Limma Res 1
Katie Rhodes
5/27/2021
Last updated: 2021-12-13
Checks: 6 1
Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ac6bd8d | KLRhodes | 2021-12-13 | Fix supp files |
| Rmd | fe7fae8 | KLRhodes | 2021-07-05 | wflow_git_commit(c("analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes*", |
| html | 475b623 | KLRhodes | 2021-07-05 | Build site. |
library(Seurat)
library(Matrix)
library(dplyr)
library(edgeR)
library(limma)
library(tibble)choose parameters (integration type, clustering res, min pct threshold)
f<- 'Harmony.Batchindividual'
pct<-0.2
res<- 'SCT_snn_res.1'path<- here::here("output/DGELists/")
dge<- readRDS(paste0(path,"Pseudobulk_dge_",f, "_", res,"_minPCT",pct,".rds"))genes.ribo <- grep('^RP',rownames(dge),value=T)
genes.no.ribo <- rownames(dge)[which(!(rownames(dge) %in% genes.ribo))]
dge$counts <- dge$counts[which(rownames(dge$counts) %in% genes.no.ribo),] #remove ribosomal genesdge<- calcNormFactors(dge, method="TMM")
summary(dge$samples$norm.factors) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.6381 0.9201 0.9755 1.0134 1.0313 1.8959 design<- model.matrix(~0+ dge$samples$cluster + dge$samples$batch + dge$samples$ind)v<- voom(dge, design, plot=TRUE)
| Version | Author | Date |
|---|---|---|
| 475b623 | KLRhodes | 2021-07-05 |
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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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[2,] 3.5291972 1.4936367 9.9607345 8.6259921 3.1592929 6.9220180 9.9729280
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[,232] [,233] [,234] [,235] [,236]
[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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[5,] 8.8338510 12.0896680 17.6259341 4.1075234 9.2791010
11435 more rows ...
$design
dge$samples$cluster0 dge$samples$cluster1 dge$samples$cluster10
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 0
dge$samples$cluster11 dge$samples$cluster12 dge$samples$cluster13
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster14 dge$samples$cluster15 dge$samples$cluster16
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster17 dge$samples$cluster18 dge$samples$cluster19
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster2 dge$samples$cluster20 dge$samples$cluster21
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster22 dge$samples$cluster23 dge$samples$cluster24
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster25 dge$samples$cluster26 dge$samples$cluster27
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster3 dge$samples$cluster4 dge$samples$cluster5
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster6 dge$samples$cluster7 dge$samples$cluster8
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
dge$samples$cluster9 dge$samples$batchBatch2 dge$samples$batchBatch3
1 0 0 0
2 0 0 0
3 0 0 0
4 0 1 0
5 0 1 0
dge$samples$indNA18858 dge$samples$indNA19160
1 0 0
2 1 0
3 0 1
4 0 0
5 1 0
231 more rows ...fit<- lmFit(v,design)nclust<- length(unique(dge$samples$cluster))
contrasts<- NULL
for (i in 1:nclust){
c<- c(rep(-1,nclust),0,0,0,0)
c[i]<- nclust-1
contrasts<- cbind(contrasts, c)
}contrasts c c c c c c c c c c c c c c c c c c c c c c c c
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[24,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 27
[25,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[26,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[27,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[28,] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
[29,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[30,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[31,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[32,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
c c c c
[1,] -1 -1 -1 -1
[2,] -1 -1 -1 -1
[3,] -1 -1 -1 -1
[4,] -1 -1 -1 -1
[5,] -1 -1 -1 -1
[6,] -1 -1 -1 -1
[7,] -1 -1 -1 -1
[8,] -1 -1 -1 -1
[9,] -1 -1 -1 -1
[10,] -1 -1 -1 -1
[11,] -1 -1 -1 -1
[12,] -1 -1 -1 -1
[13,] -1 -1 -1 -1
[14,] -1 -1 -1 -1
[15,] -1 -1 -1 -1
[16,] -1 -1 -1 -1
[17,] -1 -1 -1 -1
[18,] -1 -1 -1 -1
[19,] -1 -1 -1 -1
[20,] -1 -1 -1 -1
[21,] -1 -1 -1 -1
[22,] -1 -1 -1 -1
[23,] -1 -1 -1 -1
[24,] -1 -1 -1 -1
[25,] 27 -1 -1 -1
[26,] -1 27 -1 -1
[27,] -1 -1 27 -1
[28,] -1 -1 -1 27
[29,] 0 0 0 0
[30,] 0 0 0 0
[31,] 0 0 0 0
[32,] 0 0 0 0#first, testing all pairwise cluster comparisons.
fit<- contrasts.fit(fit, contrasts= contrasts)efit<- eBayes(fit)plotSA(efit)
| Version | Author | Date |
|---|---|---|
| 475b623 | KLRhodes | 2021-07-05 |
summary(decideTests(efit)) c c c c c c c c c c c c
Down 3190 3085 2081 3298 3338 2804 2586 4227 2310 4002 2504 3042
NotSig 3602 3424 6327 5285 5512 6750 6485 4013 4808 5226 4527 4130
Up 4648 4931 3032 2857 2590 1886 2369 3200 4322 2212 4409 4268
c c c c c c c c c c c c
Down 4625 2202 2221 1301 2311 2095 1214 797 224 4696 3055 2709
NotSig 3860 7654 7542 7989 5656 6476 6538 9424 10686 4084 5137 4434
Up 2955 1584 1677 2150 3473 2869 3688 1219 530 2660 3248 4297
c c c c
Down 1269 2643 3427 3473
NotSig 7793 4412 5869 4887
Up 2378 4385 2144 3080output.list<- list()
for (i in 1:nclust){
ta<- topTable(efit, coef=i,n=nrow(fit))
output.list[[i]]<- ta
}
listnames<- c(paste0("Cluster", unique(dge$samples$cluster)))
names(output.list)<- as.vector(listnames)#add cluster number to colnames
for (i in 1:nclust){
d<- names(output.list[i])
colnames(output.list[[i]])<- paste(d, colnames(output.list[[i]]), sep=".")
}
all<- left_join(rownames_to_column(output.list[[1]]), rownames_to_column(output.list[[2]]), by= c("rowname" = "rowname"))
for (i in 3:nclust){
all<- left_join(all, rownames_to_column(output.list[[i]]), by= c("rowname" = "rowname"))
}
colnames(all)[1]<- "gene"
write.csv(all, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/NEW_Pseudobulk_Limma_res1_OnevAllTopTables.csv")cluster 19
ta<- topTable(efit, coef=12, sort.by="logFC",n=nrow(fit))
head(ta, n=50) logFC AveExpr t P.Value adj.P.Val B
FGB 278.1245 0.94152756 26.97511 1.317518e-70 1.507241e-67 45.28002
APOC3 277.0118 1.31270428 27.76511 1.094792e-72 2.087404e-69 51.19676
APOA2 276.9414 3.20477255 31.77994 9.418792e-83 1.077510e-78 86.86351
RBP4 274.1492 1.87155010 31.13457 3.437520e-81 1.966261e-77 63.35664
SERPINA1 267.5336 0.32778879 23.66566 1.599894e-61 5.904126e-59 33.26634
FGG 266.8369 0.48271052 23.95285 2.466440e-62 1.085233e-59 35.44379
APOA1 264.2177 3.76994056 29.75466 8.855381e-78 2.532639e-74 91.71255
APOA4 260.6023 1.54602503 27.22725 2.832082e-71 3.599891e-68 52.40194
AHSG 257.7934 1.04826223 26.76211 4.858009e-70 5.052329e-67 44.55285
FGA 255.0472 0.43449708 21.84293 2.891692e-56 7.693247e-54 31.17969
TTR 252.8355 4.16473375 27.72723 1.375054e-72 2.247231e-69 91.75087
APOB 251.8871 1.42555612 26.62907 1.100663e-69 1.049298e-66 47.27848
FABP1 251.6197 0.84738327 22.51442 3.190669e-58 9.605592e-56 36.58195
AFP 248.1621 2.52162484 25.35279 3.149291e-66 2.119288e-63 59.66034
GSTA2 247.7296 0.58753374 23.95606 2.415666e-62 1.085233e-59 35.46199
SMLR1 235.9796 0.19805180 22.64837 1.306991e-58 4.271992e-56 29.23549
S100A14 227.0090 1.94172250 23.90700 3.322023e-62 1.407553e-59 45.54774
G0S2 225.5661 1.83040478 26.43145 3.724433e-69 3.277501e-66 50.58637
LGALS2 224.8574 0.00272381 19.76861 4.489088e-50 5.706130e-48 23.09418
AGT 223.0855 0.42856740 23.76310 8.474051e-62 3.342867e-59 32.28046
HMGCS2 222.1998 0.03567994 21.33278 9.200469e-55 1.949136e-52 25.62171
TF 216.1445 0.63377038 20.98969 9.591472e-54 1.769781e-51 28.72182
HNF4A 215.0345 0.03930273 19.15601 3.309507e-48 3.512938e-46 22.03974
GSTA1 213.9612 0.87529453 23.79259 6.992826e-62 2.857069e-59 35.05014
MTTP 211.2180 0.90229415 21.51413 2.679858e-55 6.011290e-53 32.27625
ASGR2 210.8724 0.13153903 20.93192 1.425196e-53 2.547537e-51 24.87168
AKR1D1 209.1598 0.21379073 20.93617 1.384294e-53 2.513702e-51 24.49740
CTSE 207.8071 -0.11926818 18.46218 4.513868e-46 3.688475e-44 19.29644
MEP1A 207.6987 -0.12715193 18.64076 1.268171e-46 1.124642e-44 19.68760
F2 206.8151 0.01426061 18.76223 5.356662e-47 5.022968e-45 21.04070
SLC39A5 206.4230 0.08339446 20.42951 4.536504e-52 6.919681e-50 23.32341
VTN 206.1911 1.32868753 22.55868 2.375260e-58 7.344046e-56 39.17265
DPYS 204.5108 0.36947445 19.68571 8.015389e-50 9.966962e-48 23.19404
GJB1 203.0073 0.03943133 19.39940 5.966671e-49 6.758977e-47 21.81401
RBP2 201.2084 -0.10556005 17.68797 1.145637e-43 7.943083e-42 17.93102
GLTPD2 200.0401 0.10520321 19.48838 3.194426e-49 3.806691e-47 22.04692
GNRH2 198.7043 0.08694866 18.08918 6.459639e-45 4.893925e-43 20.07085
PRODH2 197.6617 -0.21663878 16.89946 3.382499e-41 1.906196e-39 16.26410
CREB3L3 196.3574 -0.13122805 17.30482 1.806746e-42 1.123325e-40 17.10060
SOAT2 195.4621 -0.25227363 16.43640 9.736088e-40 4.885125e-38 15.64727
LGALS3 195.2012 3.50541585 25.40220 2.305378e-66 1.648345e-63 68.62403
PDZK1 195.1392 0.17915858 18.90890 1.895615e-47 1.837783e-45 21.32504
PLA2G12B 193.8486 0.22600123 19.39939 5.967279e-49 6.758977e-47 21.99323
IHH 193.5684 0.20684028 18.70615 7.973053e-47 7.296939e-45 21.95083
A1CF 193.5002 -0.26630115 15.93090 3.864948e-38 1.775703e-36 14.55904
HPX 193.2169 0.35767274 19.15571 3.316410e-48 3.512938e-46 23.21323
ARG1 190.2648 -0.12438155 17.14747 5.626203e-42 3.369830e-40 16.68415
SULT1E1 189.5555 -0.37959969 15.13899 1.261475e-35 4.716103e-34 12.89873
AMBP 188.7161 0.70071089 19.79135 3.829680e-50 4.922645e-48 24.92792
DPP4 187.8576 1.10957997 21.61451 1.356143e-55 3.300910e-53 31.75920sessionInfo()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] 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] tibble_3.1.5 edgeR_3.28.1 limma_3.42.2 dplyr_1.0.2
[5] Matrix_1.2-18 Seurat_3.2.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_0.1-28
[4] ellipsis_0.3.2 ggridges_0.5.2 rprojroot_2.0.2
[7] fs_1.4.2 spatstat.data_1.4-3 leiden_0.3.3
[10] listenv_0.8.0 npsurv_0.4-0 ggrepel_0.9.0
[13] fansi_0.5.0 codetools_0.2-16 splines_3.6.1
[16] lsei_1.2-0 knitr_1.29 polyclip_1.10-0
[19] jsonlite_1.7.2 ica_1.0-2 cluster_2.1.0
[22] png_0.1-7 uwot_0.1.10 shiny_1.5.0
[25] sctransform_0.2.1 compiler_3.6.1 httr_1.4.2
[28] fastmap_1.0.1 lazyeval_0.2.2 later_1.1.0.1
[31] htmltools_0.5.0 tools_3.6.1 rsvd_1.0.3
[34] igraph_1.2.6 gtable_0.3.0 glue_1.4.2
[37] RANN_2.6.1 reshape2_1.4.4 rappdirs_0.3.3
[40] Rcpp_1.0.6 spatstat_1.64-1 vctrs_0.3.8
[43] ape_5.4-1 nlme_3.1-140 lmtest_0.9-37
[46] xfun_0.16 stringr_1.4.0 globals_0.12.5
[49] mime_0.9 miniUI_0.1.1.1 lifecycle_1.0.1
[52] irlba_2.3.3 goftest_1.2-2 future_1.18.0
[55] MASS_7.3-51.4 zoo_1.8-8 scales_1.1.1
[58] promises_1.1.1 spatstat.utils_1.17-0 parallel_3.6.1
[61] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.20
[64] pbapply_1.4-2 gridExtra_2.3 ggplot2_3.3.5
[67] rpart_4.1-15 stringi_1.5.3 highr_0.8
[70] rlang_0.4.11 pkgconfig_2.0.3 evaluate_0.14
[73] lattice_0.20-38 ROCR_1.0-11 purrr_0.3.4
[76] tensor_1.5 patchwork_1.1.1 htmlwidgets_1.5.1
[79] cowplot_1.1.1 tidyselect_1.1.0 here_0.1-11
[82] RcppAnnoy_0.0.18 plyr_1.8.6 magrittr_2.0.1
[85] R6_2.5.1 generics_0.1.0 pillar_1.6.3
[88] whisker_0.4 mgcv_1.8-28 fitdistrplus_1.0-14
[91] survival_3.2-3 abind_1.4-5 future.apply_1.6.0
[94] crayon_1.4.1 KernSmooth_2.23-15 utf8_1.2.2
[97] plotly_4.9.2.1 rmarkdown_2.3 locfit_1.5-9.4
[100] grid_3.6.1 data.table_1.13.4 git2r_0.26.1
[103] digest_0.6.28 xtable_1.8-4 tidyr_1.1.0
[106] httpuv_1.5.4 munsell_0.5.0 viridisLite_0.4.0
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] 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] tibble_3.1.5 edgeR_3.28.1 limma_3.42.2 dplyr_1.0.2
[5] Matrix_1.2-18 Seurat_3.2.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_0.1-28
[4] ellipsis_0.3.2 ggridges_0.5.2 rprojroot_2.0.2
[7] fs_1.4.2 spatstat.data_1.4-3 leiden_0.3.3
[10] listenv_0.8.0 npsurv_0.4-0 ggrepel_0.9.0
[13] fansi_0.5.0 codetools_0.2-16 splines_3.6.1
[16] lsei_1.2-0 knitr_1.29 polyclip_1.10-0
[19] jsonlite_1.7.2 ica_1.0-2 cluster_2.1.0
[22] png_0.1-7 uwot_0.1.10 shiny_1.5.0
[25] sctransform_0.2.1 compiler_3.6.1 httr_1.4.2
[28] fastmap_1.0.1 lazyeval_0.2.2 later_1.1.0.1
[31] htmltools_0.5.0 tools_3.6.1 rsvd_1.0.3
[34] igraph_1.2.6 gtable_0.3.0 glue_1.4.2
[37] RANN_2.6.1 reshape2_1.4.4 rappdirs_0.3.3
[40] Rcpp_1.0.6 spatstat_1.64-1 vctrs_0.3.8
[43] ape_5.4-1 nlme_3.1-140 lmtest_0.9-37
[46] xfun_0.16 stringr_1.4.0 globals_0.12.5
[49] mime_0.9 miniUI_0.1.1.1 lifecycle_1.0.1
[52] irlba_2.3.3 goftest_1.2-2 future_1.18.0
[55] MASS_7.3-51.4 zoo_1.8-8 scales_1.1.1
[58] promises_1.1.1 spatstat.utils_1.17-0 parallel_3.6.1
[61] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.20
[64] pbapply_1.4-2 gridExtra_2.3 ggplot2_3.3.5
[67] rpart_4.1-15 stringi_1.5.3 highr_0.8
[70] rlang_0.4.11 pkgconfig_2.0.3 evaluate_0.14
[73] lattice_0.20-38 ROCR_1.0-11 purrr_0.3.4
[76] tensor_1.5 patchwork_1.1.1 htmlwidgets_1.5.1
[79] cowplot_1.1.1 tidyselect_1.1.0 here_0.1-11
[82] RcppAnnoy_0.0.18 plyr_1.8.6 magrittr_2.0.1
[85] R6_2.5.1 generics_0.1.0 pillar_1.6.3
[88] whisker_0.4 mgcv_1.8-28 fitdistrplus_1.0-14
[91] survival_3.2-3 abind_1.4-5 future.apply_1.6.0
[94] crayon_1.4.1 KernSmooth_2.23-15 utf8_1.2.2
[97] plotly_4.9.2.1 rmarkdown_2.3 locfit_1.5-9.4
[100] grid_3.6.1 data.table_1.13.4 git2r_0.26.1
[103] digest_0.6.28 xtable_1.8-4 tidyr_1.1.0
[106] httpuv_1.5.4 munsell_0.5.0 viridisLite_0.4.0