Here I'm using the new mitch package to compare methylation and RNA expression data from the RELIEF patients.
Mitch: https://doi.org/10.1186/s12864-020-06856-9
The contrasts I'm looking at are:
Base-line versus post-op for all patients.
Low versus high CRP groups in post-op samples.
Low versus high CRP groups in base-line samples.
suppressPackageStartupMessages({
library("mitch")
})Need to import the data and merge the RNA and array data. I notices that only 11370 genes had promoter methylation values, while 21907 had RNA expression values. We might need to look at updating the array annotation to include more genes as the array annotation is from ~2014 and might be missing a lot of ncRNA genes.
# t0_v_pod_rna.tsv t0vPod_meth.tsv t0_v_pod
x <- read.table("t0_v_pod_rna.tsv",header=TRUE, sep="\t")
x$gene <- sapply(strsplit(rownames(x)," "),"[[",2)
x <- x[,c(4,ncol(x))]
x <- aggregate(. ~ gene, x, sum)
rownames(x) <- x$gene
x$gene=NULL
y <- read.csv("t0vPod_meth.tsv")
y$genename <- sapply(strsplit(y[,1],"\t"),"[[",1)
y$value <- as.numeric(sapply(strsplit(y[,1],"\t"),"[[",2))
y$t = NULL
row.names(y) <- y$genename
y$t = y$genename = NULL
z <- merge(x,y,by=0)
rownames(z) <- z$Row.names
z$Row.names = NULL
colnames(z) <- c("RNA","meth")
t0_v_pod <- z
# pod_crp_rna.tsv t1_crp_meth.tsv pod_crp
x <- read.table("pod_crp_rna.tsv",header=TRUE, sep="\t")
x$gene <- sapply(strsplit(rownames(x)," "),"[[",2)
x <- x[,c(4,ncol(x))]
x <- aggregate(. ~ gene, x, sum)
rownames(x) <- x$gene
x$gene=NULL
y <- read.csv("t1_crp_meth.tsv")
y$genename <- sapply(strsplit(y[,1],"\t"),"[[",1)
y$value <- as.numeric(sapply(strsplit(y[,1],"\t"),"[[",2))
y$t = NULL
row.names(y) <- y$genename
y$t = y$genename = NULL
z <- merge(x,y,by=0)
rownames(z) <- z$Row.names
z$Row.names = NULL
colnames(z) <- c("RNA","meth")
pod_crp <- z
# t0_crp_rna.tsv t0_crp_meth.tsv t0_crp
x <- read.table("t0_crp_rna.tsv",header=TRUE, sep="\t")
x$gene <- sapply(strsplit(rownames(x)," "),"[[",2)
x <- x[,c(4,ncol(x))]
x <- aggregate(. ~ gene, x, sum)
rownames(x) <- x$gene
x$gene=NULL
y <- read.csv("t0_crp_meth.tsv")
y$genename <- sapply(strsplit(y[,1],"\t"),"[[",1)
y$value <- as.numeric(sapply(strsplit(y[,1],"\t"),"[[",2))
y$t = NULL
row.names(y) <- y$genename
y$t = y$genename = NULL
z <- merge(x,y,by=0)
rownames(z) <- z$Row.names
z$Row.names = NULL
colnames(z) <- c("RNA","meth")
t0_crp <- zdownload.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip")
genesets <- gmt_import("ReactomePathways.gmt")For each of the three contrast indicated above I have set up mitch analyses.
# t0_v_pod
head(t0_v_pod)## RNA meth
## A1BG 2.62454842 -0.45188238
## AAAS -2.57494043 0.06143186
## AACS 0.09686573 0.13997068
## AAGAB 1.08711796 -0.59522248
## AAK1 -2.99514827 -0.32725606
## AAMDC 2.30068224 -0.21261544dim(t0_v_pod)## [1] 9311 2capture.output(
res <- mitch_calc(x=t0_v_pod, genesets=genesets,priority="significance")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(res$enrichment_result,30)## set
## 597 Neutrophil degranulation
## 421 Innate Immune System
## 406 Immune System
## 1080 Vesicle-mediated transport
## 502 Membrane Trafficking
## 873 Signal Transduction
## 925 Signaling by Receptor Tyrosine Kinases
## 215 Disease
## 518 Metabolism of proteins
## 1112 rRNA processing
## 900 Signaling by Interleukins
## 505 Metabolism
## 497 Major pathway of rRNA processing in the nucleolus and cytosol
## 681 Post-translational protein modification
## 1113 rRNA processing in the nucleus and cytosol
## 346 Gene expression (Transcription)
## 391 Hemostasis
## 227 Diseases of signal transduction by growth factor receptors and second messengers
## 348 Generic Transcription Pathway
## 1059 Transport of small molecules
## 669 Platelet activation, signaling and aggregation
## 411 Infectious disease
## 130 Cellular responses to external stimuli
## 62 Asparagine N-linked glycosylation
## 249 EPH-Ephrin signaling
## 740 RNA Polymerase II Transcription
## 506 Metabolism of RNA
## 414 Influenza Viral RNA Transcription and Replication
## 131 Cellular responses to stress
## 512 Metabolism of lipids
## setSize pMANOVA s.RNA s.meth p.RNA p.meth
## 597 306 1.468753e-53 0.52014894 -4.817730e-02 7.523155e-55 1.511729e-01
## 421 617 2.235824e-46 0.34649579 -1.287089e-02 1.523065e-47 5.926374e-01
## 406 1197 1.780152e-29 0.19417700 4.940109e-02 1.210139e-27 5.714622e-03
## 1080 442 1.982334e-20 0.26649538 3.469301e-05 2.249954e-21 9.990164e-01
## 502 436 2.306787e-20 0.26779997 -1.369686e-04 2.607122e-21 9.961417e-01
## 873 1172 3.067540e-18 0.15673073 2.653323e-02 3.163302e-18 1.413313e-01
## 925 284 8.883463e-13 0.25114562 4.172771e-02 4.930813e-13 2.304665e-01
## 215 871 1.148062e-11 0.13088731 5.288251e-02 1.816388e-10 1.005902e-02
## 518 1263 1.284806e-11 0.09782930 6.718852e-02 2.112367e-08 1.200267e-04
## 1112 159 1.713008e-11 -0.28918860 1.713273e-01 3.667009e-10 2.067426e-04
## 900 262 1.718144e-11 0.25172703 1.617150e-02 3.273024e-12 6.549493e-01
## 505 1215 2.399116e-11 0.12397729 -1.526985e-02 2.792774e-12 3.900374e-01
## 497 147 4.736339e-11 -0.28337000 1.931798e-01 3.455492e-09 5.677343e-05
## 681 889 9.726147e-11 0.11964406 5.882713e-02 4.075975e-09 3.857327e-03
## 1113 154 1.179556e-10 -0.27509719 1.795163e-01 4.396930e-09 1.294478e-04
## 346 1007 1.575485e-10 -0.12786171 -8.612401e-03 3.056600e-11 6.548887e-01
## 391 304 1.936176e-10 0.22404358 1.400227e-03 2.707947e-11 9.668301e-01
## 227 260 2.049340e-10 0.24008737 1.287151e-02 3.643800e-11 7.230537e-01
## 348 803 4.393940e-10 -0.12926461 -4.228794e-02 1.279522e-09 4.725765e-02
## 1059 342 1.173140e-09 0.20377140 -2.036188e-02 1.433676e-10 5.221420e-01
## 669 133 1.374361e-08 0.29044446 6.252939e-02 8.194595e-09 2.149918e-01
## 411 486 1.721756e-08 0.11247182 1.051064e-01 2.884260e-05 9.295143e-05
## 130 397 2.577235e-08 0.12168615 1.151699e-01 3.951393e-05 1.002380e-04
## 62 223 2.834605e-08 0.22710577 2.068267e-02 6.329616e-09 5.971898e-01
## 249 55 4.439792e-08 0.44780388 -1.116288e-01 9.499295e-09 1.528481e-01
## 740 895 5.888276e-08 -0.10899537 -3.347946e-02 7.745657e-08 9.910249e-02
## 506 535 6.445924e-08 -0.09204202 1.229236e-01 3.427065e-04 1.724041e-06
## 414 110 7.144080e-08 -0.22515734 2.416496e-01 4.757849e-05 1.268767e-05
## 131 392 7.667985e-08 0.11642938 1.143912e-01 9.274670e-05 1.228631e-04
## 512 427 1.014328e-07 0.16217034 -9.591751e-03 1.397159e-08 7.374058e-01
## s.dist SD p.adjustMANOVA
## 597 0.5223753 0.401867337 1.645003e-50
## 421 0.3467348 0.254110613 1.252062e-43
## 406 0.2003626 0.102372032 6.645900e-27
## 1080 0.2664954 0.188416158 5.167203e-18
## 502 0.2678000 0.189460029 5.167203e-18
## 873 0.1589608 0.092063537 5.726075e-16
## 925 0.2545885 0.148080826 1.421354e-10
## 215 0.1411667 0.055157722 1.598870e-09
## 518 0.1186797 0.021666303 1.598870e-09
## 1112 0.3361296 0.325633915 1.749383e-09
## 900 0.2522459 0.166562912 1.749383e-09
## 505 0.1249141 0.098462598 2.239175e-09
## 497 0.3429533 0.336971570 4.080538e-09
## 681 0.1333242 0.043004067 7.780917e-09
## 1113 0.3284883 0.321460311 8.807354e-09
## 346 0.1281514 0.084321996 1.102839e-08
## 391 0.2240480 0.157432625 1.275145e-08
## 227 0.2404322 0.160665877 1.275145e-08
## 348 0.1360059 0.061501793 2.590112e-08
## 1059 0.2047862 0.158486168 6.569582e-08
## 669 0.2970992 0.161160291 7.329928e-07
## 411 0.1539391 0.005208152 8.765305e-07
## 130 0.1675459 0.004607668 1.255001e-06
## 62 0.2280456 0.145963171 1.322815e-06
## 249 0.4615076 0.395578657 1.989027e-06
## 740 0.1140213 0.053397812 2.536488e-06
## 506 0.1535641 0.152003651 2.673865e-06
## 414 0.3302883 0.330082372 2.857632e-06
## 131 0.1632212 0.001441214 2.961429e-06
## 512 0.1624538 0.121454140 3.786823e-06unlink("t0_v_pod_int.html")
capture.output(
mitch_report(res, "t0_v_pod_int.html")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Dataset saved as " /tmp/RtmpJ0JTVz/./t0_v_pod_int.RData ".##
##
## processing file: mitch.Rmd## output file: /mnt/bfx6/bfx/bain/inflam/integrate/mitch.knit.md##
## Output created: /tmp/RtmpJ0JTVz/mitch_report.html# pod_crp
head(pod_crp)## RNA meth
## A1BG -0.2700849 0.100632259
## AAAS -1.3836098 0.230035602
## AACS -0.3700472 0.682403328
## AAGAB 1.4820262 0.001540913
## AAK1 -1.0782919 0.360863030
## AAMDC 0.4638045 0.383673218dim(pod_crp)## [1] 9286 2capture.output(
res <- mitch_calc(x=pod_crp, 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,30)## set
## 596 Neutrophil degranulation
## 420 Innate Immune System
## 405 Immune System
## 1111 rRNA processing
## 1112 rRNA processing in the nucleus and cytosol
## 496 Major pathway of rRNA processing in the nucleolus and cytosol
## 872 Signal Transduction
## 501 Membrane Trafficking
## 1079 Vesicle-mediated transport
## 270 Eukaryotic Translation Elongation
## 653 Peptide chain elongation
## 302 Formation of a pool of free 40S subunits
## 413 Influenza Viral RNA Transcription and Replication
## 505 Metabolism of RNA
## 864 Selenocysteine synthesis
## 1082 Viral mRNA Translation
## 272 Eukaryotic Translation Termination
## 337 GTP hydrolysis and joining of the 60S ribosomal subunit
## 468 L13a-mediated translational silencing of Ceruloplasmin expression
## 603 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)
## 108 Cap-dependent Translation Initiation
## 271 Eukaryotic Translation Initiation
## 823 Response of EIF2AK4 (GCN2) to amino acid deficiency
## 412 Influenza Infection
## 863 Selenoamino acid metabolism
## 1040 Translation
## 602 Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)
## 604 Nonsense-Mediated Decay (NMD)
## 846 SRP-dependent cotranslational protein targeting to membrane
## 924 Signaling by Receptor Tyrosine Kinases
## setSize pMANOVA s.RNA s.meth p.RNA p.meth
## 596 306 1.487587e-50 0.5045446 -0.0656942807 1.131455e-51 5.031132e-02
## 420 617 2.850417e-45 0.3417930 -0.0001056317 2.740134e-46 9.964969e-01
## 405 1197 2.340569e-36 0.2228507 0.0319867745 6.192932e-36 7.362818e-02
## 1111 159 3.547566e-31 -0.5384983 0.1227452172 1.255146e-31 7.863070e-03
## 1112 154 2.702367e-30 -0.5374109 0.1329846238 1.409868e-30 4.585052e-03
## 496 147 3.990555e-30 -0.5484777 0.1345649541 1.967766e-30 5.053185e-03
## 872 1169 6.626950e-25 0.1769962 0.0560376984 8.882539e-23 1.915513e-03
## 501 436 1.145893e-23 0.2897165 -0.0023314155 1.104852e-24 9.344047e-01
## 1079 442 2.465903e-23 0.2852730 0.0040378118 2.852218e-24 8.859168e-01
## 270 70 2.862458e-22 -0.6701854 0.2052238343 3.061240e-22 3.046100e-03
## 653 67 3.296912e-21 -0.6670730 0.2080275486 3.568129e-21 3.293493e-03
## 302 76 1.028293e-20 -0.6216784 0.1819218241 7.288162e-21 6.221404e-03
## 413 110 2.064205e-20 -0.4946421 0.2148727907 3.544765e-19 1.039331e-04
## 505 535 6.333406e-20 -0.2276202 0.0963754209 7.314170e-19 1.774875e-04
## 864 68 7.850755e-20 -0.6432268 0.1839718964 4.603308e-20 8.841144e-03
## 1082 67 1.488766e-19 -0.6343535 0.2198493378 2.725847e-19 1.896199e-03
## 272 70 7.099956e-19 -0.6217851 0.1581132192 2.365543e-19 2.245165e-02
## 337 86 2.890064e-18 -0.5387816 0.1954170880 6.131585e-18 1.779893e-03
## 468 85 3.105969e-18 -0.5431622 0.1904255931 5.147493e-18 2.467955e-03
## 603 72 3.408472e-18 -0.5892754 0.2057979403 5.470769e-18 2.585238e-03
## 108 93 3.441137e-17 -0.4988379 0.1930840319 1.000789e-16 1.330093e-03
## 271 93 3.441137e-17 -0.4988379 0.1930840319 1.000789e-16 1.330093e-03
## 823 76 4.251706e-17 -0.5623493 0.1670866907 2.418844e-17 1.198122e-02
## 412 128 4.338902e-17 -0.4083673 0.2068683119 1.727649e-15 5.650395e-05
## 863 76 7.449484e-17 -0.5580947 0.1661609235 4.194205e-17 1.246250e-02
## 1040 234 1.062533e-16 -0.3264387 0.0472209947 1.177668e-17 2.167733e-01
## 602 86 1.507942e-15 -0.4833847 0.2151845298 9.965199e-15 5.796399e-04
## 604 86 1.507942e-15 -0.4833847 0.2151845298 9.965199e-15 5.796399e-04
## 846 85 7.973763e-15 -0.4766016 0.2047872034 3.285886e-14 1.131216e-03
## 924 284 8.985486e-15 0.2742998 0.0329426012 2.895360e-15 3.438222e-01
## s.dist SD p.adjustMANOVA
## 596 0.5088035 0.40321978 1.664610e-47
## 420 0.3417930 0.24175882 1.594808e-42
## 405 0.2251346 0.13496116 8.730322e-34
## 1111 0.5523105 0.46756979 9.924316e-29
## 1112 0.5536203 0.47404122 6.047898e-28
## 496 0.5647437 0.48298407 7.442386e-28
## 872 0.1856553 0.08553059 1.059365e-22
## 501 0.2897259 0.20650905 1.602818e-21
## 1079 0.2853016 0.19886334 3.065940e-21
## 270 0.7009032 0.61900780 3.203091e-20
## 653 0.6987574 0.61878955 3.353859e-19
## 302 0.6477496 0.56823115 9.588836e-19
## 413 0.5392969 0.50170283 1.776804e-18
## 505 0.2471825 0.22909953 5.062201e-18
## 864 0.6690190 0.58491781 5.856663e-18
## 1082 0.6713703 0.60401259 1.041206e-17
## 272 0.6415735 0.55147139 4.673442e-17
## 337 0.5731260 0.51915687 1.796657e-16
## 468 0.5755754 0.51872489 1.829252e-16
## 603 0.6241781 0.56220173 1.907040e-16
## 108 0.5349025 0.48926272 1.750287e-15
## 271 0.5349025 0.48926272 1.750287e-15
## 823 0.5866470 0.51578912 2.023013e-15
## 412 0.4577755 0.43503730 2.023013e-15
## 863 0.5823051 0.51212610 3.334389e-15
## 1040 0.3298364 0.26421730 4.572977e-15
## 602 0.5291174 0.49396306 6.026383e-14
## 604 0.5291174 0.49396306 6.026383e-14
## 846 0.5187359 0.48181468 3.076773e-13
## 924 0.2762708 0.17066529 3.351586e-13unlink("pod_crp_int.html")
capture.output(
mitch_report(res, "pod_crp_int.html")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Dataset saved as " /tmp/RtmpJ0JTVz/./pod_crp_int.RData ".##
##
## processing file: mitch.Rmd## output file: /mnt/bfx6/bfx/bain/inflam/integrate/mitch.knit.md##
## Output created: /tmp/RtmpJ0JTVz/mitch_report.html# t0_crp
head(t0_crp)## RNA meth
## A1BG -3.3590111 0.9487855
## AAAS -0.7287326 0.4334437
## AACS -0.6787675 0.8690806
## AAGAB 1.3669141 0.5727567
## AAK1 0.2026914 0.7201373
## AAMDC -1.6832172 0.8935852dim(t0_crp)## [1] 9321 2capture.output(
res <- mitch_calc(x=t0_crp, 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,30)## set
## 271 Eukaryotic Translation Elongation
## 654 Peptide chain elongation
## 303 Formation of a pool of free 40S subunits
## 866 Selenocysteine synthesis
## 273 Eukaryotic Translation Termination
## 1086 Viral mRNA Translation
## 865 Selenoamino acid metabolism
## 469 L13a-mediated translational silencing of Ceruloplasmin expression
## 338 GTP hydrolysis and joining of the 60S ribosomal subunit
## 604 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)
## 108 Cap-dependent Translation Initiation
## 272 Eukaryotic Translation Initiation
## 825 Response of EIF2AK4 (GCN2) to amino acid deficiency
## 848 SRP-dependent cotranslational protein targeting to membrane
## 603 Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)
## 605 Nonsense-Mediated Decay (NMD)
## 1044 Translation
## 414 Influenza Viral RNA Transcription and Replication
## 802 Regulation of expression of SLITs and ROBOs
## 1116 rRNA processing
## 1117 rRNA processing in the nucleus and cytosol
## 497 Major pathway of rRNA processing in the nucleolus and cytosol
## 413 Influenza Infection
## 925 Signaling by ROBO receptors
## 307 Formation of the ternary complex, and subsequently, the 43S complex
## 507 Metabolism of amino acids and derivatives
## 73 Axon guidance
## 39 Activation of the mRNA upon binding of the cap-binding complex and eIFs, and subsequent binding to 43S
## 1045 Translation initiation complex formation
## 590 Nervous system development
## setSize pMANOVA s.RNA s.meth p.RNA p.meth
## 271 70 7.592605e-37 -0.8793644 -0.08917028 3.159748e-37 0.19801642
## 654 67 5.096027e-35 -0.8738391 -0.10158092 2.836024e-35 0.15132710
## 303 76 2.336377e-33 -0.8027241 -0.08163445 8.552038e-34 0.21963374
## 866 68 7.340406e-33 -0.8447022 -0.06500912 1.596409e-33 0.35495549
## 273 70 6.848944e-32 -0.8176382 -0.08075420 2.229928e-32 0.24372635
## 1086 67 1.173370e-31 -0.8327113 -0.08073314 3.649929e-32 0.25413821
## 865 76 2.492653e-30 -0.7639805 -0.08342490 9.514672e-31 0.20968685
## 469 85 3.004403e-29 -0.7129570 -0.05336662 5.845660e-30 0.39632401
## 338 86 6.002228e-28 -0.6936352 -0.04313721 9.452234e-29 0.49044667
## 604 72 1.055212e-27 -0.7507028 -0.07310100 2.904327e-28 0.28457759
## 108 93 2.183839e-26 -0.6479974 -0.04018159 3.373717e-27 0.50430811
## 272 93 2.183839e-26 -0.6479974 -0.04018159 3.373717e-27 0.50430811
## 825 76 3.129958e-26 -0.7116649 -0.06277931 7.073576e-27 0.34518484
## 848 85 6.982717e-22 -0.6073727 -0.09060963 3.753876e-22 0.14981242
## 603 86 1.120157e-21 -0.6070384 -0.05341912 2.273789e-22 0.39312006
## 605 86 1.120157e-21 -0.6070384 -0.05341912 2.273789e-22 0.39312006
## 1044 234 2.809200e-21 -0.3557510 -0.08702580 1.087705e-20 0.02280668
## 414 110 1.097052e-20 -0.5265582 -0.03067281 1.562292e-21 0.57969384
## 802 122 3.082890e-19 -0.4790792 -0.04961872 7.222423e-20 0.34571912
## 1116 159 5.583970e-18 -0.4025720 -0.06146388 2.465499e-18 0.18326224
## 1117 154 6.425347e-18 -0.4087332 -0.05976264 2.565815e-18 0.20274204
## 497 147 1.589518e-16 -0.3993673 -0.06636324 7.768705e-17 0.16682424
## 413 128 4.199904e-15 -0.4144254 -0.02891126 6.576611e-16 0.57372866
## 925 153 2.247445e-14 -0.3708723 -0.01645108 2.966564e-15 0.72671223
## 307 40 1.958488e-11 -0.6259940 -0.10900765 7.364966e-12 0.23348106
## 507 219 5.688460e-11 -0.2652074 -0.04160258 1.759355e-11 0.29204496
## 73 295 1.021488e-10 -0.2288830 -0.02273733 1.978458e-11 0.50570496
## 39 48 2.228794e-10 -0.5468295 -0.07507010 5.688713e-11 0.36896296
## 1045 47 2.643447e-10 -0.5502182 -0.07688849 6.866110e-11 0.36250956
## 590 307 2.650546e-10 -0.2196547 -0.02294874 5.306452e-11 0.49347050
## s.dist SD p.adjustMANOVA
## 271 0.8838739 0.5587516 8.534088e-34
## 654 0.8797236 0.5460690 2.863967e-32
## 303 0.8068644 0.5098874 8.753627e-31
## 866 0.8472001 0.5513263 2.062654e-30
## 273 0.8216164 0.5210557 1.539643e-29
## 1086 0.8366158 0.5317289 2.198113e-29
## 865 0.7685219 0.4812255 4.002489e-28
## 469 0.7149515 0.4664008 4.221186e-27
## 338 0.6949752 0.4599715 7.496115e-26
## 604 0.7542535 0.4791368 1.186059e-25
## 108 0.6492421 0.4297907 2.045529e-24
## 272 0.6492421 0.4297907 2.045529e-24
## 825 0.7144286 0.4588314 2.706210e-24
## 848 0.6140942 0.3654067 5.606124e-20
## 603 0.6093843 0.3914680 7.869102e-20
## 605 0.6093843 0.3914680 7.869102e-20
## 1044 0.3662407 0.1900174 1.857377e-19
## 414 0.5274508 0.3506439 6.850480e-19
## 802 0.4816418 0.3036744 1.823773e-17
## 1116 0.4072371 0.2411999 3.138191e-16
## 1117 0.4130792 0.2467594 3.439091e-16
## 497 0.4048436 0.2354695 8.120994e-15
## 413 0.4154326 0.2725996 2.052475e-13
## 925 0.3712369 0.2506136 1.052553e-12
## 307 0.6354141 0.3655645 8.805364e-10
## 507 0.2684506 0.1581125 2.459165e-09
## 73 0.2300096 0.1457670 4.252416e-09
## 39 0.5519584 0.3335843 8.947017e-09
## 1045 0.5555645 0.3346946 9.930713e-09
## 590 0.2208502 0.1390921 9.930713e-09unlink("t0_crp_int.html")
capture.output(
mitch_report(res, "t0_crp_int.html")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Dataset saved as " /tmp/RtmpJ0JTVz/./t0_crp_int.RData ".##
##
## processing file: mitch.Rmd## output file: /mnt/bfx6/bfx/bain/inflam/integrate/mitch.knit.md##
## Output created: /tmp/RtmpJ0JTVz/mitch_report.htmlsessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pkgload_1.1.0
## [2] GGally_2.0.0
## [3] ggplot2_3.3.2
## [4] reshape2_1.4.4
## [5] gtools_3.8.2
## [6] tibble_3.0.1
## [7] dplyr_1.0.0
## [8] echarts4r_0.3.2
## [9] mitch_1.0.6
## [10] FlowSorted.Blood.EPIC_1.6.1
## [11] ExperimentHub_1.14.0
## [12] AnnotationHub_2.20.0
## [13] BiocFileCache_1.12.0
## [14] dbplyr_1.4.4
## [15] nlme_3.1-148
## [16] quadprog_1.5-8
## [17] genefilter_1.70.0
## [18] topconfects_1.4.0
## [19] gplots_3.0.3
## [20] beeswarm_0.2.3
## [21] IlluminaHumanMethylationEPICmanifest_0.3.0
## [22] IlluminaHumanMethylationEPICanno.ilm10b2.hg19_0.6.0
## [23] limma_3.44.3
## [24] missMethyl_1.22.0
## [25] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
## [26] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0
## [27] minfi_1.34.0
## [28] bumphunter_1.30.0
## [29] locfit_1.5-9.4
## [30] iterators_1.0.12
## [31] foreach_1.5.0
## [32] Biostrings_2.56.0
## [33] XVector_0.28.0
## [34] SummarizedExperiment_1.18.1
## [35] DelayedArray_0.14.0
## [36] matrixStats_0.56.0
## [37] Biobase_2.48.0
## [38] GenomicRanges_1.40.0
## [39] GenomeInfoDb_1.24.2
## [40] IRanges_2.22.2
## [41] S4Vectors_0.26.1
## [42] BiocGenerics_0.34.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.8 plyr_1.8.6
## [3] splines_4.0.2 BiocParallel_1.22.0
## [5] digest_0.6.25 htmltools_0.5.0
## [7] gdata_2.18.0 magrittr_1.5
## [9] memoise_1.1.0 readr_1.3.1
## [11] annotate_1.66.0 askpass_1.1
## [13] siggenes_1.62.0 prettyunits_1.1.1
## [15] colorspace_1.4-1 blob_1.2.1
## [17] rappdirs_0.3.1 xfun_0.15
## [19] jsonlite_1.7.0 crayon_1.3.4
## [21] RCurl_1.98-1.2 GEOquery_2.56.0
## [23] survival_3.2-3 glue_1.4.1
## [25] gtable_0.3.0 zlibbioc_1.34.0
## [27] Rhdf5lib_1.10.0 HDF5Array_1.16.1
## [29] scales_1.1.1 DBI_1.1.0
## [31] rngtools_1.5 Rcpp_1.0.4.6
## [33] xtable_1.8-4 progress_1.2.2
## [35] bit_1.1-15.2 mclust_5.4.6
## [37] preprocessCore_1.50.0 htmlwidgets_1.5.1
## [39] httr_1.4.1 RColorBrewer_1.1-2
## [41] ellipsis_0.3.1 farver_2.0.3
## [43] pkgconfig_2.0.3 reshape_0.8.8
## [45] XML_3.99-0.3 labeling_0.3
## [47] tidyselect_1.1.0 rlang_0.4.6
## [49] later_1.1.0.1 AnnotationDbi_1.50.1
## [51] pbmcapply_1.5.0 munsell_0.5.0
## [53] BiocVersion_3.11.1 tools_4.0.2
## [55] generics_0.0.2 RSQLite_2.2.0
## [57] evaluate_0.14 stringr_1.4.0
## [59] fastmap_1.0.1 yaml_2.2.1
## [61] knitr_1.29 org.Hs.eg.db_3.11.4
## [63] bit64_0.9-7 beanplot_1.2
## [65] caTools_1.18.0 scrime_1.3.5
## [67] purrr_0.3.4 doRNG_1.8.2
## [69] mime_0.9 nor1mix_1.3-0
## [71] xml2_1.3.2 biomaRt_2.44.1
## [73] compiler_4.0.2 curl_4.3
## [75] interactiveDisplayBase_1.26.3 testthat_2.3.2
## [77] statmod_1.4.34 stringi_1.4.6
## [79] highr_0.8 desc_1.2.0
## [81] GenomicFeatures_1.40.0 lattice_0.20-41
## [83] Matrix_1.2-18 multtest_2.44.0
## [85] vctrs_0.3.1 pillar_1.4.4
## [87] lifecycle_0.2.0 BiocManager_1.30.10
## [89] data.table_1.12.8 bitops_1.0-6
## [91] httpuv_1.5.4 rtracklayer_1.48.0
## [93] R6_2.4.1 promises_1.1.1
## [95] gridExtra_2.3 KernSmooth_2.23-17
## [97] codetools_0.2-16 MASS_7.3-51.6
## [99] assertthat_0.2.1 rhdf5_2.32.1
## [101] rprojroot_1.3-2 openssl_1.4.2
## [103] withr_2.2.0 GenomicAlignments_1.24.0
## [105] Rsamtools_2.4.0 GenomeInfoDbData_1.2.3
## [107] hms_0.5.3 grid_4.0.2
## [109] tidyr_1.1.0 base64_2.0
## [111] rmarkdown_2.3 DelayedMatrixStats_1.10.0
## [113] illuminaio_0.30.0 shiny_1.5.0