Variance Partition on Pseudobulk data
Last updated: 2020-08-31
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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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| File | Version | Author | Date | Message |
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| 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.0.8'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$norm.factors) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.7666 0.9011 0.9717 1.0158 1.0588 2.0554 design<- model.matrix(~submerged$samples$cluster+submerged$samples$batch+submerged$samples$ind)v<- voom(submerged, design, plot=T)
vAn object of class "EList"
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181 more rows ...
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4.Batch2.SNG-NA18511 4.Batch2.SNG-NA18858 4.Batch2.SNG-NA19160
SAMD11 3.2788062 4.432943 2.9963374
NOC2L 5.7403181 5.570447 5.9798493
PLEKHN1 -0.3765456 2.111015 -0.2220861
HES4 4.9810064 3.695978 5.2093312
ISG15 6.4839206 6.198478 6.3289566
4.Batch3.SNG-NA18511 4.Batch3.SNG-NA18858 4.Batch3.SNG-NA19160
SAMD11 3.00126940 2.351456 3.320616
NOC2L 6.04713641 5.810888 6.147016
PLEKHN1 -0.01503241 2.351456 -1.257652
HES4 3.83841892 5.158811 4.932172
ISG15 6.25515671 6.875018 6.415559
5.Batch1.SNG-NA18511 5.Batch1.SNG-NA18858 5.Batch1.SNG-NA19160
SAMD11 4.6773674 4.1943542 3.082059
NOC2L 6.4205919 6.6175656 6.257231
PLEKHN1 0.1538054 0.7349226 -1.263715
HES4 7.0963199 6.8434470 6.623675
ISG15 6.0686888 4.8223854 4.908434
5.Batch2.SNG-NA18511 5.Batch2.SNG-NA18858 5.Batch2.SNG-NA19160
SAMD11 5.2614065 2.701928 3.310124
NOC2L 6.2466095 5.871853 6.177288
PLEKHN1 -0.3533033 2.701928 -1.444764
HES4 5.9865467 5.509283 6.540509
ISG15 5.9504774 5.023856 4.784055
5.Batch3.SNG-NA18511 5.Batch3.SNG-NA18858 5.Batch3.SNG-NA19160
SAMD11 2.4440258 1.965729 4.469679
NOC2L 6.1181142 6.720617 6.036594
PLEKHN1 -0.0584745 1.965729 -1.953226
HES4 4.5063101 5.872620 5.596751
ISG15 4.3789308 5.135654 4.014864
6.Batch1.SNG-NA18511 6.Batch1.SNG-NA18858 6.Batch1.SNG-NA19160
SAMD11 3.701322 1.812091 2.0190301
NOC2L 6.134282 6.288201 5.6435210
PLEKHN1 2.964357 1.340022 0.4340676
HES4 6.588848 5.517683 4.1345074
ISG15 5.286285 5.202810 6.0487775
6.Batch2.SNG-NA18511 6.Batch2.SNG-NA18858 6.Batch2.SNG-NA19160
SAMD11 3.196284 1.051967 3.863615
NOC2L 6.300621 5.913374 6.109091
PLEKHN1 2.459319 1.664306 1.231346
HES4 5.122284 5.618152 4.975507
ISG15 4.333788 5.387922 5.729597
6.Batch3.SNG-NA18511 6.Batch3.SNG-NA18858 6.Batch3.SNG-NA19160
SAMD11 2.674473 0.8356805 2.8916891
NOC2L 6.332004 5.8146351 6.0768005
PLEKHN1 2.369619 1.2594883 0.8212998
HES4 5.595039 5.3157565 4.7661582
ISG15 5.782002 5.5562793 5.7939924
7.Batch1.SNG-NA18511 7.Batch1.SNG-NA18858 7.Batch1.SNG-NA19160
SAMD11 4.04667493 4.462000 2.2510454
NOC2L 6.21962128 6.068179 6.0715517
PLEKHN1 -0.09628303 1.292075 -0.7124287
HES4 8.51227983 5.345187 6.5067398
ISG15 5.59487888 5.738332 5.8695250
7.Batch2.SNG-NA18511 7.Batch2.SNG-NA18858 7.Batch2.SNG-NA19160
SAMD11 2.966143 3.191139 2.7256477
NOC2L 5.983057 5.639467 5.8116715
PLEKHN1 -1.176815 1.459956 0.5434444
HES4 6.936928 5.560934 4.9876629
ISG15 5.198225 6.153722 5.5118897
7.Batch3.SNG-NA18511 7.Batch3.SNG-NA18858 7.Batch3.SNG-NA19160
SAMD11 2.709869 2.728992 2.750957
NOC2L 5.943616 5.839416 6.114173
PLEKHN1 -4.110310 2.076916 -1.466274
HES4 5.367448 5.152204 5.927760
ISG15 5.701867 6.212075 5.806123
8.Batch1.SNG-NA18511 8.Batch1.SNG-NA18858 8.Batch1.SNG-NA19160
SAMD11 4.7405193 2.792468 4.536257
NOC2L 6.0353113 5.828092 5.985868
PLEKHN1 0.8690341 5.514934 3.424364
HES4 7.1013460 6.879931 7.073794
ISG15 6.9926852 6.845579 7.565565
8.Batch2.SNG-NA18511 8.Batch2.SNG-NA18858 8.Batch2.SNG-NA19160
SAMD11 4.602152 4.427559 3.813348
NOC2L 5.965072 5.345097 5.796070
PLEKHN1 1.155265 3.579562 1.491420
HES4 7.167025 6.683899 6.780341
ISG15 6.805975 6.012522 7.155740
8.Batch3.SNG-NA18511 8.Batch3.SNG-NA18858 8.Batch3.SNG-NA19160
SAMD11 4.9890423 3.364356 4.4278206
NOC2L 6.0370895 5.842403 5.9934502
PLEKHN1 0.7179385 2.141963 0.1507957
HES4 5.7144663 7.243501 6.4090958
ISG15 6.2772420 7.271246 7.0978434
9.Batch1.SNG-NA18511 9.Batch1.SNG-NA18858 9.Batch1.SNG-NA19160
SAMD11 2.714479 2.6340280 3.0230135
NOC2L 5.920249 5.7093162 5.9864876
PLEKHN1 -3.013441 -0.1733269 -0.8838771
HES4 9.152408 7.7982167 8.6623743
ISG15 4.911371 4.9559561 4.9823715
9.Batch2.SNG-NA18511 9.Batch2.SNG-NA18858 9.Batch2.SNG-NA19160
SAMD11 2.918695 2.404528 1.559203
NOC2L 5.982064 5.445555 5.633550
PLEKHN1 -3.767805 -1.295912 -2.141237
HES4 9.033701 7.117716 8.395011
ISG15 5.240623 4.853835 4.824548
9.Batch3.SNG-NA18511 9.Batch3.SNG-NA18858 9.Batch3.SNG-NA19160
SAMD11 2.115656 3.5459682 3.919656
NOC2L 5.722038 6.0961653 5.794125
PLEKHN1 -3.169746 0.7386133 -1.572197
HES4 8.481754 6.5715033 7.605222
ISG15 5.740147 5.9480667 5.370317
11151 more rows ...
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[1,] 0.8119817 8.968680 3.3154081 0.6709907 2.9010075 3.8810454 1.0841725
[2,] 2.4699975 13.963861 8.9828393 3.2173461 7.3143403 10.3077094 4.2874347
[3,] 0.4793855 3.404313 0.2855301 0.1987132 0.2221273 0.4383226 0.2401836
[4,] 2.8816805 16.962175 19.5527602 6.7883007 14.5260881 22.9998603 8.9992459
[5,] 3.7994910 19.502292 6.7529034 2.8253024 5.6566129 7.9864277 3.7112519
[,183] [,184] [,185] [,186]
[1,] 2.4103863 3.1165301 0.3521328 1.8678162
[2,] 5.9371342 8.7179503 2.2712719 4.9509839
[3,] 0.2073898 0.2672415 0.1987132 0.1987132
[4,] 12.3204123 17.5690780 4.1486468 9.5698669
[5,] 4.6508224 7.1047874 2.0859660 4.1204997
11151 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$cluster18
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster19 submerged$samples$cluster2
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster20 submerged$samples$cluster21
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$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$indNA19160
1 0
2 0
3 1
4 0
5 0
181 more rows ...form<- ~ (1|cluster) + (1|batch) + (1|ind)remove(cpm)
remove(lcpm)varpart<- suppressWarnings(fitExtractVarPartModel(v, form, submerged$samples))head(varpart) batch cluster ind Residuals
SAMD11 0.003933550 0.5452203 0.091646758 0.3591994
NOC2L 0.174385897 0.3655440 0.032410862 0.4276593
PLEKHN1 0.012890490 0.6765148 0.020947776 0.2896469
HES4 0.056184717 0.7425376 0.080004910 0.1212727
ISG15 0.001543405 0.7244953 0.000000000 0.2739613
AGRN 0.067891076 0.5041366 0.008078234 0.4198941vp<- sortCols(varpart)plotPercentBars(vp[1:10,])
plotVarPart(vp)
#do the genes most effected by individual match what I did in limma?
vp<- vp[order(vp$ind, decreasing=T),]
head(vp, 30) cluster batch ind Residuals
EIF1AY 0.005626678 3.402155e-04 0.9672310 0.02680213
TYW3 0.013442582 5.773287e-04 0.9661968 0.01978331
CRYZ 0.017399160 0.000000e+00 0.9368647 0.04573617
DDX3Y 0.012445126 0.000000e+00 0.9340323 0.05352254
CAT 0.014666341 4.622713e-03 0.9275191 0.05319184
USP9Y 0.020107149 9.209765e-12 0.9181598 0.06173304
AC004556.3 0.024996396 1.205926e-02 0.9055863 0.05735805
RWDD2B 0.009618339 0.000000e+00 0.8811817 0.10919994
USP51 0.069860656 0.000000e+00 0.8578907 0.07224866
ZNF280D 0.022974798 0.000000e+00 0.8508629 0.12616232
TRIM61 0.032219604 7.742238e-03 0.8474824 0.11255579
RRAGB 0.079455352 2.782076e-03 0.8324096 0.08535296
TAF9B 0.009462544 1.579630e-02 0.8214840 0.15325717
TSPYL5 0.006692945 1.528913e-03 0.8105076 0.18127052
ZNF248 0.080841557 4.014661e-03 0.8096089 0.10553485
MAGEH1 0.120542252 1.512701e-02 0.8057652 0.05856557
IAH1 0.125646136 7.014865e-03 0.7701525 0.09718649
ZNF717 0.114235212 4.326133e-03 0.7531180 0.12832069
CHCHD2 0.037735370 9.492234e-04 0.7521018 0.20921362
PRKY 0.065298335 0.000000e+00 0.7443822 0.19031943
ZNF300 0.119896977 0.000000e+00 0.7356872 0.14441586
QPCT 0.171930081 3.154149e-03 0.7315721 0.09334372
THOC3 0.130306729 7.739102e-03 0.7243993 0.13755488
PNPLA4 0.084304872 3.771940e-02 0.6925623 0.18541338
PSMD4 0.148410864 1.459373e-02 0.6802351 0.15676034
NDUFS2 0.191017425 4.793422e-03 0.6754137 0.12877542
MRPS14 0.087699622 0.000000e+00 0.6725741 0.23972631
HCCS 0.159950461 1.085295e-02 0.6682412 0.16095543
RNF187 0.185271155 5.000910e-02 0.6665409 0.09817883
SP5 0.197786890 1.948890e-02 0.6648352 0.11788900#do the genes most effected by batch match what I did in limma?
vp<- vp[order(vp$batch, decreasing=T),]
head(vp, 30) cluster batch ind Residuals
EEF1A1 0.118881702 0.7608723 4.224254e-02 0.07800348
AP001267.5 0.022681885 0.7570875 6.269668e-02 0.15753397
SF3A2 0.044652973 0.7236153 8.101709e-02 0.15071467
CLIC1 0.125575169 0.7064204 4.242503e-02 0.12557936
LRRC75A 0.160793175 0.7059933 1.723509e-02 0.11597839
MED21 0.100052607 0.6963202 7.568463e-03 0.19605875
SMARCB1 0.125063186 0.6909358 2.228995e-02 0.16171103
TMED2 0.125259087 0.6728219 6.759290e-03 0.19515977
TBL1XR1 0.198047469 0.6622475 0.000000e+00 0.13970507
CAPZA1 0.232415928 0.6517497 2.738789e-03 0.11309561
TMEM167A 0.105175576 0.6468470 3.976720e-02 0.20821026
PAN3 0.009489627 0.6387656 2.851908e-03 0.34889283
EIF4E 0.205831077 0.6332068 7.180814e-04 0.16024402
PRPF31 0.152856017 0.6324144 0.000000e+00 0.21472954
LYPLA2 0.150251072 0.6250772 0.000000e+00 0.22467170
UBE2W 0.108088751 0.6226187 2.470642e-04 0.26904549
USP14 0.169546731 0.6205821 3.599020e-03 0.20627216
MRPL42 0.217068791 0.6119925 6.445767e-02 0.10648105
MOB4 0.160239010 0.6106746 1.022359e-02 0.21886282
PPP1CB 0.300515015 0.6101994 1.401815e-11 0.08928555
LSM2 0.105018382 0.6059968 3.138530e-02 0.25759953
PUF60 0.154885935 0.6015040 2.569950e-03 0.24104010
RSL24D1 0.216208399 0.5975371 4.357804e-02 0.14267643
ZFAND6 0.182995659 0.5926291 8.838292e-03 0.21553692
STRN3 0.078815981 0.5884615 1.903838e-02 0.31368416
PSMA4 0.242621965 0.5793895 9.133403e-03 0.16885510
PSMD9 0.117222341 0.5765497 8.732629e-03 0.29749532
SLC25A6 0.264318133 0.5724181 5.893239e-03 0.15737056
H3F3A 0.088910730 0.5663577 2.298671e-01 0.11486439
QSER1 0.201169253 0.5658483 1.602867e-03 0.23137958summary(vp$ind) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.009717 0.036170 0.068566 0.084673 0.967231 summary(vp$batch) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.01024 0.03899 0.07405 0.10358 0.76087 #genes for which individual contributes more to variance than batch?
vp.indgreaterthanbatch<- vp[vp$ind>vp$batch,]
dim(vp.indgreaterthanbatch) #vp greater than batch for 5266 out of 11356[1] 5371 4head(vp.indgreaterthanbatch, 20) cluster batch ind Residuals
MORF4L2 0.17843522 0.3327123 0.3378180 0.15103449
TSNAX 0.12918228 0.3070578 0.4025798 0.16118007
SF3B4 0.17863159 0.2900567 0.3719349 0.15937680
MDM4 0.22607053 0.2801788 0.3750709 0.11867977
CDC73 0.09565262 0.2722614 0.4336635 0.19842255
SNAP47 0.03690812 0.2688145 0.4342896 0.25998775
TOR1AIP2 0.27720279 0.2542215 0.2583132 0.21026251
EIF2S3 0.28122012 0.2435293 0.3423216 0.13292894
ZNF678 0.24764137 0.2430320 0.2438401 0.26548659
RAP2C 0.25403008 0.2412634 0.3416177 0.16308876
PRCC 0.19571340 0.2403366 0.3180580 0.24589201
PRKAB1 0.01266803 0.2320594 0.2321029 0.52316968
PYGO2 0.16608234 0.2302998 0.2354617 0.36815616
LBR 0.44150482 0.2290209 0.2357314 0.09374283
ZNF670 0.15327444 0.2284893 0.3535837 0.26465254
GAPDH 0.30775719 0.2281483 0.3270470 0.13704743
TPM3 0.37327737 0.2266752 0.2641376 0.13590981
MAP3K12 0.10384284 0.2264864 0.2277812 0.44188955
PFKFB4 0.27023533 0.2260483 0.2436320 0.26008444
SMG9 0.14772442 0.2242687 0.3880564 0.23995047
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_3.3.2
[7] edgeR_3.28.1 limma_3.42.2 dplyr_1.0.0
[10] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 locfit_1.5-9.4 here_0.1-11
[4] lattice_0.20-38 prettyunits_1.1.1 gtools_3.8.2
[7] rprojroot_1.3-2 digest_0.6.25 plyr_1.8.6
[10] R6_2.4.1 backports_1.1.8 evaluate_0.14
[13] pillar_1.4.6 gplots_3.0.4 rlang_0.4.7
[16] progress_1.2.2 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_0.3 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_3.0.3
[37] codetools_0.2-16 crayon_1.3.4 withr_2.2.0
[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_1.5
[49] KernSmooth_2.23-15 stringi_1.4.6 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_0.0.2 vctrs_0.3.2 boot_1.3-23
[61] iterators_1.0.12 tools_3.6.1 glue_1.4.1
[64] purrr_0.3.4 hms_0.5.3 pbkrtest_0.4-8.6
[67] yaml_2.2.1 colorspace_1.4-1 caTools_1.18.0
[70] knitr_1.29