Differential Gene Expression Analysis
Last updated: 2025-02-06
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Knit directory: CX5461_Project/
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| Rmd | d31730d | sayanpaul01 | 2025-02-06 | Added DGE Analysis with structured chunks |
📌 Differential Gene Expression Analysis
1️⃣ Load Required Libraries
# Load necessary packages
library(edgeR)Warning: package 'edgeR' was built under R version 4.3.1Warning: package 'limma' was built under R version 4.3.1library(limma)
library(data.table)Warning: package 'data.table' was built under R version 4.3.2library(tidyverse)Warning: package 'tidyverse' was built under R version 4.3.2Warning: package 'ggplot2' was built under R version 4.3.3Warning: package 'tidyr' was built under R version 4.3.3Warning: package 'readr' was built under R version 4.3.3Warning: package 'purrr' was built under R version 4.3.1Warning: package 'dplyr' was built under R version 4.3.2Warning: package 'stringr' was built under R version 4.3.2Warning: package 'lubridate' was built under R version 4.3.1library(ggplot2)
library(dplyr)
library(scales)Warning: package 'scales' was built under R version 4.3.2library(biomaRt)Warning: package 'biomaRt' was built under R version 4.3.2library(Homo.sapiens)Warning: package 'AnnotationDbi' was built under R version 4.3.2Warning: package 'BiocGenerics' was built under R version 4.3.1Warning: package 'Biobase' was built under R version 4.3.1Warning: package 'IRanges' was built under R version 4.3.1Warning: package 'S4Vectors' was built under R version 4.3.1Warning: package 'OrganismDbi' was built under R version 4.3.1Warning: package 'GenomicFeatures' was built under R version 4.3.3Warning: package 'GenomeInfoDb' was built under R version 4.3.3Warning: package 'GenomicRanges' was built under R version 4.3.1📍 Load Data
📌 Normalize and Filter Counts
# ✅ Convert to Log2 CPM
lcpm <- cpm(counts_matrix, log=TRUE)
# ✅ Filter genes based on rowMeans > 0
filcpm_matrix <- subset(lcpm, (rowMeans(lcpm) > 0))
# ✅ Check dimensions of filtered matrix
dim(filcpm_matrix)[1] 14279 108head(filcpm_matrix) MCW_SP_JT_R1_R1.bam MCW_SP_JT_R10_R1.bam MCW_SP_JT_R100_R1.bam
653635 3.499737 3.777758 4.051060
729737 4.018739 3.087263 2.799033
102723897 3.581464 3.765087 4.210151
100132287 3.229725 2.970710 1.422530
102465432 6.910839 6.896427 5.596078
100133331 3.044515 2.902929 1.971919
MCW_SP_JT_R101_R1.bam MCW_SP_JT_R102_R1.bam MCW_SP_JT_R103_R1.bam
653635 3.631547 3.758155 3.982758
729737 3.631547 4.399176 4.292059
102723897 3.707275 3.679166 4.047012
100132287 2.309608 2.553616 2.748309
102465432 5.581451 5.117157 5.156085
100133331 2.674101 2.861516 2.903876
MCW_SP_JT_R104_R1.bam MCW_SP_JT_R105_R1.bam MCW_SP_JT_R106_R1.bam
653635 3.621628 3.730948 4.424852
729737 3.859895 3.335209 2.188740
102723897 3.830959 3.849826 4.383929
100132287 2.223026 1.751824 1.409709
102465432 4.996110 5.818078 5.553327
100133331 2.576789 2.446809 2.020941
MCW_SP_JT_R107_R1.bam MCW_SP_JT_R108_R1.bam MCW_SP_JT_R11_R1.bam
653635 3.457651 3.723735 3.844478
729737 3.451531 4.198994 4.393697
102723897 3.254703 3.969588 3.863450
100132287 1.825924 2.664169 3.400807
102465432 4.426062 4.868680 6.592588
100133331 2.483272 3.001402 3.283624
MCW_SP_JT_R12_R1.bam MCW_SP_JT_R13_R1.bam MCW_SP_JT_R14_R1.bam
653635 3.589909 3.545176 3.328201
729737 3.898580 4.286356 3.861968
102723897 3.574530 3.616862 3.473598
100132287 3.349076 3.344798 3.118157
102465432 6.027172 6.153771 6.533534
100133331 2.806973 3.138844 2.995086
MCW_SP_JT_R15_R1.bam MCW_SP_JT_R16_R1.bam MCW_SP_JT_R17_R1.bam
653635 3.875850 4.296957 3.630656
729737 4.247230 3.357586 4.030829
102723897 3.812616 4.383515 3.316437
100132287 3.414294 2.734813 3.054069
102465432 6.497835 6.103301 6.328824
100133331 3.269659 2.723314 3.129273
MCW_SP_JT_R18_R1.bam MCW_SP_JT_R19_R1.bam MCW_SP_JT_R2_R1.bam
653635 3.164894 3.688006 3.343134
729737 3.969134 3.558796 3.599682
102723897 3.407690 3.551664 3.269066
100132287 3.097226 3.032344 2.839430
102465432 6.414278 5.698763 6.595579
100133331 2.764790 3.241816 2.733250
MCW_SP_JT_R20_R1.bam MCW_SP_JT_R21_R1.bam MCW_SP_JT_R22_R1.bam
653635 3.661566 3.768006 3.691687
729737 3.270063 2.922711 3.627513
102723897 3.433442 3.486337 3.504276
100132287 2.930283 2.832376 3.369519
102465432 6.175604 5.991332 6.385198
100133331 2.994511 2.772902 3.131144
MCW_SP_JT_R23_R1.bam MCW_SP_JT_R24_R1.bam MCW_SP_JT_R25_R1.bam
653635 3.615626 3.375291 3.798182
729737 3.281483 3.625161 3.500438
102723897 3.289137 3.505629 3.521617
100132287 2.856198 3.214165 3.089806
102465432 5.843189 6.530526 6.050846
100133331 2.866466 3.301206 3.159020
MCW_SP_JT_R26_R1.bam MCW_SP_JT_R27_R1.bam MCW_SP_JT_R28_R1.bam
653635 3.656405 3.985945 4.163996
729737 3.213791 3.358868 2.876598
102723897 3.620785 3.700588 4.002932
100132287 2.632912 2.880691 2.774609
102465432 5.849481 5.759741 5.728299
100133331 2.873872 2.995369 2.843396
MCW_SP_JT_R29_R1.bam MCW_SP_JT_R3_R1.bam MCW_SP_JT_R30_R1.bam
653635 3.690480 3.708306 3.690736
729737 3.414789 3.908737 3.395218
102723897 3.471148 3.451582 3.560313
100132287 2.885044 2.901147 2.831789
102465432 5.788580 6.298268 5.660946
100133331 3.052714 2.981187 3.096700
MCW_SP_JT_R31_R1.bam MCW_SP_JT_R32_R1.bam MCW_SP_JT_R33_R1.bam
653635 3.484063 3.794184 4.059394
729737 3.530033 3.214611 4.070133
102723897 3.346964 3.499083 3.713919
100132287 3.076583 3.060991 3.268120
102465432 6.339198 5.543553 6.064772
100133331 3.000254 3.017691 3.323028
MCW_SP_JT_R34_R1.bam MCW_SP_JT_R35_R1.bam MCW_SP_JT_R36_R1.bam
653635 4.067770 3.520514 3.486032
729737 2.030846 3.638776 3.571840
102723897 3.648676 3.427179 3.280504
100132287 2.009051 3.071078 3.322369
102465432 5.614652 5.835268 5.765707
100133331 2.269541 3.025487 3.070539
MCW_SP_JT_R37_R1.bam MCW_SP_JT_R38_R1.bam MCW_SP_JT_R39_R1.bam
653635 4.348863 3.865031 3.986159
729737 3.259608 3.040493 3.342022
102723897 4.073834 3.750410 3.972660
100132287 2.468648 2.187540 2.562966
102465432 5.892568 5.382835 5.330899
100133331 2.609673 2.222361 2.328735
MCW_SP_JT_R4_R1.bam MCW_SP_JT_R40_R1.bam MCW_SP_JT_R41_R1.bam
653635 3.519166 4.044758 4.005217
729737 3.303539 3.002627 3.075515
102723897 3.381588 3.740798 3.745836
100132287 2.644074 1.895905 2.275072
102465432 6.190731 5.435471 4.975615
100133331 2.425670 1.986496 2.275072
MCW_SP_JT_R42_R1.bam MCW_SP_JT_R43_R1.bam MCW_SP_JT_R44_R1.bam
653635 3.974853 4.101836 4.136763
729737 3.266218 3.461840 3.753420
102723897 3.917212 3.930917 4.031566
100132287 2.264998 2.500886 2.304367
102465432 5.779068 5.471101 5.851456
100133331 2.438923 2.500886 2.600289
MCW_SP_JT_R45_R1.bam MCW_SP_JT_R46_R1.bam MCW_SP_JT_R47_R1.bam
653635 4.295232 4.555097 4.277421
729737 3.943473 2.854727 3.763068
102723897 4.013662 4.401335 3.928326
100132287 2.613999 2.151772 2.753470
102465432 5.551270 5.437887 5.271744
100133331 2.796599 2.201235 2.711388
MCW_SP_JT_R48_R1.bam MCW_SP_JT_R49_R1.bam MCW_SP_JT_R5_R1.bam
653635 4.317992 4.129247 3.629845
729737 3.713030 3.570782 3.861831
102723897 4.119661 3.933378 3.534008
100132287 2.343923 2.371794 3.224695
102465432 5.182738 5.453640 6.167150
100133331 2.718915 2.935266 3.102742
MCW_SP_JT_R50_R1.bam MCW_SP_JT_R51_R1.bam MCW_SP_JT_R52_R1.bam
653635 3.931173 4.000536 4.500237
729737 3.420081 3.211480 2.163499
102723897 3.596454 3.851259 4.332327
100132287 2.201911 2.414032 1.801331
102465432 5.588742 5.582633 5.931258
100133331 2.375675 2.530857 1.900746
MCW_SP_JT_R53_R1.bam MCW_SP_JT_R54_R1.bam MCW_SP_JT_R55_R1.bam
653635 3.953858 3.915305 3.953640
729737 3.537281 2.983090 3.958270
102723897 3.738614 3.904451 3.832775
100132287 2.744298 2.567489 2.930663
102465432 5.419675 5.480903 5.890536
100133331 2.340340 2.511366 2.986128
MCW_SP_JT_R56_R1.bam MCW_SP_JT_R57_R1.bam MCW_SP_JT_R58_R1.bam
653635 3.951355 4.099690 3.831640
729737 4.102389 3.916661 3.763764
102723897 3.782640 3.931986 3.562742
100132287 2.803895 2.700634 2.689516
102465432 5.927505 6.206681 6.011032
100133331 2.961006 3.001092 2.614427
MCW_SP_JT_R59_R1.bam MCW_SP_JT_R6_R1.bam MCW_SP_JT_R60_R1.bam
653635 3.999480 3.775726 3.667632
729737 3.921735 3.821188 4.085040
102723897 3.970814 3.610768 3.745294
100132287 2.936536 3.244299 3.004008
102465432 6.251708 6.110782 5.746996
100133331 2.975535 3.038354 3.178405
MCW_SP_JT_R61_R1.bam MCW_SP_JT_R62_R1.bam MCW_SP_JT_R63_R1.bam
653635 4.261731 4.206359 4.627284
729737 4.488571 4.316587 3.700621
102723897 4.218190 4.206359 4.536705
100132287 3.458278 3.411851 2.569848
102465432 5.949511 5.952886 6.334155
100133331 3.292603 3.175974 2.841571
MCW_SP_JT_R64_R1.bam MCW_SP_JT_R65_R1.bam MCW_SP_JT_R66_R1.bam
653635 4.056825 4.359597 4.322034
729737 3.710437 4.198531 4.355106
102723897 4.272144 4.074462 4.078431
100132287 2.790233 2.985665 3.236722
102465432 6.701197 6.612388 5.872602
100133331 2.353918 3.230118 3.285575
MCW_SP_JT_R67_R1.bam MCW_SP_JT_R68_R1.bam MCW_SP_JT_R69_R1.bam
653635 4.093075 4.169506 4.284365
729737 3.975643 4.056729 2.967701
102723897 3.828573 3.939104 4.130434
100132287 3.069437 2.758961 2.355694
102465432 6.213256 4.388984 6.032591
100133331 3.019448 3.074312 2.462787
MCW_SP_JT_R7_R1.bam MCW_SP_JT_R70_R1.bam MCW_SP_JT_R71_R1.bam
653635 3.702870 4.350319 3.862762
729737 3.906166 2.824111 3.780351
102723897 3.653521 4.256793 3.765307
100132287 3.307701 2.583392 2.989805
102465432 6.785167 6.981247 6.019382
100133331 3.055626 2.313010 3.145096
MCW_SP_JT_R72_R1.bam MCW_SP_JT_R73_R1.bam MCW_SP_JT_R74_R1.bam
653635 4.255097 3.425747 3.347306
729737 3.772805 4.160997 3.872723
102723897 4.178903 3.519192 3.217809
100132287 2.763989 3.346373 2.956400
102465432 6.549999 6.696252 5.672028
100133331 2.976098 3.247889 2.975381
MCW_SP_JT_R75_R1.bam MCW_SP_JT_R76_R1.bam MCW_SP_JT_R77_R1.bam
653635 3.553832 3.256103 3.535859
729737 3.439417 3.579072 4.101951
102723897 3.592140 3.304026 3.485473
100132287 2.572931 2.640369 3.081263
102465432 5.635716 6.157283 6.012195
100133331 2.818898 2.820624 3.365227
MCW_SP_JT_R78_R1.bam MCW_SP_JT_R79_R1.bam MCW_SP_JT_R8_R1.bam
653635 3.288436 3.520963 3.901020
729737 4.107062 3.973416 4.399858
102723897 3.271644 3.543245 3.755467
100132287 3.148289 3.057062 3.623059
102465432 5.891118 6.221300 6.711815
100133331 3.313262 3.213453 3.452829
MCW_SP_JT_R80_R1.bam MCW_SP_JT_R81_R1.bam MCW_SP_JT_R82_R1.bam
653635 3.263082 3.885913 3.122967
729737 3.930600 3.002975 2.899120
102723897 3.335835 3.839760 3.015380
100132287 2.727055 2.113936 2.262756
102465432 6.066384 6.109827 6.208978
100133331 3.299917 2.250462 2.353913
MCW_SP_JT_R83_R1.bam MCW_SP_JT_R84_R1.bam MCW_SP_JT_R85_R1.bam
653635 3.354050 3.309292 3.468823
729737 3.735780 3.758927 3.775752
102723897 3.330447 3.213049 3.715256
100132287 2.941293 3.055458 3.046060
102465432 5.653153 5.990854 5.813942
100133331 3.002313 3.073842 3.148749
MCW_SP_JT_R86_R1.bam MCW_SP_JT_R87_R1.bam MCW_SP_JT_R88_R1.bam
653635 3.358211 3.716322 3.667240
729737 3.696974 3.389934 2.510252
102723897 3.205285 3.784776 4.029517
100132287 2.935293 2.454054 2.494602
102465432 5.188764 6.005393 6.339715
100133331 3.043734 2.686519 2.766293
MCW_SP_JT_R89_R1.bam MCW_SP_JT_R9_R1.bam MCW_SP_JT_R90_R1.bam
653635 3.473576 3.935642 3.533785
729737 3.916589 4.286806 4.045149
102723897 3.651816 3.825236 3.699269
100132287 3.233295 3.114900 3.392174
102465432 5.450633 6.776393 5.644493
100133331 3.240746 3.072566 3.471774
MCW_SP_JT_R91_R1.bam MCW_SP_JT_R92_R1.bam MCW_SP_JT_R93_R1.bam
653635 3.683725 3.847415 3.817342
729737 3.739641 3.559183 3.305198
102723897 3.695083 3.833350 3.772041
100132287 2.559014 2.192977 2.141834
102465432 5.694894 4.941701 4.935861
100133331 2.958286 2.472487 2.688600
MCW_SP_JT_R94_R1.bam MCW_SP_JT_R95_R1.bam MCW_SP_JT_R96_R1.bam
653635 3.920662 3.788745 3.768642
729737 3.817067 3.487133 3.628417
102723897 4.040498 3.788745 3.759065
100132287 2.309429 2.159710 2.088023
102465432 5.661112 5.780593 5.021250
100133331 2.454577 2.624960 2.661193
MCW_SP_JT_R97_R1.bam MCW_SP_JT_R98_R1.bam MCW_SP_JT_R99_R1.bam
653635 3.821755 3.837615 3.733312
729737 4.190234 3.262419 4.049943
102723897 3.900803 3.800656 3.944660
100132287 2.680030 1.823367 2.400419
102465432 5.551285 6.230572 5.490575
100133331 2.868814 1.946904 2.789010📌 Prepare DGEList Object
# Subset count matrix based on filtered CPM matrix
x <- counts_matrix[row.names(filcpm_matrix),]
dim(x)[1] 14279 108# Modify Metadata
Metadata_2 <- Metadata
rownames(Metadata_2) <- Metadata_2$Sample_bam
colnames(x) <- Metadata_2$Sample_ID
rownames(Metadata_2) <- Metadata_2$Sample_ID
Metadata_2$Drug_time <- make.names(Metadata_2$Drug_time)
Metadata_2$Ind <- as.character(Metadata_2$Ind)
# ✅ Create DGEList object
dge <- DGEList(counts = x)
dge$samples$group <- factor(Metadata_2$Drug_time)
dge <- calcNormFactors(dge, method = "TMM")
# ✅ Check normalization factors
dge$samples group lib.size norm.factors
CX-5461_0.1_3_17-3 CX.5461_0.1_3 21200035 1.0102693
DOX_0.5_24_17-3 DOX_0.5_24 16563771 1.1076182
DOX_0.5_24_87-1 DOX_0.5_24 21656653 0.9933568
VEH_0.1_24_87-1 VEH_0.1_24 22426842 0.9766783
VEH_0.5_24_87-1 VEH_0.5_24 22162275 0.9924549
CX-5461_0.1_48_87-1 CX.5461_0.1_48 22187620 0.9683497
CX-5461_0.5_48_87-1 CX.5461_0.5_48 20782167 0.9814665
DOX_0.1_48_87-1 DOX_0.1_48 25382841 1.0011030
DOX_0.5_48_87-1 DOX_0.5_48 19863531 1.0541528
VEH_0.1_48_87-1 VEH_0.1_48 21473253 1.0138011
VEH_0.5_48_87-1 VEH_0.5_48 20337337 1.0219756
VEH_0.1_24_17-3 VEH_0.1_24 15750538 0.9952313
VEH_0.5_24_17-3 VEH_0.5_24 15638967 1.0367909
CX-5461_0.1_48_17-3 CX.5461_0.1_48 18465522 0.9543636
CX-5461_0.5_48_17-3 CX.5461_0.5_48 16876134 0.9636074
DOX_0.1_48_17-3 DOX_0.1_48 20609601 0.9839708
DOX_0.5_48_17-3 DOX_0.5_48 18829944 1.1876478
VEH_0.1_48_17-3 VEH_0.1_48 24712537 0.9804184
VEH_0.5_48_17-3 VEH_0.5_48 17006648 0.9707617
CX-5461_0.1_3_84-1 CX.5461_0.1_3 17178171 1.0219387
CX-5461_0.5_3_17-3 CX.5461_0.5_3 17694385 1.0103837
CX-5461_0.5_3_84-1 CX.5461_0.5_3 17261114 1.0060170
DOX_0.1_3_84-1 DOX_0.1_3 17352493 0.9911466
DOX_0.5_3_84-1 DOX_0.5_3 17757477 1.0034479
VEH_0.1_3_84-1 VEH_0.1_3 19305213 1.0354377
VEH_0.5_3_84-1 VEH_0.5_3 17297185 1.0285524
CX-5461_0.1_24_84-1 CX.5461_0.1_24 23860498 0.9857321
CX-5461_0.5_24_84-1 CX.5461_0.5_24 19482159 0.9638391
DOX_0.1_24_84-1 DOX_0.1_24 19659504 0.9565317
DOX_0.5_24_84-1 DOX_0.5_24 17875612 1.0212021
VEH_0.1_24_84-1 VEH_0.1_24 16449210 0.9998393
DOX_0.1_3_17-3 DOX_0.1_3 16400599 0.9962197
VEH_0.5_24_84-1 VEH_0.5_24 18783630 0.9616033
CX-5461_0.1_48_84-1 CX.5461_0.1_48 13781326 0.9400278
CX-5461_0.5_48_84-1 CX.5461_0.5_48 20232591 0.9563694
DOX_0.1_48_84-1 DOX_0.1_48 16028063 0.9651474
DOX_0.5_48_84-1 DOX_0.5_48 16269098 1.0652791
VEH_0.1_48_84-1 VEH_0.1_48 15276408 0.9612979
VEH_0.5_48_84-1 VEH_0.5_48 17447290 1.0050272
CX-5461_0.1_3_90-1 CX.5461_0.1_3 15807726 0.9991915
CX-5461_0.5_3_90-1 CX.5461_0.5_3 17946649 0.9956913
DOX_0.1_3_90-1 DOX_0.1_3 20294777 0.9830679
DOX_0.5_3_17-3 DOX_0.5_3 18191217 0.9080939
DOX_0.5_3_90-1 DOX_0.5_3 20699125 0.9833929
VEH_0.1_3_90-1 VEH_0.1_3 20027541 0.9953796
VEH_0.5_3_90-1 VEH_0.5_3 17833214 1.0003025
CX-5461_0.1_24_90-1 CX.5461_0.1_24 15094639 1.0102577
CX-5461_0.5_24_90-1 CX.5461_0.5_24 17758047 1.0114258
DOX_0.1_24_90-1 DOX_0.1_24 16921727 0.9900805
DOX_0.5_24_90-1 DOX_0.5_24 19271264 1.0068767
VEH_0.1_24_90-1 VEH_0.1_24 20601703 0.9898691
VEH_0.5_24_90-1 VEH_0.5_24 19875553 1.0060950
CX-5461_0.1_48_90-1 CX.5461_0.1_48 23817456 1.0255425
VEH_0.1_3_17-3 VEH_0.1_3 26353255 0.9919794
CX-5461_0.5_48_90-1 CX.5461_0.5_48 18651093 1.0370010
DOX_0.1_48_90-1 DOX_0.1_48 17767662 1.0233343
DOX_0.5_48_90-1 DOX_0.5_48 20031141 1.1189284
VEH_0.1_48_90-1 VEH_0.1_48 19523120 1.0484497
VEH_0.5_48_90-1 VEH_0.5_48 17657193 1.0550052
CX-5461_0.1_3_75-1 CX.5461_0.1_3 20046785 0.9894881
CX-5461_0.5_3_75-1 CX.5461_0.5_3 19885297 0.9767025
DOX_0.1_3_75-1 DOX_0.1_3 18575114 0.9755788
DOX_0.5_3_75-1 DOX_0.5_3 15260497 0.9562336
VEH_0.1_3_75-1 VEH_0.1_3 19040007 0.9839769
VEH_0.5_3_17-3 VEH_0.5_3 18217503 0.9735069
VEH_0.5_3_75-1 VEH_0.5_3 21311999 0.9663251
CX-5461_0.1_24_75-1 CX.5461_0.1_24 19256517 1.0184093
CX-5461_0.5_24_75-1 CX.5461_0.5_24 14304255 1.0186309
DOX_0.1_24_75-1 DOX_0.1_24 21875105 1.0386302
DOX_0.5_24_75-1 DOX_0.5_24 17032433 0.9686987
VEH_0.1_24_75-1 VEH_0.1_24 19796422 1.0295019
VEH_0.5_24_75-1 VEH_0.5_24 21524523 1.0376940
CX-5461_0.1_48_75-1 CX.5461_0.1_48 20955873 0.9752971
CX-5461_0.5_48_75-1 CX.5461_0.5_48 19924867 0.9768371
DOX_0.1_48_75-1 DOX_0.1_48 17698471 1.0369925
CX-5461_0.1_24_17-3 CX.5461_0.1_24 18239213 0.9808618
DOX_0.5_48_75-1 DOX_0.5_48 15422887 1.0296304
VEH_0.1_48_75-1 VEH_0.1_48 21014387 1.0100812
VEH_0.5_48_75-1 VEH_0.5_48 21346438 1.0015670
CX-5461_0.1_3_78-1 CX.5461_0.1_3 20824890 0.9686975
CX-5461_0.5_3_78-1 CX.5461_0.5_3 19425405 1.0100551
DOX_0.1_3_78-1 DOX_0.1_3 18950843 0.9933529
DOX_0.5_3_78-1 DOX_0.5_3 21654404 0.9808038
VEH_0.1_3_78-1 VEH_0.1_3 20054872 0.9802655
VEH_0.5_3_78-1 VEH_0.5_3 17661233 1.0220629
CX-5461_0.1_24_78-1 CX.5461_0.1_24 16754941 0.9785599
CX-5461_0.5_24_17-3 CX.5461_0.5_24 16014473 1.0010257
CX-5461_0.5_24_78-1 CX.5461_0.5_24 16077548 0.9819166
DOX_0.1_24_78-1 DOX_0.1_24 27842910 0.9788383
DOX_0.5_24_78-1 DOX_0.5_24 6326261 1.0007627
VEH_0.1_24_78-1 VEH_0.1_24 18032196 0.9767098
VEH_0.5_24_78-1 VEH_0.5_24 18727398 0.9931695
CX-5461_0.1_48_78-1 CX.5461_0.1_48 21292146 1.0036183
CX-5461_0.5_48_78-1 CX.5461_0.5_48 18392707 0.9988905
DOX_0.1_48_78-1 DOX_0.1_48 18735925 1.0433629
DOX_0.5_48_78-1 DOX_0.5_48 16079343 1.1063695
VEH_0.1_48_78-1 VEH_0.1_48 20500742 1.0149206
DOX_0.1_24_17-3 DOX_0.1_24 23900805 1.0243090
VEH_0.5_48_78-1 VEH_0.5_48 18433692 1.0396358
CX-5461_0.1_3_87-1 CX.5461_0.1_3 19664754 0.9573708
CX-5461_0.5_3_87-1 CX.5461_0.5_3 21451819 1.0192062
DOX_0.1_3_87-1 DOX_0.1_3 20624889 0.9539861
DOX_0.5_3_87-1 DOX_0.5_3 22838835 0.9919424
VEH_0.1_3_87-1 VEH_0.1_3 17616767 0.9841881
VEH_0.5_3_87-1 VEH_0.5_3 22148086 1.0203028
CX-5461_0.1_24_87-1 CX.5461_0.1_24 30093416 0.9439250
CX-5461_0.5_24_87-1 CX.5461_0.5_24 22093483 0.9589794
DOX_0.1_24_87-1 DOX_0.1_24 21415578 0.9549937📌 Create Design Matrix
# ✅ Create Design Matrix
design <- model.matrix(~ 0 + Metadata_2$Drug_time)
colnames(design) <- gsub("Metadata_2\\$Drug_time", "", colnames(design))
design CX.5461_0.1_24 CX.5461_0.1_3 CX.5461_0.1_48 CX.5461_0.5_24 CX.5461_0.5_3
1 0 1 0 0 0
2 0 0 0 0 0
3 0 0 0 0 0
4 0 0 0 0 0
5 0 0 0 0 0
6 0 0 1 0 0
7 0 0 0 0 0
8 0 0 0 0 0
9 0 0 0 0 0
10 0 0 0 0 0
11 0 0 0 0 0
12 0 0 0 0 0
13 0 0 0 0 0
14 0 0 1 0 0
15 0 0 0 0 0
16 0 0 0 0 0
17 0 0 0 0 0
18 0 0 0 0 0
19 0 0 0 0 0
20 0 1 0 0 0
21 0 0 0 0 1
22 0 0 0 0 1
23 0 0 0 0 0
24 0 0 0 0 0
25 0 0 0 0 0
26 0 0 0 0 0
27 1 0 0 0 0
28 0 0 0 1 0
29 0 0 0 0 0
30 0 0 0 0 0
31 0 0 0 0 0
32 0 0 0 0 0
33 0 0 0 0 0
34 0 0 1 0 0
35 0 0 0 0 0
36 0 0 0 0 0
37 0 0 0 0 0
38 0 0 0 0 0
39 0 0 0 0 0
40 0 1 0 0 0
41 0 0 0 0 1
42 0 0 0 0 0
43 0 0 0 0 0
44 0 0 0 0 0
45 0 0 0 0 0
46 0 0 0 0 0
47 1 0 0 0 0
48 0 0 0 1 0
49 0 0 0 0 0
50 0 0 0 0 0
51 0 0 0 0 0
52 0 0 0 0 0
53 0 0 1 0 0
54 0 0 0 0 0
55 0 0 0 0 0
56 0 0 0 0 0
57 0 0 0 0 0
58 0 0 0 0 0
59 0 0 0 0 0
60 0 1 0 0 0
61 0 0 0 0 1
62 0 0 0 0 0
63 0 0 0 0 0
64 0 0 0 0 0
65 0 0 0 0 0
66 0 0 0 0 0
67 1 0 0 0 0
68 0 0 0 1 0
69 0 0 0 0 0
70 0 0 0 0 0
71 0 0 0 0 0
72 0 0 0 0 0
73 0 0 1 0 0
74 0 0 0 0 0
75 0 0 0 0 0
76 1 0 0 0 0
77 0 0 0 0 0
78 0 0 0 0 0
79 0 0 0 0 0
80 0 1 0 0 0
81 0 0 0 0 1
82 0 0 0 0 0
83 0 0 0 0 0
84 0 0 0 0 0
85 0 0 0 0 0
86 1 0 0 0 0
87 0 0 0 1 0
88 0 0 0 1 0
89 0 0 0 0 0
90 0 0 0 0 0
91 0 0 0 0 0
92 0 0 0 0 0
93 0 0 1 0 0
94 0 0 0 0 0
95 0 0 0 0 0
96 0 0 0 0 0
97 0 0 0 0 0
98 0 0 0 0 0
99 0 0 0 0 0
100 0 1 0 0 0
101 0 0 0 0 1
102 0 0 0 0 0
103 0 0 0 0 0
104 0 0 0 0 0
105 0 0 0 0 0
106 1 0 0 0 0
107 0 0 0 1 0
108 0 0 0 0 0
CX.5461_0.5_48 DOX_0.1_24 DOX_0.1_3 DOX_0.1_48 DOX_0.5_24 DOX_0.5_3
1 0 0 0 0 0 0
2 0 0 0 0 1 0
3 0 0 0 0 1 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
6 0 0 0 0 0 0
7 1 0 0 0 0 0
8 0 0 0 1 0 0
9 0 0 0 0 0 0
10 0 0 0 0 0 0
11 0 0 0 0 0 0
12 0 0 0 0 0 0
13 0 0 0 0 0 0
14 0 0 0 0 0 0
15 1 0 0 0 0 0
16 0 0 0 1 0 0
17 0 0 0 0 0 0
18 0 0 0 0 0 0
19 0 0 0 0 0 0
20 0 0 0 0 0 0
21 0 0 0 0 0 0
22 0 0 0 0 0 0
23 0 0 1 0 0 0
24 0 0 0 0 0 1
25 0 0 0 0 0 0
26 0 0 0 0 0 0
27 0 0 0 0 0 0
28 0 0 0 0 0 0
29 0 1 0 0 0 0
30 0 0 0 0 1 0
31 0 0 0 0 0 0
32 0 0 1 0 0 0
33 0 0 0 0 0 0
34 0 0 0 0 0 0
35 1 0 0 0 0 0
36 0 0 0 1 0 0
37 0 0 0 0 0 0
38 0 0 0 0 0 0
39 0 0 0 0 0 0
40 0 0 0 0 0 0
41 0 0 0 0 0 0
42 0 0 1 0 0 0
43 0 0 0 0 0 1
44 0 0 0 0 0 1
45 0 0 0 0 0 0
46 0 0 0 0 0 0
47 0 0 0 0 0 0
48 0 0 0 0 0 0
49 0 1 0 0 0 0
50 0 0 0 0 1 0
51 0 0 0 0 0 0
52 0 0 0 0 0 0
53 0 0 0 0 0 0
54 0 0 0 0 0 0
55 1 0 0 0 0 0
56 0 0 0 1 0 0
57 0 0 0 0 0 0
58 0 0 0 0 0 0
59 0 0 0 0 0 0
60 0 0 0 0 0 0
61 0 0 0 0 0 0
62 0 0 1 0 0 0
63 0 0 0 0 0 1
64 0 0 0 0 0 0
65 0 0 0 0 0 0
66 0 0 0 0 0 0
67 0 0 0 0 0 0
68 0 0 0 0 0 0
69 0 1 0 0 0 0
70 0 0 0 0 1 0
71 0 0 0 0 0 0
72 0 0 0 0 0 0
73 0 0 0 0 0 0
74 1 0 0 0 0 0
75 0 0 0 1 0 0
76 0 0 0 0 0 0
77 0 0 0 0 0 0
78 0 0 0 0 0 0
79 0 0 0 0 0 0
80 0 0 0 0 0 0
81 0 0 0 0 0 0
82 0 0 1 0 0 0
83 0 0 0 0 0 1
84 0 0 0 0 0 0
85 0 0 0 0 0 0
86 0 0 0 0 0 0
87 0 0 0 0 0 0
88 0 0 0 0 0 0
89 0 1 0 0 0 0
90 0 0 0 0 1 0
91 0 0 0 0 0 0
92 0 0 0 0 0 0
93 0 0 0 0 0 0
94 1 0 0 0 0 0
95 0 0 0 1 0 0
96 0 0 0 0 0 0
97 0 0 0 0 0 0
98 0 1 0 0 0 0
99 0 0 0 0 0 0
100 0 0 0 0 0 0
101 0 0 0 0 0 0
102 0 0 1 0 0 0
103 0 0 0 0 0 1
104 0 0 0 0 0 0
105 0 0 0 0 0 0
106 0 0 0 0 0 0
107 0 0 0 0 0 0
108 0 1 0 0 0 0
DOX_0.5_48 VEH_0.1_24 VEH_0.1_3 VEH_0.1_48 VEH_0.5_24 VEH_0.5_3 VEH_0.5_48
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 1 0 0 0 0 0
5 0 0 0 0 1 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 1 0 0 0 0 0 0
10 0 0 0 1 0 0 0
11 0 0 0 0 0 0 1
12 0 1 0 0 0 0 0
13 0 0 0 0 1 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0
16 0 0 0 0 0 0 0
17 1 0 0 0 0 0 0
18 0 0 0 1 0 0 0
19 0 0 0 0 0 0 1
20 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0
22 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0
24 0 0 0 0 0 0 0
25 0 0 1 0 0 0 0
26 0 0 0 0 0 1 0
27 0 0 0 0 0 0 0
28 0 0 0 0 0 0 0
29 0 0 0 0 0 0 0
30 0 0 0 0 0 0 0
31 0 1 0 0 0 0 0
32 0 0 0 0 0 0 0
33 0 0 0 0 1 0 0
34 0 0 0 0 0 0 0
35 0 0 0 0 0 0 0
36 0 0 0 0 0 0 0
37 1 0 0 0 0 0 0
38 0 0 0 1 0 0 0
39 0 0 0 0 0 0 1
40 0 0 0 0 0 0 0
41 0 0 0 0 0 0 0
42 0 0 0 0 0 0 0
43 0 0 0 0 0 0 0
44 0 0 0 0 0 0 0
45 0 0 1 0 0 0 0
46 0 0 0 0 0 1 0
47 0 0 0 0 0 0 0
48 0 0 0 0 0 0 0
49 0 0 0 0 0 0 0
50 0 0 0 0 0 0 0
51 0 1 0 0 0 0 0
52 0 0 0 0 1 0 0
53 0 0 0 0 0 0 0
54 0 0 1 0 0 0 0
55 0 0 0 0 0 0 0
56 0 0 0 0 0 0 0
57 1 0 0 0 0 0 0
58 0 0 0 1 0 0 0
59 0 0 0 0 0 0 1
60 0 0 0 0 0 0 0
61 0 0 0 0 0 0 0
62 0 0 0 0 0 0 0
63 0 0 0 0 0 0 0
64 0 0 1 0 0 0 0
65 0 0 0 0 0 1 0
66 0 0 0 0 0 1 0
67 0 0 0 0 0 0 0
68 0 0 0 0 0 0 0
69 0 0 0 0 0 0 0
70 0 0 0 0 0 0 0
71 0 1 0 0 0 0 0
72 0 0 0 0 1 0 0
73 0 0 0 0 0 0 0
74 0 0 0 0 0 0 0
75 0 0 0 0 0 0 0
76 0 0 0 0 0 0 0
77 1 0 0 0 0 0 0
78 0 0 0 1 0 0 0
79 0 0 0 0 0 0 1
80 0 0 0 0 0 0 0
81 0 0 0 0 0 0 0
82 0 0 0 0 0 0 0
83 0 0 0 0 0 0 0
84 0 0 1 0 0 0 0
85 0 0 0 0 0 1 0
86 0 0 0 0 0 0 0
87 0 0 0 0 0 0 0
88 0 0 0 0 0 0 0
89 0 0 0 0 0 0 0
90 0 0 0 0 0 0 0
91 0 1 0 0 0 0 0
92 0 0 0 0 1 0 0
93 0 0 0 0 0 0 0
94 0 0 0 0 0 0 0
95 0 0 0 0 0 0 0
96 1 0 0 0 0 0 0
97 0 0 0 1 0 0 0
98 0 0 0 0 0 0 0
99 0 0 0 0 0 0 1
100 0 0 0 0 0 0 0
101 0 0 0 0 0 0 0
102 0 0 0 0 0 0 0
103 0 0 0 0 0 0 0
104 0 0 1 0 0 0 0
105 0 0 0 0 0 1 0
106 0 0 0 0 0 0 0
107 0 0 0 0 0 0 0
108 0 0 0 0 0 0 0
attr(,"assign")
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`Metadata_2$Drug_time`
[1] "contr.treatment"📌 Voom Transformation and Linear Modeling
# ✅ Duplicate Correlation for Individual Effect
corfit <- duplicateCorrelation(object = dge$counts, design = design, block = Metadata_2$Ind)
# ✅ Voom Transformation
v <- voom(dge, design, block = Metadata_2$Ind, correlation = corfit$consensus.correlation, plot = TRUE)
# ✅ Fit Linear Model
fit <- lmFit(v, design, block = Metadata_2$Ind, correlation = corfit$consensus.correlation)📌 Create Contrast Matrix
contrast_matrix <- makeContrasts(
CX_0.1_3vsVEH_0.1_3 = CX.5461_0.1_3 - VEH_0.1_3,
CX_0.1_24vsVEH_0.1_24 = CX.5461_0.1_24 - VEH_0.1_24,
CX_0.1_48vsVEH_0.1_48 = CX.5461_0.1_48 - VEH_0.1_48,
CX_0.5_3vsVEH_0.5_3 = CX.5461_0.5_3 - VEH_0.5_3,
CX_0.5_24vsVEH_0.5_24 = CX.5461_0.5_24 - VEH_0.5_24,
CX_0.5_48vsVEH_0.5_48 = CX.5461_0.5_48 - VEH_0.5_48,
DOX_0.1_3vsVEH_0.1_3 = DOX_0.1_3 - VEH_0.1_3,
DOX_0.1_24vsVEH_0.1_24 = DOX_0.1_24 - VEH_0.1_24,
DOX_0.1_48vsVEH_0.1_48 = DOX_0.1_48 - VEH_0.1_48,
DOX_0.5_3vsVEH_0.5_3 = DOX_0.5_3 - VEH_0.5_3,
DOX_0.5_24vsVEH_0.5_24 = DOX_0.5_24 - VEH_0.5_24,
DOX_0.5_48vsVEH_0.5_48 = DOX_0.5_48 - VEH_0.5_48,
levels = design
)📌 Fit Model and Generate Results
# ✅ Apply Contrasts
fit2 <- contrasts.fit(fit, contrast_matrix)
fit2 <- eBayes(fit2)
# ✅ Summary of Results
results_summary <- decideTests(fit2, adjust.method = "BH", p.value = 0.05)
summary(results_summary) CX_0.1_3vsVEH_0.1_3 CX_0.1_24vsVEH_0.1_24 CX_0.1_48vsVEH_0.1_48
Down 0 204 294
NotSig 14278 14074 13961
Up 1 1 24
CX_0.5_3vsVEH_0.5_3 CX_0.5_24vsVEH_0.5_24 CX_0.5_48vsVEH_0.5_48
Down 1 259 337
NotSig 14277 14001 13909
Up 1 19 33
DOX_0.1_3vsVEH_0.1_3 DOX_0.1_24vsVEH_0.1_24 DOX_0.1_48vsVEH_0.1_48
Down 1 2528 1696
NotSig 14277 9215 10818
Up 1 2536 1765
DOX_0.5_3vsVEH_0.5_3 DOX_0.5_24vsVEH_0.5_24 DOX_0.5_48vsVEH_0.5_48
Down 476 5067 5177
NotSig 13746 4451 4151
Up 57 4761 4951
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Homo.sapiens_1.3.1
[2] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[3] org.Hs.eg.db_3.18.0
[4] GO.db_3.18.0
[5] OrganismDbi_1.44.0
[6] GenomicFeatures_1.54.4
[7] GenomicRanges_1.54.1
[8] GenomeInfoDb_1.38.8
[9] AnnotationDbi_1.64.1
[10] IRanges_2.36.0
[11] S4Vectors_0.40.1
[12] Biobase_2.62.0
[13] BiocGenerics_0.48.1
[14] biomaRt_2.58.2
[15] scales_1.3.0
[16] lubridate_1.9.3
[17] forcats_1.0.0
[18] stringr_1.5.1
[19] dplyr_1.1.4
[20] purrr_1.0.2
[21] readr_2.1.5
[22] tidyr_1.3.1
[23] tibble_3.2.1
[24] ggplot2_3.5.1
[25] tidyverse_2.0.0
[26] data.table_1.14.10
[27] edgeR_4.0.1
[28] limma_3.58.1
[29] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-7
[3] RBGL_1.78.0 rlang_1.1.3
[5] magrittr_2.0.3 git2r_0.35.0
[7] matrixStats_1.4.1 compiler_4.3.0
[9] RSQLite_2.3.3 getPass_0.2-4
[11] png_0.1-8 callr_3.7.6
[13] vctrs_0.6.5 pkgconfig_2.0.3
[15] crayon_1.5.3 fastmap_1.1.1
[17] dbplyr_2.5.0 XVector_0.42.0
[19] Rsamtools_2.18.0 promises_1.3.0
[21] rmarkdown_2.29 tzdb_0.4.0
[23] graph_1.80.0 ps_1.8.1
[25] bit_4.0.5 xfun_0.50
[27] zlibbioc_1.48.0 cachem_1.0.8
[29] jsonlite_1.8.9 progress_1.2.3
[31] blob_1.2.4 later_1.3.2
[33] DelayedArray_0.28.0 BiocParallel_1.36.0
[35] parallel_4.3.0 prettyunits_1.2.0
[37] R6_2.5.1 bslib_0.8.0
[39] stringi_1.8.3 rtracklayer_1.62.0
[41] jquerylib_0.1.4 SummarizedExperiment_1.32.0
[43] Rcpp_1.0.12 knitr_1.49
[45] Matrix_1.6-1.1 httpuv_1.6.15
[47] timechange_0.3.0 tidyselect_1.2.1
[49] abind_1.4-8 rstudioapi_0.17.1
[51] yaml_2.3.10 codetools_0.2-20
[53] curl_6.0.1 processx_3.8.5
[55] lattice_0.22-5 withr_3.0.2
[57] KEGGREST_1.42.0 evaluate_1.0.3
[59] BiocFileCache_2.10.2 xml2_1.3.6
[61] Biostrings_2.70.1 BiocManager_1.30.25
[63] pillar_1.10.1 filelock_1.0.3
[65] MatrixGenerics_1.14.0 whisker_0.4.1
[67] generics_0.1.3 rprojroot_2.0.4
[69] RCurl_1.98-1.13 hms_1.1.3
[71] munsell_0.5.1 glue_1.7.0
[73] tools_4.3.0 BiocIO_1.12.0
[75] locfit_1.5-9.8 GenomicAlignments_1.38.2
[77] fs_1.6.3 XML_3.99-0.17
[79] grid_4.3.0 colorspace_2.1-0
[81] GenomeInfoDbData_1.2.11 restfulr_0.0.15
[83] cli_3.6.1 rappdirs_0.3.3
[85] S4Arrays_1.2.1 gtable_0.3.6
[87] sass_0.4.9 digest_0.6.34
[89] SparseArray_1.2.4 rjson_0.2.23
[91] memoise_2.0.1 htmltools_0.5.8.1
[93] lifecycle_1.0.4 httr_1.4.7
[95] statmod_1.5.0 bit64_4.0.5