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$Row.names = NULL
colnames(z) <- c("RNA","meth")
t0_crp <- zdownload.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip")
genesets <- gmt_import("ReactomePathways.gmt")For each of the three contrast indicated above I have set up mitch analyses.
# t0_v_pod
head(t0_v_pod)## RNA meth
## A1BG 2.6192564 -0.45188238
## AAAS -2.6372410 0.06143186
## AACS 0.1010134 0.13997068
## AAGAB 1.0702585 -0.59522248
## AAK1 -3.0047762 -0.32725606
## AAMDC 2.2859640 -0.21261544dim(t0_v_pod)## [1] 9311 2capture.output(
res <- mitch_calc(x=t0_v_pod, genesets=genesets,priority="significance")
, file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.head(res$enrichment_result,30)## set
## 603 Neutrophil degranulation
## 427 Innate Immune System
## 411 Immune System
## 1124 Vesicle-mediated transport
## 508 Membrane Trafficking
## 908 Signal Transduction
## 968 Signaling by Receptor Tyrosine Kinases
## 216 Disease
## 943 Signaling by Interleukins
## 524 Metabolism of proteins
## 1155 rRNA processing
## 511 Metabolism
## 503 Major pathway of rRNA processing in the nucleolus and cytosol
## 687 Post-translational protein modification
## 1156 rRNA processing in the nucleus and cytosol
## 229 Diseases of signal transduction by growth factor receptors and second messengers
## 350 Gene expression (Transcription)
## 396 Hemostasis
## 352 Generic Transcription Pathway
## 1103 Transport of small molecules
## 417 Infectious disease
## 705 Programmed Cell Death
## 127 Cellular response to chemical stress
## 675 Platelet activation, signaling and aggregation
## 131 Cellular responses to external stimuli
## 252 EPH-Ephrin signaling
## 60 Asparagine N-linked glycosylation
## 765 RNA Polymerase II Transcription
## 512 Metabolism of RNA
## 518 Metabolism of lipids
## setSize pMANOVA s.RNA s.meth p.RNA p.meth
## 603 305 5.795072e-54 0.52291915 -4.989606e-02 2.977618e-55 1.377381e-01
## 427 616 1.274048e-46 0.34770281 -1.369349e-02 8.508006e-48 5.695027e-01
## 411 1142 2.008686e-30 0.20194280 4.931333e-02 1.163054e-28 6.855574e-03
## 1124 442 1.925675e-20 0.26657293 3.469301e-05 2.190807e-21 9.990164e-01
## 508 436 2.226129e-20 0.26789766 -1.369686e-04 2.521765e-21 9.961417e-01
## 908 1292 1.724093e-18 0.15115957 2.675418e-02 2.127594e-18 1.221857e-01
## 968 284 6.360102e-13 0.25268951 4.172771e-02 3.548689e-13 2.304665e-01
## 216 930 7.120276e-12 0.12819832 5.203193e-02 1.270201e-10 9.117658e-03
## 943 263 1.384831e-11 0.25250376 1.506036e-02 2.559798e-12 6.767067e-01
## 524 1263 1.630373e-11 0.09904508 6.447656e-02 1.409068e-08 2.236244e-04
## 1155 159 1.709576e-11 -0.28925183 1.713273e-01 3.634821e-10 2.067426e-04
## 511 1216 3.159757e-11 0.12325164 -1.481645e-02 3.673509e-12 4.041018e-01
## 503 147 5.246796e-11 -0.28255196 1.931798e-01 3.832561e-09 5.677343e-05
## 687 890 7.818233e-11 0.12066837 5.808246e-02 2.947190e-09 4.311271e-03
## 1156 154 1.282056e-10 -0.27445046 1.795163e-01 4.778668e-09 1.294478e-04
## 229 265 1.361496e-10 0.23986167 1.409567e-02 2.502505e-11 6.952870e-01
## 350 1006 1.807766e-10 -0.12769308 -7.410324e-03 3.308719e-11 7.006633e-01
## 396 304 1.928866e-10 0.22405600 1.400227e-03 2.701135e-11 9.668301e-01
## 352 803 3.145790e-10 -0.13037323 -4.228794e-02 9.234432e-10 4.725765e-02
## 1103 342 1.212177e-09 0.20361036 -2.036188e-02 1.482241e-10 5.221420e-01
## 417 530 4.837266e-09 0.11609500 9.958465e-02 6.878294e-06 1.146606e-04
## 705 153 1.123935e-08 0.27896464 3.259124e-02 2.990542e-09 4.886865e-01
## 127 116 1.145043e-08 0.32588926 -3.298457e-02 1.480095e-09 5.409360e-01
## 675 133 1.352266e-08 0.29055423 6.252939e-02 8.089221e-09 2.149918e-01
## 131 457 2.726624e-08 0.12084587 9.945980e-02 1.271123e-05 3.282151e-04
## 252 55 3.523329e-08 0.45099788 -1.116288e-01 7.450318e-09 1.528481e-01
## 60 222 3.899765e-08 0.22605189 1.718244e-02 8.015755e-09 6.613486e-01
## 765 895 4.609059e-08 -0.10987186 -3.347946e-02 6.086883e-08 9.910249e-02
## 512 535 6.676233e-08 -0.09179836 1.229236e-01 3.553622e-04 1.724041e-06
## 518 427 7.260730e-08 0.16383847 -9.591751e-03 9.913576e-09 7.374058e-01
## s.dist SD p.adjustMANOVA
## 603 0.5252943 0.40504152 6.733874e-51
## 427 0.3479724 0.25554578 7.402217e-44
## 411 0.2078766 0.10792533 7.780310e-28
## 1124 0.2665729 0.18847099 5.173524e-18
## 508 0.2678977 0.18952910 5.173524e-18
## 908 0.1535090 0.08796789 3.338994e-16
## 968 0.2561117 0.14917251 1.055777e-10
## 216 0.1383551 0.05385777 1.034220e-09
## 943 0.2529525 0.16789783 1.787971e-09
## 524 0.1181827 0.02444363 1.805934e-09
## 1155 0.3361840 0.32567862 1.805934e-09
## 511 0.1241390 0.09762888 3.059698e-09
## 503 0.3422777 0.33639312 4.689828e-09
## 687 0.1339195 0.04425492 6.489134e-09
## 1156 0.3279469 0.32100301 9.887863e-09
## 229 0.2402755 0.15964067 9.887863e-09
## 350 0.1279079 0.08505275 1.235661e-08
## 396 0.2240604 0.15744141 1.245190e-08
## 352 0.1370600 0.06228571 1.923899e-08
## 1103 0.2046260 0.15837229 7.042749e-08
## 417 0.1529547 0.01167458 2.676621e-07
## 705 0.2808620 0.17421230 5.784959e-07
## 127 0.3275543 0.25376212 5.784959e-07
## 675 0.2972065 0.16123791 6.547221e-07
## 131 0.1565119 0.01512224 1.267335e-06
## 252 0.4646074 0.39783715 1.574657e-06
## 60 0.2267040 0.14769300 1.678343e-06
## 765 0.1148595 0.05401758 1.912760e-06
## 512 0.1534182 0.15183136 2.675098e-06
## 518 0.1641190 0.12263369 2.812323e-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/RtmpbT00es/t0_v_pod_int.rds ".##
##
## processing file: mitch.Rmd## output file: /mnt/bfx6/bfx/bain/inflam/integrate/mitch.knit.md##
## Output created: /tmp/RtmpbT00es/mitch_report.html# pod_crp
head(pod_crp)## RNA meth
## A1BG -0.30654761 0.100632259
## AAAS -0.72511657 0.230035602
## AACS 0.09916863 0.682403328
## AAGAB 0.67982671 0.001540913
## AAK1 -0.73201617 0.360863030
## AAMDC -0.04498728 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
## 602 Neutrophil degranulation
## 426 Innate Immune System
## 1154 rRNA processing
## 1155 rRNA processing in the nucleus and cytosol
## 502 Major pathway of rRNA processing in the nucleolus and cytosol
## 410 Immune System
## 273 Eukaryotic Translation Elongation
## 306 Formation of a pool of free 40S subunits
## 659 Peptide chain elongation
## 907 Signal Transduction
## 511 Metabolism of RNA
## 1084 Translation
## 474 L13a-mediated translational silencing of Ceruloplasmin expression
## 341 GTP hydrolysis and joining of the 60S ribosomal subunit
## 895 Selenocysteine synthesis
## 1126 Viral mRNA Translation
## 419 Influenza Viral RNA Transcription and Replication
## 275 Eukaryotic Translation Termination
## 107 Cap-dependent Translation Initiation
## 274 Eukaryotic Translation Initiation
## 507 Membrane Trafficking
## 609 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)
## 1123 Vesicle-mediated transport
## 854 Response of EIF2AK4 (GCN2) to amino acid deficiency
## 894 Selenoamino acid metabolism
## 418 Influenza Infection
## 877 SRP-dependent cotranslational protein targeting to membrane
## 608 Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)
## 610 Nonsense-Mediated Decay (NMD)
## 967 Signaling by Receptor Tyrosine Kinases
## setSize pMANOVA s.RNA s.meth p.RNA p.meth
## 602 305 1.839942e-43 0.4682129 -0.0637568200 1.556093e-44 5.788476e-02
## 426 616 3.632803e-39 0.3179279 0.0009946224 3.598477e-40 9.670495e-01
## 1154 159 2.242209e-36 -0.5836874 0.1227452172 6.593984e-37 7.863070e-03
## 1155 154 1.524395e-35 -0.5845706 0.1329846238 6.490250e-36 4.585052e-03
## 502 147 2.326663e-35 -0.5967108 0.1345649541 9.377045e-36 5.053185e-03
## 410 1142 8.468712e-34 0.2184200 0.0348905773 2.870621e-33 5.581816e-02
## 273 70 2.390361e-26 -0.7344990 0.2052238343 2.057379e-26 3.046100e-03
## 306 76 8.930095e-26 -0.6998628 0.1819218241 4.840533e-26 6.221404e-03
## 659 67 3.937790e-25 -0.7320038 0.2080275486 3.431098e-25 3.293493e-03
## 907 1290 5.270862e-25 0.1705675 0.0528060542 5.570059e-23 2.298511e-03
## 511 535 5.950107e-25 -0.2593991 0.0963754209 4.749592e-24 1.774875e-04
## 1084 234 6.928909e-25 -0.4016650 0.0472209947 5.814428e-26 2.167733e-01
## 474 85 1.427499e-23 -0.6269152 0.1904255931 1.676289e-23 2.467955e-03
## 341 86 1.510523e-23 -0.6214611 0.1954170880 2.257091e-23 1.779893e-03
## 895 68 1.635992e-23 -0.7054580 0.1839718964 7.885506e-24 8.841144e-03
## 1126 67 2.227142e-23 -0.7008968 0.2198493378 3.210041e-23 1.896199e-03
## 419 110 4.420886e-23 -0.5317726 0.2148727907 6.245388e-22 1.039331e-04
## 275 70 1.335358e-22 -0.6848617 0.1581132192 3.693743e-23 2.245165e-02
## 107 93 2.306870e-22 -0.5796381 0.1930840319 4.628933e-22 1.330093e-03
## 274 93 2.306870e-22 -0.5796381 0.1930840319 4.628933e-22 1.330093e-03
## 507 436 7.777816e-22 0.2779962 -0.0023314155 7.609158e-23 9.344047e-01
## 609 72 1.019976e-21 -0.6507911 0.2057979403 1.301906e-21 2.585238e-03
## 1123 442 1.603235e-21 0.2736656 0.0040378118 1.865388e-22 8.859168e-01
## 854 76 1.049725e-20 -0.6245443 0.1670866907 4.826701e-21 1.198122e-02
## 894 76 3.411738e-20 -0.6162866 0.1661609235 1.574327e-20 1.246250e-02
## 418 128 8.196374e-20 -0.4477489 0.2068683119 2.544846e-18 5.650395e-05
## 877 85 6.571163e-19 -0.5501435 0.2047872034 1.923401e-18 1.131216e-03
## 608 86 1.637725e-18 -0.5365319 0.2151845298 8.407537e-18 5.796399e-04
## 610 86 1.637725e-18 -0.5365319 0.2151845298 8.407537e-18 5.796399e-04
## 967 284 1.177266e-14 0.2731803 0.0329426012 3.750258e-15 3.438222e-01
## s.dist SD p.adjustMANOVA
## 602 0.4725338 0.37615937 2.136172e-40
## 426 0.3179295 0.22410568 2.108842e-36
## 1154 0.5964539 0.49952326 8.677349e-34
## 1155 0.5995062 0.50738816 4.424556e-33
## 502 0.6116956 0.51709006 5.402510e-33
## 410 0.2211892 0.12977491 1.638696e-31
## 273 0.7626307 0.66448439 3.964585e-24
## 306 0.7231207 0.62351592 1.295980e-23
## 659 0.7609895 0.66470256 5.079749e-23
## 907 0.1785546 0.08326988 6.119470e-23
## 511 0.2767239 0.25157060 6.280067e-23
## 1084 0.4044312 0.31741035 6.703720e-23
## 474 0.6551982 0.57794724 1.252656e-21
## 341 0.6514612 0.57762009 1.252656e-21
## 895 0.7290519 0.62892191 1.266258e-21
## 1126 0.7345679 0.65106580 1.616070e-21
## 419 0.5735437 0.52795802 3.019205e-21
## 275 0.7028765 0.59607330 8.613056e-21
## 107 0.6109515 0.54639705 1.339138e-20
## 274 0.6109515 0.54639705 1.339138e-20
## 507 0.2780059 0.19822153 4.300021e-20
## 609 0.6825553 0.60569990 5.382692e-20
## 1123 0.2736954 0.19065564 8.092851e-20
## 854 0.6465087 0.55976761 5.078046e-19
## 894 0.6382936 0.55327398 1.584411e-18
## 418 0.4932277 0.46288427 3.659996e-18
## 877 0.5870227 0.53381664 2.825600e-17
## 608 0.5780751 0.53154375 6.556548e-17
## 610 0.5780751 0.53154375 6.556548e-17
## 967 0.2751594 0.16987370 4.556020e-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/RtmpbT00es/pod_crp_int.rds ".##
##
## processing file: mitch.Rmd## output file: /mnt/bfx6/bfx/bain/inflam/integrate/mitch.knit.md##
## Output created: /tmp/RtmpbT00es/mitch_report.html# t0_crp
head(t0_crp)## RNA meth
## A1BG -3.3462109 0.9487855
## AAAS -0.3078565 0.4334437
## AACS -0.4896883 0.8690806
## AAGAB 1.1384144 0.5727567
## AAK1 0.5029925 0.7201373
## AAMDC -1.2878387 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
## 274 Eukaryotic Translation Elongation
## 660 Peptide chain elongation
## 307 Formation of a pool of free 40S subunits
## 897 Selenocysteine synthesis
## 1130 Viral mRNA Translation
## 276 Eukaryotic Translation Termination
## 475 L13a-mediated translational silencing of Ceruloplasmin expression
## 896 Selenoamino acid metabolism
## 342 GTP hydrolysis and joining of the 60S ribosomal subunit
## 610 Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)
## 107 Cap-dependent Translation Initiation
## 275 Eukaryotic Translation Initiation
## 856 Response of EIF2AK4 (GCN2) to amino acid deficiency
## 833 Regulation of expression of SLITs and ROBOs
## 879 SRP-dependent cotranslational protein targeting to membrane
## 609 Nonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC)
## 611 Nonsense-Mediated Decay (NMD)
## 130 Cellular response to starvation
## 71 Axon guidance
## 596 Nervous system development
## 968 Signaling by ROBO receptors
## 420 Influenza Viral RNA Transcription and Replication
## 1088 Translation
## 131 Cellular responses to external stimuli
## 132 Cellular responses to stress
## 603 Neutrophil degranulation
## 511 Metabolism
## 1160 rRNA processing in the nucleus and cytosol
## 214 Developmental Biology
## 1159 rRNA processing
## setSize pMANOVA s.RNA s.meth p.RNA p.meth
## 274 70 3.258046e-31 -0.8067915 -0.0891702827 1.456184e-31 0.19801642
## 660 67 1.738058e-29 -0.7987446 -0.1015809218 1.036587e-29 0.15132710
## 307 76 4.759693e-29 -0.7468845 -0.0816344539 1.879334e-29 0.21963374
## 897 68 3.255640e-27 -0.7662189 -0.0650091226 7.640162e-28 0.35495549
## 1130 67 9.036884e-27 -0.7630682 -0.0807331400 3.027390e-27 0.25413821
## 276 70 1.732271e-26 -0.7422703 -0.0807542042 6.066653e-27 0.24372635
## 475 85 5.910754e-26 -0.6700303 -0.0533666217 1.238251e-26 0.39632401
## 896 76 1.736220e-25 -0.6974581 -0.0834248954 7.129800e-26 0.20968685
## 342 86 1.355598e-24 -0.6491381 -0.0431372055 2.297222e-25 0.49044667
## 610 72 1.454166e-23 -0.6897322 -0.0731009959 4.317261e-24 0.28457759
## 107 93 1.535829e-23 -0.6106474 -0.0401815885 2.546915e-24 0.50430811
## 275 93 1.535829e-23 -0.6106474 -0.0401815885 2.546915e-24 0.50430811
## 856 76 5.391045e-22 -0.6490478 -0.0627793117 1.317139e-22 0.34518484
## 833 122 1.215849e-18 -0.4709083 -0.0496187219 3.020337e-19 0.34571912
## 879 85 2.353438e-18 -0.5525565 -0.0906096349 1.363646e-18 0.14981242
## 609 86 8.857790e-18 -0.5468428 -0.0534191209 1.955780e-18 0.39312006
## 611 86 8.857790e-18 -0.5468428 -0.0534191209 1.955780e-18 0.39312006
## 130 119 1.097946e-17 -0.4690942 -0.0005534055 1.107063e-18 0.99171178
## 71 295 1.684724e-17 -0.2970875 -0.0227373276 2.943405e-18 0.50570496
## 596 307 1.445504e-16 -0.2830552 -0.0229487392 2.614699e-17 0.49347050
## 968 153 1.714218e-15 -0.3856152 -0.0164510831 2.264908e-16 0.72671223
## 420 110 5.567492e-15 -0.4445317 -0.0306728122 8.956213e-16 0.57969384
## 1088 234 3.991040e-14 -0.2816986 -0.0870257972 1.592675e-13 0.02280668
## 131 457 1.775564e-12 -0.2019760 -0.0134440986 2.827067e-13 0.62740947
## 132 452 2.146138e-12 -0.2024482 -0.0124047468 3.306123e-13 0.65594350
## 603 305 3.801828e-12 -0.2402156 -0.0263560592 8.351452e-13 0.43304056
## 511 1216 5.099476e-12 -0.1248466 0.0345132959 1.923375e-12 0.05192319
## 1160 154 7.221995e-12 -0.3264845 -0.0597626438 3.227685e-12 0.20274204
## 214 451 1.058663e-11 -0.1957744 -0.0180848272 2.018446e-12 0.51643904
## 1159 159 1.563430e-11 -0.3156859 -0.0614638808 7.739023e-12 0.18326224
## s.dist SD p.adjustMANOVA
## 274 0.8117043 0.5074349 3.798882e-28
## 660 0.8051780 0.4929691 1.013288e-26
## 307 0.7513326 0.4704028 1.849934e-26
## 897 0.7689718 0.4958302 9.490191e-25
## 1130 0.7673271 0.4824837 2.107401e-24
## 276 0.7466502 0.4677625 3.366381e-24
## 475 0.6721522 0.4360471 9.845628e-24
## 896 0.7024297 0.4341870 2.530541e-23
## 342 0.6505699 0.4285074 1.756252e-22
## 610 0.6935952 0.4360241 1.492314e-21
## 107 0.6119679 0.4033802 1.492314e-21
## 275 0.6119679 0.4033802 1.492314e-21
## 856 0.6520770 0.4145545 4.835353e-20
## 833 0.4735152 0.2978967 1.012629e-16
## 879 0.5599364 0.3266458 1.829406e-16
## 609 0.5494457 0.3489032 6.075402e-16
## 611 0.5494457 0.3489032 6.075402e-16
## 130 0.4690945 0.3313084 7.112252e-16
## 71 0.2979563 0.1939949 1.033889e-15
## 596 0.2839839 0.1839230 8.427286e-15
## 968 0.3859660 0.2610385 9.517989e-14
## 420 0.4455887 0.2926425 2.950771e-13
## 1088 0.2948348 0.1376544 2.023284e-12
## 131 0.2024229 0.1333122 8.626281e-11
## 132 0.2028279 0.1343810 1.000959e-10
## 603 0.2416571 0.1512215 1.704974e-10
## 511 0.1295293 0.1126845 2.202218e-10
## 1160 0.3319091 0.1886008 3.007445e-10
## 214 0.1966079 0.1256455 4.256556e-10
## 1159 0.3216138 0.1797621 6.076531e-10unlink("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/RtmpbT00es/t0_crp_int.rds ".##
##
## processing file: mitch.Rmd## output file: /mnt/bfx6/bfx/bain/inflam/integrate/mitch.knit.md##
## Output created: /tmp/RtmpbT00es/mitch_report.htmlsessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] pkgload_1.2.0 GGally_2.1.1 ggplot2_3.3.3 reshape2_1.4.4
## [5] beeswarm_0.3.1 gplots_3.1.1 gtools_3.8.2 tibble_3.1.0
## [9] dplyr_1.0.5 echarts4r_0.4.0 mitch_1.2.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.6 rprojroot_2.0.2 assertthat_0.2.1 digest_0.6.27
## [5] utf8_1.2.1 mime_0.10 R6_2.5.0 plyr_1.8.6
## [9] evaluate_0.14 highr_0.8 pillar_1.5.1 rlang_0.4.10
## [13] jquerylib_0.1.3 rmarkdown_2.7 labeling_0.4.2 desc_1.3.0
## [17] stringr_1.4.0 htmlwidgets_1.5.3 munsell_0.5.0 shiny_1.6.0
## [21] compiler_4.0.3 httpuv_1.5.5 xfun_0.22 pkgconfig_2.0.3
## [25] htmltools_0.5.1.1 tidyselect_1.1.0 gridExtra_2.3 reshape_0.8.8
## [29] fansi_0.4.2 crayon_1.4.1 withr_2.4.1 later_1.1.0.1
## [33] MASS_7.3-53.1 bitops_1.0-6 grid_4.0.3 jsonlite_1.7.2
## [37] xtable_1.8-4 gtable_0.3.0 lifecycle_1.0.0 DBI_1.1.1
## [41] magrittr_2.0.1 scales_1.1.1 KernSmooth_2.23-18 stringi_1.5.3
## [45] farver_2.1.0 promises_1.2.0.1 testthat_3.0.2 bslib_0.2.4
## [49] ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.6 RColorBrewer_1.1-2
## [53] tools_4.0.3 glue_1.4.2 purrr_0.3.4 parallel_4.0.3
## [57] fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-0 caTools_1.18.1
## [61] knitr_1.31 sass_0.3.1