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.0.5'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)
vAn object of class "EList"
$targets
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153 more rows ...
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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 ...
$weights
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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,])
plotVarPart(vp)
#do the genes most effected by individual match what I did in limma?
vp<- vp[order(vp$ind, decreasing=T),]
head(vp, 30) cluster batch ind Residuals
EIF1AY 0.006056199 1.630896e-04 0.9687224 0.02505827
TYW3 0.016044549 2.632478e-04 0.9634700 0.02022221
DDX3Y 0.007466603 0.000000e+00 0.9420062 0.05052720
CRYZ 0.020581155 0.000000e+00 0.9366369 0.04278189
USP9Y 0.010607063 0.000000e+00 0.9319171 0.05747579
CAT 0.016701539 3.759428e-03 0.9291598 0.05037924
AC004556.3 0.025558899 1.332542e-02 0.9072925 0.05382319
USP51 0.062809732 0.000000e+00 0.8702684 0.06692190
RRAGB 0.060873141 2.176313e-03 0.8658992 0.07105134
ZNF248 0.034672597 3.893244e-03 0.8617221 0.09971210
ZNF280D 0.024247071 0.000000e+00 0.8600373 0.11571565
TRIM61 0.025738022 1.041216e-02 0.8508388 0.11301099
MAGEH1 0.112875303 1.586753e-02 0.8278961 0.04336102
TSPYL5 0.009575607 1.763938e-03 0.8227102 0.16595021
TAF9B 0.007771881 2.113038e-02 0.8197301 0.15136760
IAH1 0.098476663 5.994039e-03 0.8053270 0.09020233
ZNF717 0.081245670 4.325480e-03 0.7874866 0.12694225
PRKY 0.058114147 1.638350e-10 0.7639476 0.17793821
ZNF300 0.098065748 0.000000e+00 0.7563901 0.14554419
CHCHD2 0.026112556 0.000000e+00 0.7423313 0.23155617
THOC3 0.132757376 8.437761e-03 0.7111300 0.14767490
QPCT 0.196648213 3.577643e-03 0.7076413 0.09213289
MRPS14 0.071026852 0.000000e+00 0.6961753 0.23279783
HEPH 0.169047777 2.303834e-02 0.6922256 0.11568833
NDUFS2 0.169031809 1.406084e-03 0.6868309 0.14273117
PSMD4 0.122100637 1.693527e-02 0.6820493 0.17891483
PNPLA4 0.091558726 4.426092e-02 0.6810775 0.18310281
HAX1 0.125717228 5.096895e-02 0.6766626 0.14665120
SP5 0.194588213 2.094276e-02 0.6705651 0.11390396
INPP5F 0.063949131 5.384900e-02 0.6693502 0.21285166#do the genes most effected by batch match what I did in limma?
vp<- vp[order(vp$batch, decreasing=T),]
head(vp, 30) cluster batch ind 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$ind) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.008912 0.036037 0.069530 0.086874 0.968722 summary(vp$batch) 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$ind>vp$batch,]
dim(vp.indgreaterthanbatch) #vp greater than batch for 5266 out of 11356[1] 5203 4head(vp.indgreaterthanbatch, 20) cluster batch ind 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 0.2557017
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