 Mitotic Metaphase and Anaphase 186 0.0000185 -0.0286868 -0.0546685 0.0949508 0.1684906 0.1069017 0.5021271 0.2008816 0.0263043 0.0000803 0.0123702 0.2294434 0.0950945 0.0019208
495 Major pathway of rRNA processing in the nucleolus and cytosol 140 0.0000221 0.0188465 -0.1166571 0.0718544 0.1620707 0.1872003 0.7013929 0.0176070 0.1437443 0.0009727 0.0001389 0.2836159 0.1219954 0.0020450
539 Mitotic Anaphase 185 0.0000233 -0.0294864 -0.0535774 0.0951617 0.1658729 0.1086819 0.4914325 0.2112250 0.0263682 0.0001080 0.0112013 0.2283009 0.0944555 0.0020450
591 Neuronal System 127 0.0000254 0.0918258 0.1029264 -0.1944369 -0.1131847 -0.0491564 0.0749589 0.0459315 0.0001622 0.0281631 0.3404874 0.2684375 0.1292364 0.0020684
1076 Transmission across Chemical Synapses 93 0.0000475 0.0764105 0.1252860 -0.2295945 -0.1158070 -0.0533191 0.2040239 0.0372722 0.0001346 0.0542095 0.3754541 0.3008371 0.1438222 0.0036098
882 Selenocysteine synthesis 65 0.0000722 0.1117126 -0.1438314 0.0982550 0.2550419 0.2035213 0.1200093 0.0453040 0.1714929 0.0003846 0.0046133 0.3863787 0.1534818 0.0049671
649 Peptide chain elongation 64 0.0000776 0.1142242 -0.1075982 0.1252295 0.2482024 0.1984702 0.1146812 0.1372853 0.0837184 0.0006067 0.0061192 0.3759014 0.1363478 0.0049671
951 Signaling by ROBO receptors 146 0.0000784 0.0126730 -0.1132043 0.0852251 0.1590537 0.1440392 0.7923784 0.0186913 0.0766766 0.0009514 0.0027676 0.2574578 0.1114902 0.0049671
592 Neurotransmitter receptors and postsynaptic signal transmission 73 0.0000870 0.0835091 0.1797770 -0.1819166 -0.1807279 -0.1231870 0.2182729 0.0080317 0.0073119 0.0077043 0.0693446 0.3467345 0.1660719 0.0050750
600 Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC) 81 0.0000957 0.0980566 -0.0574147 0.1481809 0.2064458 0.1299576 0.1279686 0.3728050 0.0214238 0.0013489 0.0436475 0.3072100 0.0990018 0.0050750
602 Nonsense-Mediated Decay (NMD) 81 0.0000957 0.0980566 -0.0574147 0.1481809 0.2064458 0.1299576 0.1279686 0.3728050 0.0214238 0.0013489 0.0436475 0.3072100 0.0990018 0.0050750
329 G2/M Transition 143 0.0001096 -0.0525791 -0.1188035 0.1440907 0.1139516 0.0882287 0.2796919 0.0145713 0.0030462 0.0191258 0.0696695 0.2416821 0.1143384 0.0050750
271 Eukaryotic Translation Elongation 67 0.0001102 0.1183645 -0.0909257 0.1282772 0.2293684 0.1878934 0.0944637 0.1989314 0.0699274 0.0011901 0.0079325 0.3558742 0.1234765 0.0050750
601 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC) 69 0.0001129 0.1156610 -0.0747442 0.1337445 0.2352026 0.1642828 0.0972871 0.2839399 0.0551815 0.0007444 0.0185041 0.3451968 0.1153602 0.0050750
1121 mRNA Splicing 137 0.0001133 -0.1295283 -0.0045752 0.1098446 0.1325899 0.0406440 0.0091133 0.9266223 0.0270098 0.0075979 0.4132702 0.2193088 0.1044721 0.0050750
881 Selenoamino acid metabolism 73 0.0001201 0.1113734 -0.1332414 0.0921539 0.2213148 0.1938616 0.1005956 0.0494802 0.1742760 0.0011005 0.0042566 0.3538534 0.1397118 0.0050750
337 GTP hydrolysis and joining of the 60S ribosomal subunit 83 0.0001201 0.0861890 -0.1002613 0.1044792 0.2135606 0.1857288 0.1756726 0.1151806 0.1006706 0.0007900 0.0035154 0.3293935 0.1230240 0.0050750
541 Mitotic G2-G2/M phases 145 0.0001412 -0.0505255 -0.1120456 0.1375310 0.1147165 0.0970070 0.2955979 0.0203578 0.0044063 0.0175490 0.0446141 0.2378909 0.1113799 0.0055117
467 L13a-mediated translational silencing of Ceruloplasmin expression 82 0.0001432 0.0855665 -0.0929431 0.1111155 0.2172992 0.1727827 0.1814311 0.1466183 0.0826635 0.0006873 0.0069576 0.3246221 0.1189619 0.0055117
1107 Viral mRNA Translation 65 0.0001449 0.1103411 -0.1081976 0.1425101 0.2192386 0.1654113 0.1246255 0.1321162 0.0473197 0.0022752 0.0213216 0.3458576 0.1260804 0.0055117
672 Post NMDA receptor activation events 37 0.0001830 0.1546247 0.2200592 -0.2491698 -0.2340015 -0.1864568 0.1039669 0.0206602 0.0087836 0.0138628 0.0499139 0.4732265 0.2272360 0.0067346
1122 mRNA Splicing - Major Pathway 129 0.0001899 -0.1219346 -0.0130350 0.1209207 0.1369307 0.0405473 0.0171699 0.7989424 0.0181166 0.0074456 0.4281623 0.2237273 0.1057262 0.0067694
865 SRP-dependent cotranslational protein targeting to membrane 81 0.0001985 0.0317143 -0.1410293 0.1220391 0.2127624 0.1384361 0.6225179 0.0285694 0.0581573 0.0009549 0.0316257 0.3165770 0.1357692 0.0068629
304 Formation of a pool of free 40S subunits 73 0.0002125 0.0769394 -0.1186926 0.1178843 0.2103878 0.1880164 0.2566796 0.0801364 0.0822149 0.0019203 0.0055667 0.3369236 0.1308441 0.0071312
639 PKA-mediated phosphorylation of CREB 11 0.0003012 0.0031539 0.4455634 -0.4441063 -0.4237629 -0.2177834 0.9855549 0.0105203 0.0107763 0.0149730 0.2112030 0.7891588 0.3679791 0.0098176
105 Cap-dependent Translation Initiation 90 0.0003689 0.0643874 -0.0735431 0.1325721 0.1773119 0.1414869 0.2923162 0.2290518 0.0301276 0.0037261 0.0206533 0.2803350 0.0993439 0.0113771
272 Eukaryotic Translation Initiation 90 0.0003689 0.0643874 -0.0735431 0.1325721 0.1773119 0.1414869 0.2923162 0.2290518 0.0301276 0.0037261 0.0206533 0.2803350 0.0993439 0.0113771
273 Eukaryotic Translation Termination 66 0.0004031 0.0977808 -0.0869678 0.1244672 0.2273361 0.1747084 0.1703184 0.2226400 0.0809056 0.0014296 0.0142779 0.3388529 0.1194547 0.0121040
842 Response of EIF2AK4 (GCN2) to amino acid deficiency 72 0.0004943 0.1034869 -0.1205556 0.1128237 0.1945692 0.1511999 0.1297019 0.0775236 0.0985330 0.0043809 0.0268254 0.3141512 0.1221702 0.0144602
811 Regulation of TP53 Activity through Methylation 16 0.0005365 -0.2614305 -0.3651172 -0.0382878 0.3997767 0.4403481 0.0703430 0.0114831 0.7909959 0.0056458 0.0022985 0.7462241 0.3710475 0.0153047
129 Cellular responses to stress 425 0.0005735 0.0110043 0.0042310 0.0799945 0.0781623 0.0398834 0.7007328 0.8825244 0.0051997 0.0063264 0.1636160 0.1193237 0.0358499 0.0158342
516 Metabolism of proteins 1217 0.0005829 -0.0185399 -0.0151510 0.0377194 0.0552935 0.0352059 0.2942698 0.3914006 0.0328455 0.0017562 0.0464001 0.0793276 0.0335625 0.0158342
641 PLC beta mediated events 29 0.0006067 -0.0895010 0.0573311 -0.1511775 -0.3884567 -0.3026493 0.4045677 0.5934116 0.1591582 0.0002961 0.0048176 0.5259725 0.1758646 0.0160984
892 Separation of Sister Chromatids 141 0.0006322 -0.0593520 -0.0727709 0.0795204 0.1616027 0.1047995 0.2255775 0.1373213 0.1044461 0.0009657 0.0323603 0.2285609 0.1038003 0.0163952
582 Negative regulation of NMDA receptor-mediated neuronal transmission 11 0.0007500 0.5745218 0.1932348 -0.4029953 -0.1861340 -0.2347148 0.0009702 0.2672900 0.0206808 0.2852689 0.1778278 0.7871195 0.3933599 0.0190173
100 Ca-dependent events 21 0.0009395 0.0022533 0.2836061 -0.1912015 -0.3921415 -0.2983734 0.9857476 0.0245341 0.1295458 0.0018735 0.0179921 0.5998309 0.2686950 0.0233031
638 PKA activation 10 0.0010733 0.0035906 0.4195152 -0.4603915 -0.3905873 -0.2346688 0.9843192 0.0216398 0.0117196 0.0324983 0.1989413 0.7717474 0.3563011 0.0258752
392 Heparan sulfate/heparin (HS-GAG) metabolism 19 0.0010885 0.0622388 -0.1690195 -0.3476356 -0.3031258 -0.1695859 0.6387955 0.2024044 0.0087367 0.0222333 0.2008959 0.5233894 0.1597053 0.0258752
922 Signaling by GPCR 195 0.0011797 0.0171414 0.0709382 -0.0533887 -0.1448175 -0.0975409 0.6814921 0.0893869 0.2011190 0.0005234 0.0195016 0.1966284 0.0866568 0.0274705
194 Defective CFTR causes cystic fibrosis 51 0.0012240 -0.0864399 -0.1616559 0.0427227 0.2374151 0.2624751 0.2862738 0.0461141 0.5981799 0.0033969 0.0012008 0.4008601 0.1893018 0.0278252
unlink("multi_mitch.pdf")
     capture.output(
        mitch_plots(res,outfile="multi_mitch.pdf")
        , file = "/dev/null", append = FALSE,
        type = c("output", "message"), split = FALSE)
## ```

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