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.1'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$norm.factors) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.6381 0.9201 0.9755 1.0134 1.0313 1.8959 design<- model.matrix(~submerged$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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231 more rows ...
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HES4 7.309168 5.287009 7.024283
16.Batch1.SNG-NA18511 16.Batch1.SNG-NA18858 16.Batch1.SNG-NA19160
SAMD11 3.537328 4.741345 2.819310
NOC2L 6.810347 5.963737 6.906773
KLHL17 3.537328 3.156382 4.404273
PLEKHN1 1.952366 3.156382 2.819310
HES4 3.537328 3.156382 4.404273
16.Batch2.SNG-NA18511 16.Batch2.SNG-NA18858 16.Batch2.SNG-NA19160
SAMD11 3.230132 1.631713 4.4387383
NOC2L 6.261610 5.538603 6.0039375
KLHL17 1.950024 3.216675 3.0719559
PLEKHN1 2.602100 1.631713 0.4089909
HES4 3.534986 5.091144 3.3475904
16.Batch3.SNG-NA18511 16.Batch3.SNG-NA18858 16.Batch3.SNG-NA19160
SAMD11 1.564902 1.813825 3.650348
NOC2L 6.238135 4.983750 6.149672
KLHL17 2.878562 1.813825 2.839618
PLEKHN1 1.793171 3.398787 2.194283
HES4 4.743958 3.398787 3.175667
17.Batch1.SNG-NA18511 17.Batch1.SNG-NA18858 17.Batch1.SNG-NA19160
SAMD11 2.236570 3.102061 2.060245
NOC2L 6.607593 6.375080 6.754506
KLHL17 2.623593 3.839027 1.212248
PLEKHN1 -1.463869 1.517099 -1.109680
HES4 8.389440 6.802501 7.829899
17.Batch2.SNG-NA18511 17.Batch2.SNG-NA18858 17.Batch2.SNG-NA19160
SAMD11 2.0020421 3.842817 1.682705
NOC2L 6.1473652 6.050412 6.260973
KLHL17 0.9379118 2.257855 2.419671
PLEKHN1 -2.5215198 1.035462 -1.487220
HES4 7.9633031 5.600247 7.629124
17.Batch3.SNG-NA18511 17.Batch3.SNG-NA18858 17.Batch3.SNG-NA19160
SAMD11 1.178533 1.943548 2.7537095
NOC2L 5.885125 6.467110 6.3923099
KLHL17 1.178533 1.943548 2.1016328
PLEKHN1 -1.991392 1.943548 -0.7057222
HES4 7.210732 5.643988 7.3331968
18.Batch1.SNG-NA18511 18.Batch1.SNG-NA18858 18.Batch1.SNG-NA19160
SAMD11 5.211612 3.652666 4.17572
NOC2L 6.434005 5.974594 4.17572
KLHL17 4.363615 3.652666 4.17572
PLEKHN1 2.041687 3.652666 4.17572
HES4 4.363615 3.652666 4.17572
18.Batch2.SNG-NA18511 18.Batch2.SNG-NA18858 18.Batch2.SNG-NA19160
SAMD11 3.066308 3.857087 5.549440
NOC2L 6.339326 6.179015 6.611368
KLHL17 3.803274 3.857087 3.964478
PLEKHN1 3.066308 3.857087 2.038479
HES4 5.025666 6.664441 5.208404
18.Batch3.SNG-NA18511 18.Batch3.SNG-NA18858 18.Batch3.SNG-NA19160
SAMD11 3.268177 3.514799 5.004664
NOC2L 6.509715 5.099762 6.436892
KLHL17 3.394626 5.099762 3.458696
PLEKHN1 1.444208 3.514799 1.984765
HES4 5.832807 6.974231 5.212871
19.Batch1.SNG-NA18511 19.Batch1.SNG-NA18858 19.Batch1.SNG-NA19160
SAMD11 3.900034 1.725509 2.9813378
NOC2L 6.015511 5.973437 6.6817775
KLHL17 2.967148 4.532864 0.6594097
PLEKHN1 1.829645 4.047437 2.2443722
HES4 6.353207 7.010912 4.3598494
19.Batch2.SNG-NA18511 19.Batch2.SNG-NA18858 19.Batch2.SNG-NA19160
SAMD11 4.288259 3.148565 3.4413604
NOC2L 6.101491 4.733527 6.0180077
KLHL17 2.618408 3.148565 2.2838191
PLEKHN1 1.480904 3.148565 0.9053075
HES4 6.170203 7.236027 5.2976249
19.Batch3.SNG-NA18511 19.Batch3.SNG-NA18858 19.Batch3.SNG-NA19160
SAMD11 3.666625 3.015927 3.1432359
NOC2L 5.957857 5.337855 6.0236543
KLHL17 1.740626 3.015927 1.2687668
PLEKHN1 1.003660 3.015927 -0.3161957
HES4 4.776250 8.570516 5.7923288
2.Batch1.SNG-NA18511 2.Batch1.SNG-NA18858 2.Batch1.SNG-NA19160
SAMD11 2.743679 3.6168845 2.361112
NOC2L 6.344131 6.0820072 6.248878
KLHL17 2.206504 2.2632475 2.560421
PLEKHN1 -2.041423 -0.9066775 -2.393775
HES4 9.365162 8.0504246 8.885255
2.Batch2.SNG-NA18511 2.Batch2.SNG-NA18858 2.Batch2.SNG-NA19160
SAMD11 2.463453 2.911455 2.5691537
NOC2L 5.990949 5.846660 6.0661088
KLHL17 1.758651 2.656198 2.3913666
PLEKHN1 -3.645072 -2.297998 -0.7320158
HES4 9.210511 7.625329 8.5114982
2.Batch3.SNG-NA18511 2.Batch3.SNG-NA18858 2.Batch3.SNG-NA19160
SAMD11 1.9444289 3.5461850 3.698448
NOC2L 5.8620859 5.7076484 6.004405
KLHL17 0.9605486 2.1056124 1.178192
PLEKHN1 -4.5313045 -0.7017425 -3.069736
HES4 8.6928479 7.3371765 7.645368
20.Batch1.SNG-NA18511 20.Batch1.SNG-NA18858 20.Batch1.SNG-NA19160
SAMD11 5.675671 4.358388 3.173114
NOC2L 6.207781 6.197267 5.495042
KLHL17 1.726655 2.802869 3.173114
PLEKHN1 -1.443270 0.928400 3.173114
HES4 8.241478 7.599253 6.873554
20.Batch2.SNG-NA18511 20.Batch2.SNG-NA18858 20.Batch2.SNG-NA19160
SAMD11 4.997627 3.759448 3.673293
NOC2L 5.820749 5.836643 4.895685
KLHL17 3.619116 2.597177 2.088331
PLEKHN1 1.297187 -1.058175 2.088331
HES4 8.731816 6.974146 6.946312
20.Batch3.SNG-NA18511 20.Batch3.SNG-NA18858 20.Batch3.SNG-NA19160
SAMD11 2.098433 0.3350927 3.225388
NOC2L 5.268358 6.0630132 4.447781
KLHL17 3.683395 2.6570208 1.640426
PLEKHN1 2.098433 0.3350927 1.640426
HES4 7.770858 7.0215932 6.925828
21.Batch1.SNG-NA18511 21.Batch1.SNG-NA18858 21.Batch1.SNG-NA19160
SAMD11 2.3145075 4.6892737 1.566721
NOC2L 5.9840035 5.9593629 6.112914
KLHL17 0.9159581 0.6018109 1.807730
PLEKHN1 -0.9585110 0.6018109 -1.892710
HES4 9.2850597 7.4473009 8.600144
21.Batch2.SNG-NA18511 21.Batch2.SNG-NA18858 21.Batch2.SNG-NA19160
SAMD11 4.197172 6.245438 4.111140
NOC2L 5.782134 6.245438 6.433068
KLHL17 4.197172 3.923510 4.111140
PLEKHN1 4.197172 3.923510 4.111140
HES4 7.367097 3.923510 6.433068
22.Batch1.SNG-NA18511 22.Batch1.SNG-NA18858 22.Batch1.SNG-NA19160
SAMD11 4.974793 4.132295 4.6428307
NOC2L 6.503722 7.191189 6.4271020
KLHL17 2.167438 5.354688 3.3473748
PLEKHN1 2.167438 4.132295 -0.1120569
HES4 5.728153 4.869261 5.3142079
22.Batch2.SNG-NA18511 22.Batch2.SNG-NA18858 22.Batch2.SNG-NA19160
SAMD11 5.5121348 5.436912 4.5497970
NOC2L 6.1318628 5.436912 5.9245418
KLHL17 2.9619378 3.851950 3.3110101
PLEKHN1 0.1545828 3.851950 0.2521164
HES4 5.8825033 3.851950 5.7651860
22.Batch3.SNG-NA18511 22.Batch3.SNG-NA19160 23.Batch1.SNG-NA18511
SAMD11 4.7887442 4.466292 2.876854
NOC2L 6.3737067 6.326012 6.046779
KLHL17 -0.7658446 2.591823 3.166361
PLEKHN1 2.4040804 -2.693579 5.198782
HES4 3.9890429 4.619304 8.186709
23.Batch1.SNG-NA18858 23.Batch1.SNG-NA19160 23.Batch2.SNG-NA18511
SAMD11 2.745404 4.353028 5.981564
NOC2L 5.372187 5.731539 5.244598
KLHL17 2.745404 3.905569 2.922670
PLEKHN1 5.032981 5.849859 4.507633
HES4 7.045959 6.921871 6.195689
23.Batch2.SNG-NA18858 23.Batch2.SNG-NA19160 23.Batch3.SNG-NA18511
SAMD11 3.950859 4.370001 4.947105
NOC2L 5.848979 5.927218 5.454065
KLHL17 3.298782 2.410643 2.284140
PLEKHN1 4.739355 4.285112 4.158609
HES4 7.232894 6.440390 4.947105
23.Batch3.SNG-NA18858 23.Batch3.SNG-NA19160 24.Batch1.SNG-NA18511
SAMD11 3.010554 5.415915 2.4266342
NOC2L 5.984559 5.504724 6.1270739
KLHL17 3.010554 2.245990 2.9120610
PLEKHN1 5.412653 3.962197 0.1047061
HES4 7.023378 6.008490 7.3238746
24.Batch1.SNG-NA18858 24.Batch1.SNG-NA19160 24.Batch2.SNG-NA18511
SAMD11 3.253443 1.7642929 0.2611045
NOC2L 5.575371 5.8174043 6.1437476
KLHL17 4.838405 1.7642929 3.0684594
PLEKHN1 3.253443 -0.5576352 0.2611045
HES4 6.060797 7.0348219 6.0939945
24.Batch2.SNG-NA19160 24.Batch3.SNG-NA18511 24.Batch3.SNG-NA19160
SAMD11 -0.8068596 -0.05698745 0.78768386
NOC2L 5.7930533 5.55772240 5.70416051
KLHL17 1.5150685 -0.05698745 1.92518739
PLEKHN1 -0.8068596 -0.05698745 0.05071827
HES4 6.8584764 6.99829499 6.44874934
25.Batch1.SNG-NA18858 25.Batch1.SNG-NA19160 25.Batch2.SNG-NA18511
SAMD11 4.838228 5.926917 3.855911
NOC2L 6.801166 6.663882 6.177839
KLHL17 3.587684 5.926917 3.855911
PLEKHN1 1.051632 4.341954 3.855911
HES4 5.672218 4.341954 5.440873
25.Batch2.SNG-NA18858 25.Batch2.SNG-NA19160 25.Batch3.SNG-NA18511
SAMD11 4.314452 3.873682 4.102044
NOC2L 6.196396 6.681037 6.370533
KLHL17 3.577486 5.458644 3.449967
PLEKHN1 1.992524 5.458644 2.964540
HES4 5.334916 3.873682 6.315037
25.Batch3.SNG-NA18858 25.Batch3.SNG-NA19160 26.Batch1.SNG-NA18858
SAMD11 3.621701 5.042221 1.9140691
NOC2L 5.910271 5.959759 6.2117497
KLHL17 1.951850 4.194224 2.6510347
PLEKHN1 -1.507582 1.872296 0.3291066
HES4 5.286834 4.194224 8.2339921
26.Batch2.SNG-NA18858 26.Batch3.SNG-NA18858 27.Batch1.SNG-NA18511
SAMD11 0.2360877 2.4858969 4.128298
NOC2L 6.1668250 5.9453285 6.630798
KLHL17 0.2360877 1.7489313 2.543335
PLEKHN1 0.2360877 0.1639688 2.543335
HES4 8.1844549 8.1355124 4.865264
27.Batch1.SNG-NA19160 27.Batch2.SNG-NA18511 27.Batch2.SNG-NA19160
SAMD11 3.1520027 3.071536 5.423657
NOC2L 6.1625719 6.241461 6.374747
KLHL17 1.8718948 3.071536 3.101729
PLEKHN1 -0.9354601 3.071536 1.516766
HES4 5.8847188 7.929517 1.516766
27.Batch3.SNG-NA18511 27.Batch3.SNG-NA19160 3.Batch1.SNG-NA18511
SAMD11 2.012473 4.0515211 1.881528
NOC2L 5.182398 6.0839426 6.270647
KLHL17 2.012473 2.9140176 2.288154
PLEKHN1 2.012473 0.5920895 -1.222808
HES4 4.819828 5.5462858 9.199046
3.Batch1.SNG-NA18858 3.Batch1.SNG-NA19160 3.Batch2.SNG-NA18511
SAMD11 3.020678 3.319460 1.457331
NOC2L 6.046213 6.051328 6.012634
KLHL17 2.535251 2.093071 1.585655
PLEKHN1 1.798285 -1.423504 -3.968934
HES4 7.451728 8.794453 8.891183
3.Batch2.SNG-NA18858 3.Batch2.SNG-NA19160 3.Batch3.SNG-NA18511
SAMD11 1.653223 2.192074 2.152178
NOC2L 5.494525 5.997234 5.991641
KLHL17 1.942729 2.418844 1.319906
PLEKHN1 -1.516702 -1.777553 -1.230291
HES4 7.344385 8.502058 8.175154
3.Batch3.SNG-NA18858 3.Batch3.SNG-NA19160 4.Batch1.SNG-NA18511
SAMD11 2.3774221 4.026152 2.3532720
NOC2L 5.8368537 6.191111 6.2443737
KLHL17 3.4769578 2.555523 2.6692995
PLEKHN1 -0.4299328 -2.573760 0.1979938
HES4 6.9009841 7.577256 5.5297250
4.Batch1.SNG-NA18858 4.Batch1.SNG-NA19160 4.Batch2.SNG-NA18511
SAMD11 3.4123510 3.138851 2.0219482
NOC2L 6.4378861 6.261547 5.6225075
KLHL17 2.9269241 2.605235 2.2532737
PLEKHN1 0.6049961 -1.095205 -0.1796857
HES4 6.2774214 5.973818 4.7557741
4.Batch2.SNG-NA18858 4.Batch2.SNG-NA19160 4.Batch3.SNG-NA18511
SAMD11 4.152933 2.2832796 2.127780
NOC2L 5.737895 5.9588447 5.918261
KLHL17 2.567970 1.9784251 1.456403
PLEKHN1 2.567970 -0.2345687 -1.713522
HES4 4.152933 5.0373187 3.756798
4.Batch3.SNG-NA18858 4.Batch3.SNG-NA19160 5.Batch1.SNG-NA18511
SAMD11 2.485509 2.1873709 3.735063
NOC2L 5.944940 5.9733943 6.168022
KLHL17 4.070471 1.9748262 3.735063
PLEKHN1 2.485509 -0.8325287 2.998097
HES4 5.292864 4.9833882 6.622588
5.Batch1.SNG-NA18858 5.Batch1.SNG-NA19160 5.Batch2.SNG-NA18511
SAMD11 1.803949 2.283655 3.314847
NOC2L 6.393778 5.743087 6.419183
KLHL17 3.692528 3.020621 2.577881
PLEKHN1 1.452477 0.698693 2.577881
HES4 5.557392 4.399133 5.240846
5.Batch2.SNG-NA18858 5.Batch2.SNG-NA19160 5.Batch3.SNG-NA18511
SAMD11 1.154396 3.855332 2.952780
NOC2L 5.959932 6.136902 6.457124
KLHL17 2.909912 3.084814 2.952780
PLEKHN1 1.687520 1.319279 2.516681
HES4 5.708624 5.063440 5.742101
5.Batch3.SNG-NA18858 5.Batch3.SNG-NA19160 6.Batch1.SNG-NA18511
SAMD11 0.9469797 3.0035250 4.077737237
NOC2L 5.8427298 6.1425890 6.180098955
KLHL17 2.8414866 2.5180981 2.797629318
PLEKHN1 1.2161664 0.1961701 -0.009725604
HES4 5.3741045 4.7197320 8.613546253
6.Batch1.SNG-NA18858 6.Batch1.SNG-NA19160 6.Batch2.SNG-NA18511
SAMD11 4.634032 1.8940007 2.929009
NOC2L 6.117355 6.0881152 5.972578
KLHL17 3.267250 3.2033288 2.815798
PLEKHN1 1.341251 -0.3076331 -1.213949
HES4 5.495056 6.6946193 7.162538
6.Batch2.SNG-NA18858 6.Batch2.SNG-NA19160 6.Batch3.SNG-NA18511
SAMD11 3.220251 2.3798168 2.563337
NOC2L 5.642873 5.7918455 5.865274
KLHL17 2.775949 2.7208537 2.098934
PLEKHN1 1.489068 0.5884034 -2.619884
HES4 5.603435 5.0326219 5.670134
6.Batch3.SNG-NA18858 6.Batch3.SNG-NA19160 7.Batch1.SNG-NA18511
SAMD11 2.811920 2.621884 3.898300
NOC2L 5.981845 6.103270 6.836899
KLHL17 1.433409 2.177582 4.635266
PLEKHN1 1.918836 -1.431227 2.313337
HES4 5.088761 5.917501 7.670890
7.Batch1.SNG-NA18858 7.Batch1.SNG-NA19160 7.Batch2.SNG-NA18511
SAMD11 1.551764 3.386048 3.477590
NOC2L 6.690013 4.971010 6.109858
KLHL17 3.463891 3.386048 3.963017
PLEKHN1 2.037191 4.971010 1.155662
HES4 6.901618 5.707976 7.869907
7.Batch2.SNG-NA18858 7.Batch2.SNG-NA19160 7.Batch3.SNG-NA18511
SAMD11 0.5307336 5.214150 2.351542
NOC2L 6.2031589 3.629187 6.391956
KLHL17 3.1847377 5.214150 2.351542
PLEKHN1 1.4876648 3.629187 1.699465
HES4 5.8334397 7.716650 6.875104
7.Batch3.SNG-NA18858 7.Batch3.SNG-NA19160 8.Batch1.SNG-NA18511
SAMD11 1.185680 3.788896 4.7670255
NOC2L 6.176635 6.620296 6.5512968
KLHL17 2.873736 3.401873 2.9756121
PLEKHN1 1.366253 3.401873 -0.7248276
HES4 6.152234 6.469617 6.8972242
8.Batch1.SNG-NA18858 8.Batch1.SNG-NA19160 8.Batch2.SNG-NA18511
SAMD11 3.8022873 2.961660 5.9768392
NOC2L 6.5495213 6.223111 6.3394092
KLHL17 3.8022873 1.946393 0.3621293
PLEKHN1 0.9949324 -1.286268 0.3621293
HES4 6.9256697 6.752651 6.1950193
8.Batch2.SNG-NA18858 8.Batch2.SNG-NA19160 8.Batch3.SNG-NA18511
SAMD11 3.120364 3.219931 2.5485723
NOC2L 4.705326 6.033425 6.1506083
KLHL17 3.120364 2.011823 -0.2587826
PLEKHN1 3.120364 -1.261196 -0.2587826
HES4 5.442292 6.733158 5.1674822
8.Batch3.SNG-NA18858 8.Batch3.SNG-NA19160 9.Batch1.SNG-NA18511
SAMD11 2.046844 4.716863 2.801452
NOC2L 6.439161 5.908644 5.908142
KLHL17 3.631806 2.259734 2.990930
PLEKHN1 2.046844 -1.251228 -3.031438
HES4 5.953734 5.424024 9.135039
9.Batch1.SNG-NA18858 9.Batch1.SNG-NA19160 9.Batch2.SNG-NA18511
SAMD11 2.6719645 3.0386564 3.283467
NOC2L 5.7472526 6.0021305 5.897133
KLHL17 2.6719645 2.6760863 1.925276
PLEKHN1 -0.1353904 -0.8682342 -3.856084
HES4 7.8361532 8.6663929 8.945422
9.Batch2.SNG-NA18858 9.Batch2.SNG-NA19160 9.Batch3.SNG-NA18511
SAMD11 2.359283 1.490751 2.1063312
NOC2L 5.400311 5.565098 5.6160580
KLHL17 2.746307 2.744507 0.6556698
PLEKHN1 -1.341156 -2.209689 -3.2512208
HES4 7.089296 8.326558 8.3930862
9.Batch3.SNG-NA18858 9.Batch3.SNG-NA19160
SAMD11 3.4904453 3.864056
NOC2L 6.0406424 5.710347
KLHL17 2.2680529 1.768899
PLEKHN1 0.6830904 -1.690533
HES4 6.5159804 7.476886
11435 more rows ...
$weights
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[,15] [,16] [,17] [,18] [,19] [,20] [,21]
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[1,] 1.3598124 2.4740914 3.0918833 0.4051218 2.0142742
[2,] 4.1821391 5.6980011 8.4417539 2.3124333 4.8979144
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11435 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$cluster22 submerged$samples$cluster23
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster24 submerged$samples$cluster25
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$samples$cluster26 submerged$samples$cluster27
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
submerged$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$indNA19160
1 0
2 0
3 1
4 0
5 0
231 more rows ...form<- ~ (1|cluster) + (1|batch) + (1|ind)remove(cpm)
remove(lcpm)varpart<- suppressWarnings(fitExtractVarPartModel(v, form, submerged$samples))head(varpart) batch cluster ind Residuals
SAMD11 0.008134020 0.5309650 0.08328585 0.3776152
NOC2L 0.210459072 0.3399214 0.01997909 0.4296404
KLHL17 0.113124551 0.4733206 0.05042636 0.3631285
PLEKHN1 0.017561041 0.6116619 0.03892802 0.3318491
HES4 0.066522890 0.7151827 0.07791295 0.1403815
ISG15 0.001938057 0.7361390 0.00000000 0.2619230vp<- sortCols(varpart)plotPercentBars(vp[1:10,])
| Version | Author | Date |
|---|---|---|
| 5536dae | KLRhodes | 2020-08-31 |
colnames(vp)<- c("Cluster", "Replicate", "Individual", "Residuals")
V<- plotVarPart(vp)
V
| Version | Author | Date |
|---|---|---|
| 5536dae | KLRhodes | 2020-08-31 |
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig4_VarPartRes1.png", width=8, height=5, units= "in", res=1080)
V
dev.off()#do the genes most effected by individual match what I did in limma?
vp<- vp[order(vp$Individual, decreasing=T),]
head(vp, 30) Cluster Replicate Individual Residuals
TYW3 0.012673254 3.194716e-11 0.9626331 0.02469367
EIF1AY 0.007990717 2.862921e-04 0.9581962 0.03352684
CRYZ 0.023990563 0.000000e+00 0.9233626 0.05264679
DDX3Y 0.017819828 0.000000e+00 0.9181737 0.06400645
CAT 0.019923713 5.494305e-03 0.9109269 0.06365504
USP9Y 0.020347845 2.677458e-11 0.8973358 0.08231632
AC004556.3 0.031422589 1.564283e-02 0.8783546 0.07457996
RWDD2B 0.020407214 2.851418e-11 0.8486486 0.13094422
USP51 0.073114130 1.923581e-11 0.8400385 0.08684740
ZNF280D 0.035580567 0.000000e+00 0.8239942 0.14042524
TAF9B 0.005534833 1.420684e-02 0.8224649 0.15779342
TRIM61 0.050591435 1.043082e-02 0.8189257 0.12005204
RRAGB 0.070846429 6.026204e-03 0.8110918 0.11203554
IAH1 0.092588865 5.835641e-03 0.7999390 0.10163646
MAGEH1 0.127273192 1.733048e-02 0.7923943 0.06300202
TSPYL5 0.020730890 1.610238e-03 0.7918744 0.18578446
ZNF248 0.089006498 5.025846e-03 0.7734273 0.13254038
CHCHD2 0.027792151 4.653040e-03 0.7459806 0.22157425
ZNF300 0.121672426 1.097822e-10 0.7249267 0.15340082
ZNF717 0.137789144 4.200968e-03 0.7138058 0.14420405
NDUFS2 0.140961352 0.000000e+00 0.7131410 0.14589766
PRKY 0.065456192 9.472876e-11 0.6957758 0.23876801
MRPS14 0.039223597 0.000000e+00 0.6906343 0.27014212
QPCT 0.207580995 5.654361e-03 0.6842109 0.10255375
MRPS21 0.168356845 1.609365e-03 0.6782824 0.15175134
PSMD4 0.103423859 2.170627e-02 0.6726663 0.20220355
PNPLA4 0.074041297 5.257533e-02 0.6693239 0.20405947
RNF187 0.143695026 7.541818e-02 0.6685190 0.11236782
FAM199X 0.032805358 1.079154e-01 0.6545404 0.20473888
THOC3 0.174798442 1.233453e-02 0.6481008 0.16476625#do the genes most effected by batch match what I did in limma?
vp<- vp[order(vp$Replicate, decreasing=T),]
head(vp, 30) Cluster Replicate Individual Residuals
EEF1A1 0.11974329 0.7673963 4.086558e-02 0.07199486
AP001267.5 0.02406530 0.7411943 5.611633e-02 0.17862406
SMARCB1 0.09433392 0.7156048 2.210116e-02 0.16796011
SF3A2 0.05271636 0.7072443 6.807192e-02 0.17196747
CAPZA1 0.15336072 0.6989011 1.662478e-03 0.14607566
LRRC75A 0.17849391 0.6776789 1.534714e-02 0.12848009
CLIC1 0.13565745 0.6669713 4.359335e-02 0.15377787
TBL1XR1 0.15341667 0.6511418 2.215175e-10 0.19544149
PPP1CB 0.26985603 0.6342063 1.263794e-10 0.09593764
USP14 0.09189205 0.6260121 9.721179e-03 0.27237465
MED21 0.16331737 0.5951325 1.454638e-02 0.22700372
TMED2 0.15588091 0.5948383 5.410093e-03 0.24387074
MRPL42 0.21915778 0.5851833 7.349005e-02 0.12216891
EIF4E 0.21595226 0.5835840 1.897080e-03 0.19856665
TMEM167A 0.12647804 0.5832594 4.326011e-02 0.24700247
PUF60 0.17097362 0.5748966 2.421143e-03 0.25170860
PRPF31 0.19135608 0.5680119 0.000000e+00 0.24063200
PAN3 0.04293099 0.5673294 6.606537e-03 0.38313309
TRA2A 0.20536387 0.5651448 2.978803e-02 0.19970334
PSMA4 0.20686382 0.5636326 1.298156e-02 0.21652197
H3F3A 0.08998637 0.5635909 2.228967e-01 0.12352603
HSF1 0.03488441 0.5616304 4.013144e-03 0.39947205
SLC25A6 0.28242571 0.5555418 5.546147e-03 0.15648636
RSL24D1 0.21869074 0.5540410 5.417895e-02 0.17308934
LYPLA2 0.23218268 0.5506040 0.000000e+00 0.21721332
LSM2 0.12646275 0.5500372 3.722248e-02 0.28627761
CAPZA2 0.23838649 0.5477628 2.372182e-02 0.19012885
QSER1 0.21927223 0.5380249 6.138884e-03 0.23656401
PCNP 0.21460607 0.5343011 0.000000e+00 0.25109285
ZFAND6 0.25047728 0.5339575 6.888443e-03 0.20867680summary(vp$Individual) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.009085 0.033515 0.064427 0.079708 0.962633 summary(vp$Replicate) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.01022 0.03631 0.06834 0.09430 0.76740 #genes for which individual contributes more to variance than batch?
vp.indgreaterthanbatch<- vp[vp$Individual>vp$Replicate,]
dim(vp.indgreaterthanbatch) #vp greater than batch for 5266 out of 11356[1] 5458 4head(vp.indgreaterthanbatch, 20) Cluster Replicate Individual Residuals
MORF4L2 0.13368671 0.3305262 0.3748252 0.1609618
RNF2 0.05470967 0.3270797 0.3656271 0.2525835
CHM 0.04738132 0.3246448 0.3576527 0.2703212
SF3B4 0.18631050 0.3201600 0.3353219 0.1582076
SNAP47 0.01531640 0.3015715 0.3953149 0.2877971
ZNF281 0.18298881 0.3005685 0.3088606 0.2075821
GAPDH 0.24148707 0.2946808 0.3247554 0.1390767
TSNAX 0.09686234 0.2752320 0.4217176 0.2061881
MDM4 0.15786840 0.2690496 0.4317879 0.1412941
PRCC 0.19296982 0.2628047 0.2843338 0.2598916
UHMK1 0.09206190 0.2626274 0.2893352 0.3559755
TPI1 0.24538846 0.2591399 0.2672175 0.2282542
SCNM1 0.09285215 0.2590271 0.4035077 0.2446131
CDK16 0.14904931 0.2515498 0.4295413 0.1698596
SMG9 0.11170557 0.2398780 0.3800367 0.2683797
B4GALT3 0.19446296 0.2351935 0.3182017 0.2521418
CDC73 0.06846824 0.2277811 0.4339902 0.2697605
ADNP 0.35396235 0.2253128 0.2361214 0.1846035
RAP2C 0.24318596 0.2187700 0.3508156 0.1872284
VEGFB 0.28876666 0.2094572 0.2703386 0.2314376sessionInfo()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