Variance Partition on Pseudobulk data
Last updated: 2021-07-05
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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 5536dae | KLRhodes | 2020-08-31 | Build site. |
| Rmd | e16856a | KLRhodes | 2020-08-31 | wflow_publish("analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes*") |
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(limma)
library(edgeR)
library(variancePartition)Loading required package: ggplot2Loading required package: foreachLoading required package: scalesLoading required package: BiobaseLoading required package: BiocGenericsLoading required package: parallel
Attaching package: 'BiocGenerics'The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLBThe following object is masked from 'package:limma':
plotMAThe following objects are masked from 'package:dplyr':
combine, intersect, setdiff, unionThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colnames, dirname, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
tapply, union, unique, unsplit, which, which.max, which.minWelcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'variancePartition'The following object is masked from 'package:limma':
classifyTestsFchoose parameters (integration type, clustering res, min pct threshold)
f<- 'Harmony.Batchindividual'
pct<-0.2
res<- 'SCT_snn_res.0.5'path<- here::here("output/DGELists/")
submerged<- readRDS(paste0(path,"Pseudobulk_dge_",f, "_", res,"_minPCT",pct,".rds"))cpm<- cpm(submerged)
lcpm<- cpm(submerged, log=TRUE)L<- mean(submerged$samples$lib.size) *1e-6
M<- median(submerged$samples$lib.size) *1e-6genes.ribo <- grep('^RP',rownames(submerged),value=T)
genes.no.ribo <- rownames(submerged)[which(!(rownames(submerged) %in% genes.ribo))]
submerged$counts <- submerged$counts[which(rownames(submerged$counts) %in% genes.no.ribo),] #remove ribosomal genessubmerged<- calcNormFactors(submerged, method="TMM")
summary(submerged$samples$norm.factors) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.7736 0.8998 0.9755 1.0174 1.0565 2.1383 design<- model.matrix(~submerged$samples$cluster+submerged$samples$batch+submerged$samples$ind)v<- voom(submerged, design, plot=T)
| Version | Author | Date |
|---|---|---|
| 5536dae | KLRhodes | 2020-08-31 |
vAn object of class "EList"
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HES4 6.435908 8.071288 6.926417
ISG15 4.113980 5.017773 8.121483
16.Batch1.SNG-NA19160 16.Batch2.SNG-NA18511 16.Batch2.SNG-NA18858
SAMD11 4.245240 5.889975 3.845817
NOC2L 5.623752 5.153009 5.743937
PLEKHN1 5.742072 4.416044 4.634313
HES4 6.814083 6.104100 7.127852
ISG15 6.084304 6.455572 8.023010
16.Batch2.SNG-NA19160 16.Batch3.SNG-NA18511 16.Batch3.SNG-NA18858
SAMD11 4.349821 4.928228 2.930750
NOC2L 5.907039 5.435188 5.904755
PLEKHN1 4.264932 4.139732 5.332848
HES4 6.420211 4.928228 6.943574
ISG15 4.775127 4.380741 8.288302
16.Batch3.SNG-NA19160 17.Batch1.SNG-NA18511 17.Batch1.SNG-NA18858
SAMD11 5.423176 2.33381637 3.100060
NOC2L 5.511985 6.03425609 5.421988
PLEKHN1 3.969458 0.01188828 3.100060
HES4 6.015751 7.23105680 5.907415
ISG15 5.278786 9.65754671 5.421988
17.Batch1.SNG-NA19160 17.Batch2.SNG-NA18511 17.Batch2.SNG-NA19160
SAMD11 1.6766868 0.2434696 -0.8078316
NOC2L 5.7297981 6.1261127 5.7920813
PLEKHN1 -0.6452413 0.2434696 -0.8078316
HES4 6.9472157 6.0763597 6.8575044
ISG15 8.1588897 8.0700181 7.5185979
17.Batch3.SNG-NA18511 17.Batch3.SNG-NA19160 2.Batch1.SNG-NA18511
SAMD11 -0.03310476 0.79402418 2.2563784
NOC2L 5.58160508 5.71050082 6.4543889
PLEKHN1 -0.03310476 0.05705858 -0.9586345
HES4 7.02217767 6.45508966 9.1634539
ISG15 8.07018305 8.38648122 5.7789750
2.Batch1.SNG-NA18858 2.Batch1.SNG-NA19160 2.Batch2.SNG-NA18511
SAMD11 3.371089 3.347964 1.689570
NOC2L 6.146383 6.216891 6.145876
PLEKHN1 1.255612 -1.442113 -2.702747
HES4 7.277980 8.816453 8.803891
ISG15 5.601387 6.054186 5.847999
2.Batch2.SNG-NA18858 2.Batch2.SNG-NA19160 2.Batch3.SNG-NA18511
SAMD11 2.732835 2.168768 2.184671
NOC2L 5.786350 6.170406 6.126751
PLEKHN1 -0.371502 -2.027630 -1.833251
HES4 6.812381 8.443707 8.169846
ISG15 6.051404 5.688904 5.803374
2.Batch3.SNG-NA18858 2.Batch3.SNG-NA19160 3.Batch1.SNG-NA18511
SAMD11 1.6522064 4.053993 2.8900071
NOC2L 6.0445238 6.321567 6.2101960
PLEKHN1 -0.6697217 -2.840825 0.4218583
HES4 6.7978838 7.646010 5.4836345
ISG15 6.2727928 6.361299 6.4263597
3.Batch1.SNG-NA18858 3.Batch1.SNG-NA19160 3.Batch2.SNG-NA18511
SAMD11 3.314066 3.219328 3.2494233
NOC2L 6.519180 6.247685 5.7109353
PLEKHN1 1.729103 -1.185427 -0.4059285
HES4 5.925500 5.927842 4.9516235
ISG15 6.802352 6.621343 6.4545378
3.Batch2.SNG-NA18858 3.Batch2.SNG-NA19160 3.Batch3.SNG-NA18511
SAMD11 4.442953 2.984379 2.97513203
NOC2L 5.580457 5.967890 6.02099904
PLEKHN1 2.121025 -0.234045 -0.04116978
HES4 3.705987 5.197372 3.81228156
ISG15 6.208488 6.316998 6.22901935
3.Batch3.SNG-NA18858 3.Batch3.SNG-NA19160 4.Batch1.SNG-NA18511
SAMD11 2.342371 3.308837 4.64058645
NOC2L 5.801803 6.135238 6.43920657
PLEKHN1 2.342371 -1.269431 0.07580184
HES4 5.149726 4.920394 7.03387116
ISG15 6.865933 6.403780 6.03773380
4.Batch1.SNG-NA18858 4.Batch1.SNG-NA19160 4.Batch2.SNG-NA18511
SAMD11 4.1871834 3.048785 5.1585532
NOC2L 6.5606418 6.254799 6.2580889
PLEKHN1 0.7277518 -1.373747 -0.4561566
HES4 6.8362763 6.582387 6.3120277
ISG15 4.8152147 5.112753 5.8836934
4.Batch2.SNG-NA18858 4.Batch2.SNG-NA19160 4.Batch3.SNG-NA18511
SAMD11 2.596729 3.410961 2.4756421
NOC2L 5.766654 6.181896 6.0794637
PLEKHN1 2.596729 -1.486280 -0.1873229
HES4 5.404084 6.501269 4.4945012
ISG15 4.918657 4.943336 4.6706581
4.Batch3.SNG-NA18858 4.Batch3.SNG-NA19160 5.Batch1.SNG-NA18511
SAMD11 1.904391 4.451682 3.9378483
NOC2L 6.659279 6.030057 6.2301309
PLEKHN1 1.904391 -2.741433 0.3771334
HES4 5.811282 5.590977 8.2413195
ISG15 5.074316 4.232026 5.5709051
5.Batch1.SNG-NA18858 5.Batch1.SNG-NA19160 5.Batch2.SNG-NA18511
SAMD11 4.491454 1.8299187 2.9613083
NOC2L 6.055807 6.0645800 6.0063548
PLEKHN1 1.258794 -0.3717152 0.5956588
HES4 5.240646 6.6124184 6.6450885
ISG15 5.731281 5.6595036 5.2745975
5.Batch2.SNG-NA18858 5.Batch2.SNG-NA19160 5.Batch3.SNG-NA18511
SAMD11 3.147236 3.0621076 2.2925337
NOC2L 5.639475 5.7920754 5.9991685
PLEKHN1 1.416052 0.6104116 0.4180646
HES4 5.523740 4.7756142 5.0289947
ISG15 6.136331 5.3715717 5.5704144
5.Batch3.SNG-NA18858 5.Batch3.SNG-NA19160 6.Batch1.SNG-NA18511
SAMD11 2.512142 3.1373449 4.7206961
NOC2L 5.710922 6.0961633 6.0154881
PLEKHN1 2.222636 0.8453183 0.8492108
HES4 4.983448 5.2655485 7.0815227
ISG15 6.018495 5.7705874 6.9728620
6.Batch1.SNG-NA18858 6.Batch1.SNG-NA19160 6.Batch2.SNG-NA18511
SAMD11 2.725273 4.488108 4.579490
NOC2L 5.760897 5.937718 5.942410
PLEKHN1 5.447739 3.376215 1.132604
HES4 6.812736 7.025645 7.144364
ISG15 6.778384 7.517415 6.783314
6.Batch2.SNG-NA18858 6.Batch2.SNG-NA19160 6.Batch3.SNG-NA18511
SAMD11 4.369502 3.789253 4.9796225
NOC2L 5.287039 5.771975 6.0276697
PLEKHN1 3.521505 1.467325 0.7085187
HES4 6.625841 6.756246 5.7050465
ISG15 5.954464 7.131645 6.2678222
6.Batch3.SNG-NA18858 6.Batch3.SNG-NA19160 7.Batch1.SNG-NA18511
SAMD11 3.334450 4.4266903 3.718819
NOC2L 5.812497 5.9923199 6.151778
PLEKHN1 2.112058 0.1496654 2.981853
HES4 7.213596 6.4079655 6.606344
ISG15 7.241341 7.0967132 5.303781
7.Batch1.SNG-NA18858 7.Batch1.SNG-NA19160 7.Batch2.SNG-NA18511
SAMD11 1.860589 2.202334 3.227946
NOC2L 6.238745 5.661766 6.332282
PLEKHN1 1.098748 0.617372 2.490980
HES4 5.538082 4.076804 5.153945
ISG15 5.110462 6.109225 4.365449
7.Batch2.SNG-NA18858 7.Batch2.SNG-NA19160 7.Batch3.SNG-NA18511
SAMD11 1.070192 3.885427 2.816636
NOC2L 5.884888 6.141767 6.316902
PLEKHN1 1.597821 0.715502 2.028141
HES4 5.643396 5.013183 5.708260
ISG15 5.347403 5.759896 5.666741
7.Batch3.SNG-NA18858 7.Batch3.SNG-NA19160 8.Batch1.SNG-NA18511
SAMD11 0.9598028 2.8576628 2.707524
NOC2L 5.7741038 6.0609464 5.889967
PLEKHN1 1.3650593 0.1946978 -2.964902
HES4 5.2797800 4.7995599 9.128846
ISG15 5.5398831 5.7896444 4.935965
8.Batch1.SNG-NA18858 8.Batch1.SNG-NA19160 8.Batch2.SNG-NA18511
SAMD11 2.5725796 3.0196698 2.910340
NOC2L 5.6478677 5.9582693 5.894180
PLEKHN1 -0.2347754 -0.8872208 -3.747871
HES4 7.7367682 8.6278219 8.975576
ISG15 4.8945077 4.8941389 5.209231
8.Batch2.SNG-NA18858 8.Batch2.SNG-NA19160 8.Batch3.SNG-NA18511
SAMD11 2.352117 1.509119 2.128847
NOC2L 5.393145 5.583467 5.698313
PLEKHN1 -1.348322 -2.191320 -3.156555
HES4 7.065306 8.344927 8.441962
ISG15 4.801425 4.774464 5.741290
8.Batch3.SNG-NA18858 8.Batch3.SNG-NA19160 9.Batch1.SNG-NA18511
SAMD11 3.4885531 3.896876 4.0997227
NOC2L 6.0387501 5.771345 6.5538986
PLEKHN1 0.6811981 -1.594977 -0.5441335
HES4 6.5140881 7.557308 6.6357756
ISG15 5.8906515 5.299841 3.9794285
9.Batch1.SNG-NA18858 9.Batch1.SNG-NA19160 9.Batch2.SNG-NA18511
SAMD11 3.717622 3.1891642 0.01814573
NOC2L 6.440088 6.6459964 5.30354795
PLEKHN1 1.395694 -0.2991222 0.01814573
HES4 5.919256 6.7220120 6.73239125
ISG15 6.821959 2.7207773 4.10560857
9.Batch2.SNG-NA18858 9.Batch2.SNG-NA19160 9.Batch3.SNG-NA18511
SAMD11 5.938852 2.7629643 3.440982
NOC2L 5.201886 6.1343611 5.969361
PLEKHN1 3.616923 0.7961311 1.325505
HES4 6.424278 6.2703902 5.295131
ISG15 3.616923 2.5461529 4.495430
9.Batch3.SNG-NA18858 9.Batch3.SNG-NA19160
SAMD11 2.305067 3.9919915
NOC2L 5.764498 6.2076266
PLEKHN1 2.305067 -2.7494755
HES4 3.890029 5.5405434
ISG15 3.890029 0.9509643
10910 more rows ...
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[,128] [,129] [,130] [,131] [,132] [,133] [,134]
[1,] 4.906621 4.690456 0.7208601 8.402172 0.3541681 2.854388 0.5264401
[2,] 7.436713 7.988575 2.4108431 12.185855 2.1437669 9.490563 2.7526030
[3,] 1.838781 1.786653 0.5083354 3.299111 0.2080015 2.952120 0.2210246
[4,] 9.808512 9.821654 2.7852738 13.894474 1.7503882 7.747257 2.1855487
[5,] 9.251173 10.255212 3.8924813 15.325287 1.3077562 8.623161 1.9743526
[,135] [,136] [,137] [,138] [,139] [,140] [,141]
[1,] 0.3893749 4.735203 0.9900201 1.2410678 3.260434 1.2893143 3.194116
[2,] 2.3556930 13.570135 4.0969404 5.3533541 11.100627 5.0859999 8.164818
[3,] 0.2201520 5.595792 0.4245559 0.5206975 3.600769 0.4716777 0.276699
[4,] 1.9934519 11.382466 3.2993097 3.9330690 8.124000 3.4280556 15.364851
[5,] 1.5322707 12.523126 3.1001201 4.2384504 10.652753 4.0396019 6.398980
[,142] [,143] [,144] [,145] [,146] [,147] [,148]
[1,] 0.6215747 2.8507008 3.7922673 0.9739062 2.3586125 3.0353457 0.3454113
[2,] 3.1769970 6.8903612 9.3386884 4.2766794 5.8384105 8.1830512 2.1983235
[3,] 0.2080015 0.2144732 0.4022401 0.2524875 0.2080015 0.2776995 0.2080015
[4,] 6.7875551 12.7021814 17.3764770 8.6299043 11.2267058 13.8394955 4.0259093
[5,] 2.8636735 5.3315572 7.5034786 3.7699647 4.4474726 6.8141108 2.0481007
[,149] [,150] [,151] [,152] [,153] [,154] [,155]
[1,] 1.6834395 1.451467 0.3757177 4.7933313 0.8696654 0.2080015 3.2600460
[2,] 4.9254779 4.117287 1.9341539 9.6632011 3.1036773 0.4856851 7.2767409
[3,] 0.2080015 0.270927 0.2103745 0.9950778 0.2272734 0.2080015 0.6793922
[4,] 8.7711245 4.539112 1.8183528 9.7506262 3.3779583 0.4642358 7.3879275
[5,] 3.9212741 1.465926 0.5836596 4.5117504 0.8926882 0.2080015 3.1281272
[,156] [,157] [,158]
[1,] 0.9652068 0.2243569 3.2504393
[2,] 3.4678662 0.9942515 7.6310988
[3,] 0.2262572 0.2080015 0.6142165
[4,] 3.2502862 0.7209098 6.7372140
[5,] 1.2356906 0.3870356 3.4842327
10910 more rows ...
$design
(Intercept) submerged$samples$cluster1 submerged$samples$cluster10
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
submerged$samples$cluster11 submerged$samples$cluster12
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster13 submerged$samples$cluster14
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster15 submerged$samples$cluster16
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster17 submerged$samples$cluster2
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster3 submerged$samples$cluster4
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster5 submerged$samples$cluster6
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster7 submerged$samples$cluster8
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster9 submerged$samples$batchBatch2
1 0 0
2 0 0
3 0 0
4 0 1
5 0 1
submerged$samples$batchBatch3 submerged$samples$indNA18858
1 0 0
2 0 1
3 0 0
4 0 0
5 0 1
submerged$samples$indNA19160
1 0
2 0
3 1
4 0
5 0
153 more rows ...form<- ~ (1|cluster) + (1|batch) + (1|ind)remove(cpm)
remove(lcpm)varpart<- suppressWarnings(fitExtractVarPartModel(v, form, submerged$samples))head(varpart) batch cluster ind Residuals
SAMD11 0.002799719 0.5429372 0.096601793 0.3576613
NOC2L 0.149957205 0.3864233 0.035753172 0.4278663
PLEKHN1 0.001463317 0.7223096 0.037157698 0.2390694
HES4 0.081212319 0.6843282 0.088018345 0.1464411
ISG15 0.001168324 0.7520208 0.000000000 0.2468109
AGRN 0.093225600 0.5196503 0.004175498 0.3829486vp<- sortCols(varpart)plotPercentBars(vp[1:10,])
| Version | Author | Date |
|---|---|---|
| 5536dae | KLRhodes | 2020-08-31 |
colnames(vp)<- c("Cluster", "Replicate", "Individual", "Residuals")
G<- plotVarPart(vp)
G
| Version | Author | Date |
|---|---|---|
| 5536dae | KLRhodes | 2020-08-31 |
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_Psb_VarPart_Res0.5.png", width=8, height=5, units= "in", res=1080)
G
dev.off()#do the genes most effected by individual match what I did in limma?
vp<- vp[order(vp$Individual, decreasing=T),]
head(vp, 30) Cluster Replicate Individual Residuals
EIF1AY 0.006056199 1.630896e-04 0.9687224 0.02505827
TYW3 0.016044549 2.632478e-04 0.9634700 0.02022221
DDX3Y 0.007466603 0.000000e+00 0.9420062 0.05052720
CRYZ 0.020581155 0.000000e+00 0.9366369 0.04278189
USP9Y 0.010607063 0.000000e+00 0.9319171 0.05747579
CAT 0.016701539 3.759428e-03 0.9291598 0.05037924
AC004556.3 0.025558899 1.332542e-02 0.9072925 0.05382319
USP51 0.062809732 0.000000e+00 0.8702684 0.06692190
RRAGB 0.060873141 2.176313e-03 0.8658992 0.07105134
ZNF248 0.034672597 3.893244e-03 0.8617221 0.09971210
ZNF280D 0.024247071 0.000000e+00 0.8600373 0.11571565
TRIM61 0.025738022 1.041216e-02 0.8508388 0.11301099
MAGEH1 0.112875303 1.586753e-02 0.8278961 0.04336102
TSPYL5 0.009575607 1.763938e-03 0.8227102 0.16595021
TAF9B 0.007771881 2.113038e-02 0.8197301 0.15136760
IAH1 0.098476663 5.994039e-03 0.8053270 0.09020233
ZNF717 0.081245670 4.325480e-03 0.7874866 0.12694225
PRKY 0.058114147 1.638350e-10 0.7639476 0.17793821
ZNF300 0.098065748 0.000000e+00 0.7563901 0.14554419
CHCHD2 0.026112556 0.000000e+00 0.7423313 0.23155617
THOC3 0.132757376 8.437761e-03 0.7111300 0.14767490
QPCT 0.196648213 3.577643e-03 0.7076413 0.09213289
MRPS14 0.071026852 0.000000e+00 0.6961753 0.23279783
HEPH 0.169047777 2.303834e-02 0.6922256 0.11568833
NDUFS2 0.169031809 1.406084e-03 0.6868309 0.14273117
PSMD4 0.122100637 1.693527e-02 0.6820493 0.17891483
PNPLA4 0.091558726 4.426092e-02 0.6810775 0.18310281
HAX1 0.125717228 5.096895e-02 0.6766626 0.14665120
SP5 0.194588213 2.094276e-02 0.6705651 0.11390396
INPP5F 0.063949131 5.384900e-02 0.6693502 0.21285166#do the genes most effected by batch match what I did in limma?
vp<- vp[order(vp$Replicate, decreasing=T),]
head(vp, 30) Cluster Replicate Individual Residuals
AP001267.5 0.02924022 0.7957063 5.041800e-02 0.12463545
EEF1A1 0.11980663 0.7321807 5.071019e-02 0.09730243
SF3A2 0.04652698 0.7285281 8.125107e-02 0.14369387
TBL1XR1 0.12485359 0.7258867 5.354097e-05 0.14920617
SMARCB1 0.12503979 0.7175742 2.182838e-02 0.13555760
MED21 0.11861264 0.7116417 2.831116e-03 0.16691460
CLIC1 0.14012194 0.7106406 3.226263e-02 0.11697486
CAPZA1 0.16697815 0.6994491 5.268294e-03 0.12830446
PRPF31 0.13791801 0.6817758 0.000000e+00 0.18030623
TMED2 0.10133690 0.6801096 8.503009e-03 0.21005048
LRRC75A 0.19115624 0.6780323 1.524408e-02 0.11556739
PPP1CB 0.23685359 0.6645484 3.025083e-04 0.09829548
PAN3 0.01604053 0.6544504 5.177421e-03 0.32433169
TMEM167A 0.12432376 0.6523057 2.841913e-02 0.19495144
USP14 0.12543213 0.6415741 6.337160e-03 0.22665659
LSM2 0.09708889 0.6350278 2.237171e-02 0.24551162
EIF4E 0.20357374 0.6334272 0.000000e+00 0.16299911
LYPLA2 0.15760290 0.6323046 0.000000e+00 0.21009253
MOB4 0.16492930 0.6194043 1.630290e-02 0.19936354
UBE2W 0.11951005 0.6185053 2.762684e-11 0.26198460
STRN3 0.07270111 0.6117959 2.690620e-02 0.28859675
MRPL42 0.24325560 0.5931007 6.349586e-02 0.10014788
ZFAND6 0.21190999 0.5920832 1.276013e-02 0.18324666
PUF60 0.15029086 0.5902717 1.693118e-04 0.25926813
HSF1 0.05286916 0.5870064 1.043437e-03 0.35908102
TRA2A 0.25553322 0.5798440 4.176718e-02 0.12285556
PSMD9 0.12868449 0.5780620 8.528791e-03 0.28472472
QSER1 0.20460366 0.5766015 1.764243e-10 0.21879484
TSTA3 0.19144441 0.5748793 8.038499e-03 0.22563776
PDCD10 0.21324747 0.5734357 3.977944e-11 0.21331681summary(vp$Individual) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.008912 0.036037 0.069530 0.086874 0.968722 summary(vp$Replicate) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.009714 0.040345 0.076982 0.108384 0.795706 #genes for which individual contributes more to variance than batch?
vp.indgreaterthanbatch<- vp[vp$Individual>vp$Replicate,]
dim(vp.indgreaterthanbatch) [1] 5203 4head(vp.indgreaterthanbatch, 20) Cluster Replicate Individual Residuals
TSNAX 0.10516774 0.3184507 0.4048210 0.1715606
UHMK1 0.06551171 0.3161128 0.3239734 0.2944021
MDM4 0.16945409 0.3043024 0.4035009 0.1227426
SF3B4 0.12731902 0.2998926 0.3907234 0.1820650
SNAP47 0.04257368 0.2892635 0.4384036 0.2297592
CDC73 0.10351765 0.2640608 0.4608451 0.1715764
ADNP 0.33351307 0.2622948 0.2684509 0.1357412
TOR1AIP2 0.25641631 0.2564138 0.2838155 0.2033545
PFKFB4 0.25582652 0.2508210 0.2585593 0.2347932
RAP2C 0.27249752 0.2501271 0.3323323 0.1450431
LBR 0.37384770 0.2497258 0.2603981 0.1160284
PRCC 0.19161988 0.2445727 0.3259689 0.2378386
FNTA 0.17852576 0.2381401 0.3267866 0.2565475
ACBD3 0.19141131 0.2375936 0.2550699 0.3159252
SMG9 0.14010265 0.2368916 0.4115361 0.2114697
CDK16 0.15026381 0.2325361 0.4426566 0.1745435
EIF2S3 0.33793423 0.2323612 0.2891242 0.1405804
ZNF678 0.24818548 0.2311941 0.2531715 0.2674489
NUDT12 0.20969801 0.2305364 0.2336649 0.3261007
ADCK1 0.21141675 0.2287885 0.3040930 0.2557017sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] variancePartition_1.16.1 Biobase_2.46.0 BiocGenerics_0.32.0
[4] scales_1.1.1 foreach_1.5.0 ggplot2_3.3.3
[7] edgeR_3.28.1 limma_3.42.2 dplyr_1.0.2
[10] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 locfit_1.5-9.4 here_0.1-11
[4] lattice_0.20-38 prettyunits_1.1.1 gtools_3.8.2
[7] rprojroot_2.0.2 digest_0.6.27 plyr_1.8.6
[10] R6_2.5.0 evaluate_0.14 highr_0.8
[13] pillar_1.4.7 progress_1.2.2 gplots_3.0.4
[16] rlang_0.4.10 minqa_1.2.4 gdata_2.18.0
[19] whisker_0.4 nloptr_1.2.2.2 Matrix_1.2-18
[22] rmarkdown_2.3 labeling_0.4.2 splines_3.6.1
[25] BiocParallel_1.20.1 lme4_1.1-23 statmod_1.4.34
[28] stringr_1.4.0 munsell_0.5.0 compiler_3.6.1
[31] httpuv_1.5.4 xfun_0.16 pkgconfig_2.0.3
[34] htmltools_0.5.0 tidyselect_1.1.0 tibble_3.0.4
[37] codetools_0.2-16 crayon_1.3.4 withr_2.4.2
[40] later_1.1.0.1 MASS_7.3-51.4 bitops_1.0-6
[43] grid_3.6.1 nlme_3.1-140 gtable_0.3.0
[46] lifecycle_0.2.0 git2r_0.26.1 magrittr_2.0.1
[49] KernSmooth_2.23-15 stringi_1.5.3 farver_2.0.3
[52] reshape2_1.4.4 fs_1.4.2 promises_1.1.1
[55] doParallel_1.0.15 colorRamps_2.3 ellipsis_0.3.1
[58] generics_0.1.0 vctrs_0.3.6 boot_1.3-23
[61] iterators_1.0.12 tools_3.6.1 glue_1.4.2
[64] purrr_0.3.4 hms_0.5.3 pbkrtest_0.4-8.6
[67] yaml_2.2.1 colorspace_2.0-0 caTools_1.18.0
[70] knitr_1.29
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] variancePartition_1.16.1 Biobase_2.46.0 BiocGenerics_0.32.0
[4] scales_1.1.1 foreach_1.5.0 ggplot2_3.3.3
[7] edgeR_3.28.1 limma_3.42.2 dplyr_1.0.2
[10] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 locfit_1.5-9.4 here_0.1-11
[4] lattice_0.20-38 prettyunits_1.1.1 gtools_3.8.2
[7] rprojroot_2.0.2 digest_0.6.27 plyr_1.8.6
[10] R6_2.5.0 evaluate_0.14 highr_0.8
[13] pillar_1.4.7 progress_1.2.2 gplots_3.0.4
[16] rlang_0.4.10 minqa_1.2.4 gdata_2.18.0
[19] whisker_0.4 nloptr_1.2.2.2 Matrix_1.2-18
[22] rmarkdown_2.3 labeling_0.4.2 splines_3.6.1
[25] BiocParallel_1.20.1 lme4_1.1-23 statmod_1.4.34
[28] stringr_1.4.0 munsell_0.5.0 compiler_3.6.1
[31] httpuv_1.5.4 xfun_0.16 pkgconfig_2.0.3
[34] htmltools_0.5.0 tidyselect_1.1.0 tibble_3.0.4
[37] codetools_0.2-16 crayon_1.3.4 withr_2.4.2
[40] later_1.1.0.1 MASS_7.3-51.4 bitops_1.0-6
[43] grid_3.6.1 nlme_3.1-140 gtable_0.3.0
[46] lifecycle_0.2.0 git2r_0.26.1 magrittr_2.0.1
[49] KernSmooth_2.23-15 stringi_1.5.3 farver_2.0.3
[52] reshape2_1.4.4 fs_1.4.2 promises_1.1.1
[55] doParallel_1.0.15 colorRamps_2.3 ellipsis_0.3.1
[58] generics_0.1.0 vctrs_0.3.6 boot_1.3-23
[61] iterators_1.0.12 tools_3.6.1 glue_1.4.2
[64] purrr_0.3.4 hms_0.5.3 pbkrtest_0.4-8.6
[67] yaml_2.2.1 colorspace_2.0-0 caTools_1.18.0
[70] knitr_1.29