DESeq2 - Differential expression analysis

Owned by Roberto Barrero Gumiel
Last updated: Jul 18, 2023
3 min read

Pre-requisites

  • Run your samples (FASTQ) using the nextflow nf-core/RNA-seq pipeline using ‘star_salmon’ or an alternative pipeline that generates feature counts. For example:

    xaccession gene M.209.3D.C M.647.C M.711.C M.8.C M.996.C M.996.T M.209.3D.T M.647.T M.711.T M.8.T NM_000014.4 A2M 78132 51570 32120 73355 106630 111500 93300 51695 106295 120380 NM_000015.2 NAT2 0 0 0 0 0 0 2 0 0 0 NM_000016.5 ACADM 1241 1614 1436 1192 2962 2127 1579 1471 2084 1123 NM_000017.2 ACADS 799 987 984 1010 2407 1460 1050 879 1462 816 NM_000018.3 ACADVL 6025 5078 4570 3725 8332 8693 8144 6549 10236 6501 NM_000019.3 ACAT1 1020 1178 1272 1109 2781 1400 1266 1011 1575 824 NM_000020.2 ACVRL1 0 51 137 0 132 220 0 237 0 22 NM_000021.3 PSEN1 367 911 355 766 1469 1287 1201 1248 1400 994 NM_000022.2 ADA 77 177 124 62 261 234 104 141 232 58 NM_000023.2 SGCA 0 0 0 0 0 0 0 0 0 0 NM_000024.5 ADRB2 671 1002 336 171 426 166 692 847 277 79 NM_000025.2 ADRB3 0 0 0 0 0 0 1 0 0 0 NM_000026.2 ADSL 1243 1597 1214 1441 2420 1842 1665 1424 1964 1281 NM_000027.3 AGA 535 712 412 358 1104 1017 727 716 915 391

Prepare INPUT files

  • INPUT 1: Prepare a feature count table in CSV format by either using gene symbols or accession numbers as row names:

gene,M.209.3D.C,M.647.C,M.711.C,M.8.C,M.996.C,M.996.T,M.209.3D.T,M.647.T,M.711.T,M.8.T A2M,78132,51570,32120,73355,106630,111500,93300,51695,106295,120380 NAT2,0,0,0,0,0,0,2,0,0,0 ACADM,1241,1614,1436,1192,2962,2127,1579,1471,2084,1123 ACADS,799,987,984,1010,2407,1460,1050,879,1462,816 ACADVL,6025,5078,4570,3725,8332,8693,8144,6549,10236,6501 ACAT1,1020,1178,1272,1109,2781,1400,1266,1011,1575,824 ACVRL1,0,51,137,0,132,220,0,237,0,22 PSEN1,367,911,355,766,1469,1287,1201,1248,1400,994 ADA,77,177,124,62,261,234,104,141,232,58 SGCA,0,0,0,0,0,0,0,0,0,0 ADRB2,671,1002,336,171,426,166,692,847,277,79 ADRB3,0,0,0,0,0,0,1,0,0,0 ADSL,1243,1597,1214,1441,2420,1842,1665,1424,1964,1281 AGA,535,712,412,358,1104,1017,727,716,915,391

  • INPUT 2: Sample metadata:

id,dex,celltype,geoid M.209.3D.C,melanocytes_control,melanocytes_control,none M.647.C,melanocytes_control,melanocytes_control,none M.711.C,melanocytes_control,melanocytes_control,none M.8.C,melanocytes_control,melanocytes_control,none M.996.C,melanocytes_control,melanocytes_control,none M.209.3D.T,melanocytes_treatment,melanocytes_treatment,none M.647.T,melanocytes_treatment,melanocytes_treatment,none M.711.T,melanocytes_treatment,melanocytes_treatment,none M.8.T,melanocytes_treatment,melanocytes_treatment,none M.996.T,melanocytes_treatment,melanocytes_treatment,none

 

R script - run the script using Rstudio