Test whether Gal3 KO causes changes to expressionn of mitochondrial genes.
library("mitch")Read in a gene table, reactome gene sets and the mitochondrially encoded gene.
gt <- read.table("genetable.tsv")
head(gt)## V1 V2
## 1 ENSMUSG00000000001_Gnai3 Gnai3
## 2 ENSMUSG00000000028_Cdc45 Cdc45
## 3 ENSMUSG00000000031_H19 H19
## 4 ENSMUSG00000000037_Scml2 Scml2
## 5 ENSMUSG00000000056_Narf Narf
## 6 ENSMUSG00000000058_Cav2 Cav2reactome <- gmt_import("m2.cp.reactome.v2024.1.Mm.symbols.gmt")
mtgenes <- readLines("mtgenes.txt")
mtgenes <- sapply(strsplit(mtgenes,"_"),"[[",2)
mt_trna <- mtgenes[grep("mt-T",mtgenes)]
mt_mrna <- mtgenes[grep("mt-T",mtgenes,invert=TRUE)]
mtgenelist <- list("mtgenes"=mtgenes,"mt tRNA"=mt_trna, "mt mRNA"=mt_mrna)Gal3KO causes a subtle increase iin electron transport, but no changes to other mitochondrial pathways.
wt_v_gal3ko <- read.table("WTvsGAL3KO.tsv",row.names=1,header=TRUE)
head(wt_v_gal3ko)## logFC logCPM LR PValue
## ENSMUSG00000088025_Rprl3 7.1388625 4.6718624 912.8749 1.559547e-200
## ENSMUSG00000097431_Gm26782 -1.9635345 2.6124053 603.9507 2.314757e-133
## ENSMUSG00000087775_Rprl2 4.8667199 0.9246235 299.0305 5.357773e-67
## ENSMUSG00000021908_Gm6768 3.6461143 1.3744482 217.9958 2.474886e-49
## ENSMUSG00000071470_Ccnb1ip1 5.4338686 -0.5132433 134.8661 3.531964e-31
## ENSMUSG00000022193_Psmb5 -0.7204651 3.6202031 127.0168 1.842077e-29
## FDR dispersion Gal3Ko_rep1.x
## ENSMUSG00000088025_Rprl3 2.378153e-196 3.479995e-03 1161.79000
## ENSMUSG00000097431_Gm26782 1.764886e-129 2.035316e-04 45.06740
## ENSMUSG00000087775_Rprl2 2.723356e-63 1.362906e-01 81.57848
## ENSMUSG00000021908_Gm6768 9.434883e-46 9.765625e-05 110.61975
## ENSMUSG00000071470_Ccnb1ip1 1.077178e-27 4.650901e-03 27.95984
## ENSMUSG00000022193_Psmb5 4.681639e-26 9.399001e-03 222.31981
## Gal3Ko_rep2.x Gal3Ko_rep3.x Gal3Ko_rep4.x
## ENSMUSG00000088025_Rprl3 722.68505 1060.17546 1344.78288
## ENSMUSG00000097431_Gm26782 28.03393 41.12563 52.16594
## ENSMUSG00000087775_Rprl2 50.74544 74.44332 94.42786
## ENSMUSG00000021908_Gm6768 68.81040 100.94453 128.04340
## ENSMUSG00000071470_Ccnb1ip1 17.39227 25.51437 32.36378
## ENSMUSG00000022193_Psmb5 138.29281 202.87488 257.33728
## Gal3Ko_rep5.x Gal3Ko_rep6.x Gal3Ko_rep7.x
## ENSMUSG00000088025_Rprl3 949.62323 1123.82738 1089.44575
## ENSMUSG00000097431_Gm26782 36.83716 43.59478 42.26107
## ENSMUSG00000087775_Rprl2 66.68057 78.91282 76.49861
## ENSMUSG00000021908_Gm6768 90.41830 107.00514 103.73149
## ENSMUSG00000071470_Ccnb1ip1 22.85380 27.04623 26.21879
## ENSMUSG00000022193_Psmb5 181.71964 215.05530 208.47604
## Gal3Ko_rep8.x wt_rep1.x wt_rep2.x wt_rep3.x
## ENSMUSG00000088025_Rprl3 1164.84862 0.8287214 0.7721829 0.7913688
## ENSMUSG00000097431_Gm26782 45.18605 277.3421045 258.4207690 264.8416002
## ENSMUSG00000087775_Rprl2 81.79325 0.4978984 0.4639299 0.4754569
## ENSMUSG00000021908_Gm6768 110.91097 2.6589084 2.4775075 2.5390647
## ENSMUSG00000071470_Ccnb1ip1 28.03345 0.0000000 0.0000000 0.0000000
## ENSMUSG00000022193_Psmb5 222.90511 403.5946096 376.0598470 385.4035883
## wt_rep4.x wt_rep5.x wt_rep6.x Gal3Ko_rep1.y
## ENSMUSG00000088025_Rprl3 0.7437782 0.8391747 1.0189975 925
## ENSMUSG00000097431_Gm26782 248.9148053 280.8404436 341.0204235 47
## ENSMUSG00000087775_Rprl2 0.4468643 0.5041788 0.6122169 80
## ENSMUSG00000021908_Gm6768 2.3863728 2.6924473 3.2693992 151
## ENSMUSG00000071470_Ccnb1ip1 0.0000000 0.0000000 0.0000000 18
## ENSMUSG00000022193_Psmb5 362.2265500 408.6854732 496.2607641 186
## Gal3Ko_rep2.y Gal3Ko_rep3.y Gal3Ko_rep4.y
## ENSMUSG00000088025_Rprl3 667 1056 1828
## ENSMUSG00000097431_Gm26782 21 32 53
## ENSMUSG00000087775_Rprl2 51 63 134
## ENSMUSG00000021908_Gm6768 38 61 164
## ENSMUSG00000071470_Ccnb1ip1 16 37 37
## ENSMUSG00000022193_Psmb5 128 206 308
## Gal3Ko_rep5.y Gal3Ko_rep6.y Gal3Ko_rep7.y
## ENSMUSG00000088025_Rprl3 806 997 1505
## ENSMUSG00000097431_Gm26782 41 42 58
## ENSMUSG00000087775_Rprl2 40 76 73
## ENSMUSG00000021908_Gm6768 91 109 118
## ENSMUSG00000071470_Ccnb1ip1 25 21 14
## ENSMUSG00000022193_Psmb5 183 206 218
## Gal3Ko_rep8.y wt_rep1.y wt_rep2.y wt_rep3.y
## ENSMUSG00000088025_Rprl3 939 0 1 1
## ENSMUSG00000097431_Gm26782 41 285 208 279
## ENSMUSG00000087775_Rprl2 98 0 0 0
## ENSMUSG00000021908_Gm6768 111 3 4 3
## ENSMUSG00000071470_Ccnb1ip1 40 0 0 0
## ENSMUSG00000022193_Psmb5 222 385 436 412
## wt_rep4.y wt_rep5.y wt_rep6.y
## ENSMUSG00000088025_Rprl3 1 0 2
## ENSMUSG00000097431_Gm26782 284 265 351
## ENSMUSG00000087775_Rprl2 2 0 1
## ENSMUSG00000021908_Gm6768 0 4 2
## ENSMUSG00000071470_Ccnb1ip1 0 0 0
## ENSMUSG00000022193_Psmb5 372 325 496m_wt_v_gal3ko <- mitch_import(x=wt_v_gal3ko,DEtype="edger",geneTable=gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 15249## Note: no. genes in output = 15245## Note: estimated proportion of input genes in output = 1dim(m_wt_v_gal3ko)## [1] 15245 1tail(m_wt_v_gal3ko)## x
## Zxdb -0.1616329
## Zxdc 0.1539530
## Zyg11b -0.4278787
## Zyx -0.7270816
## Zzef1 0.1674099
## Zzz3 0.5227042mitch with mito genes - no change.
mt_wt_v_gal3ko <- mitch_calc(x=m_wt_v_gal3ko,genesets=mtgenelist,priority="effect",cores=4,minsetsize=3)## Note: Enrichments with large effect sizes may not be
## statistically significant.mt_wt_v_gal3ko$enrichment_result## set setSize pANOVA s.dist p.adjustANOVA
## 3 mt mRNA 9 0.001499638 0.6111111 0.004498915
## 2 mt tRNA 7 0.013236250 -0.5407159 0.019854376
## 1 mtgenes 16 0.457909209 0.1072050 0.457909209Mitch with reactome. Noticed big change in electron transport but not much with other mito pathways.
mr_wt_v_gal3ko <- mitch_calc(x=m_wt_v_gal3ko,genesets=reactome,priority="effect",cores=4,minsetsize=3)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mr_wt_v_gal3ko$enrichment_result)## set
## 183 REACTOME_CLASSICAL_ANTIBODY_MEDIATED_COMPLEMENT_ACTIVATION
## 484 REACTOME_INCRETIN_SYNTHESIS_SECRETION_AND_INACTIVATION
## 304 REACTOME_ENDOSOMAL_VACUOLAR_PATHWAY
## 1254 REACTOME_ZINC_INFLUX_INTO_CELLS_BY_THE_SLC39_GENE_FAMILY
## 220 REACTOME_CROSS_PRESENTATION_OF_PARTICULATE_EXOGENOUS_ANTIGENS_PHAGOSOMES
## 737 REACTOME_PD_1_SIGNALING
## setSize pANOVA s.dist p.adjustANOVA
## 183 5 0.0042555280 -0.7381627 0.033589230
## 484 3 0.0325424822 -0.7126361 0.141288012
## 304 10 0.0001741564 -0.6854742 0.002512256
## 1254 7 0.0018913255 -0.6780605 0.017200098
## 220 7 0.0019697498 -0.6754355 0.017657400
## 737 9 0.0005546690 -0.6646247 0.006386327# some subtle changes in electron transport
mr2_wt_v_gal3ko <- mr_wt_v_gal3ko$enrichment_result
head(mr2_wt_v_gal3ko[grep("ELECTRON",mr2_wt_v_gal3ko$set),])## set setSize
## 915 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT 124
## 46 REACTOME_AEROBIC_RESPIRATION_AND_RESPIRATORY_ELECTRON_TRANSPORT 202
## pANOVA s.dist p.adjustANOVA
## 915 7.707120e-05 0.2057734 0.0014018023
## 46 1.652967e-05 0.1761018 0.0004714713mr2_wt_v_gal3ko_mito <- mr2_wt_v_gal3ko[grep("MITOC",mr2_wt_v_gal3ko$set),]
subset(mr2_wt_v_gal3ko_mito, p.adjustANOVA < 0.05)## [1] set setSize pANOVA s.dist p.adjustANOVA
## <0 rows> (or 0-length row.names)First set up the analysis.
tg_v_tggal3ko <- read.table("MST1TGvsMST1TGGAL3KO.tsv",row.names=1,header=TRUE)
head(tg_v_tggal3ko)## logFC logCPM LR PValue
## ENSMUSG00000050335_Lgals3 -2.702830 5.2244881 818.9174 4.160516e-180
## ENSMUSG00000021908_Gm6768 4.551871 0.9754383 655.4656 1.446964e-144
## ENSMUSG00000063506_Arhgap22 -1.815747 3.7621134 423.9686 3.339128e-94
## ENSMUSG00000088025_Rprl3 7.010163 4.5693474 332.9309 2.210212e-74
## ENSMUSG00000021913_Ogdhl -3.051331 3.0467219 317.0852 6.249057e-71
## ENSMUSG00000107470_Gm3375 5.156573 1.2163947 311.9248 8.316352e-70
## FDR dispersion Mst1Gal3_rep1.x
## ENSMUSG00000050335_Lgals3 6.344372e-176 0.003211607 99.47726
## ENSMUSG00000021908_Gm6768 1.103237e-140 0.001782366 104.59775
## ENSMUSG00000063506_Arhgap22 1.697279e-90 0.033966698 80.35369
## ENSMUSG00000088025_Rprl3 8.425881e-71 0.007818055 1316.05343
## ENSMUSG00000021913_Ogdhl 1.905837e-67 0.006602482 15.51841
## ENSMUSG00000107470_Gm3375 2.113601e-66 0.042465258 125.70615
## Mst1Gal3_rep2.x Mst1Gal3_rep3.x Mst1Gal3_rep4.x
## ENSMUSG00000050335_Lgals3 96.45176 103.27405 93.01363
## ENSMUSG00000021908_Gm6768 101.41651 108.58997 97.80141
## ENSMUSG00000063506_Arhgap22 77.90982 83.42058 75.13264
## ENSMUSG00000088025_Rprl3 1276.02700 1366.28375 1230.54164
## ENSMUSG00000021913_Ogdhl 15.04643 16.11071 14.51009
## ENSMUSG00000107470_Gm3375 121.88292 130.50402 117.53827
## Mst1Gal3_rep5.x MstTg_rep1.x MstTg_rep2.x
## ENSMUSG00000050335_Lgals3 108.26685 1206.1553481 1322.9870992
## ENSMUSG00000021908_Gm6768 113.83978 0.9308369 1.0210005
## ENSMUSG00000063506_Arhgap22 87.45357 408.4030962 447.9622202
## ENSMUSG00000088025_Rprl3 1432.33702 1.0632147 1.1662008
## ENSMUSG00000021913_Ogdhl 16.88958 266.4454734 292.2541649
## ENSMUSG00000107470_Gm3375 136.81326 0.5832351 0.6397289
## MstTg_rep3.x MstTg_rep4.x MstTg_rep5.x MstTg_rep6.x
## ENSMUSG00000050335_Lgals3 1430.1985892 1593.3183547 1815.727468 1351.1534419
## ENSMUSG00000021908_Gm6768 1.1037398 1.2296256 1.401267 1.0427375
## ENSMUSG00000063506_Arhgap22 484.2639325 539.4961357 614.803658 457.4993180
## ENSMUSG00000088025_Rprl3 1.2607067 1.4044953 1.600547 1.1910292
## ENSMUSG00000021913_Ogdhl 315.9376947 351.9716295 401.102864 298.4762444
## ENSMUSG00000107470_Gm3375 0.6915709 0.7704473 0.877993 0.6533487
## MstTg_rep7.x Mst1Gal3_rep1.y Mst1Gal3_rep2.y
## ENSMUSG00000050335_Lgals3 1653.2130010 64 87
## ENSMUSG00000021908_Gm6768 1.2758486 84 103
## ENSMUSG00000063506_Arhgap22 559.7764081 75 68
## ENSMUSG00000088025_Rprl3 1.4572919 660 922
## ENSMUSG00000021913_Ogdhl 365.2026428 13 16
## ENSMUSG00000107470_Gm3375 0.7994093 115 90
## Mst1Gal3_rep3.y Mst1Gal3_rep4.y Mst1Gal3_rep5.y
## ENSMUSG00000050335_Lgals3 109 92 151
## ENSMUSG00000021908_Gm6768 122 107 110
## ENSMUSG00000063506_Arhgap22 76 79 107
## ENSMUSG00000088025_Rprl3 2369 1487 1194
## ENSMUSG00000021913_Ogdhl 11 18 20
## ENSMUSG00000107470_Gm3375 101 186 136
## MstTg_rep1.y MstTg_rep2.y MstTg_rep3.y MstTg_rep4.y
## ENSMUSG00000050335_Lgals3 1126 1438 1344 1679
## ENSMUSG00000021908_Gm6768 0 2 1 1
## ENSMUSG00000063506_Arhgap22 353 535 520 505
## ENSMUSG00000088025_Rprl3 1 1 3 1
## ENSMUSG00000021913_Ogdhl 166 323 345 433
## ENSMUSG00000107470_Gm3375 0 1 1 0
## MstTg_rep5.y MstTg_rep6.y MstTg_rep7.y
## ENSMUSG00000050335_Lgals3 1791 1399 1594
## ENSMUSG00000021908_Gm6768 0 3 1
## ENSMUSG00000063506_Arhgap22 640 485 466
## ENSMUSG00000088025_Rprl3 0 3 0
## ENSMUSG00000021913_Ogdhl 410 227 423
## ENSMUSG00000107470_Gm3375 0 2 1m_tg_v_tggal3ko <- mitch_import(x=tg_v_tggal3ko,DEtype="edger",geneTable=gt)## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 15249## Note: no. genes in output = 15245## Note: estimated proportion of input genes in output = 1dim(m_tg_v_tggal3ko)## [1] 15245 1tail(m_tg_v_tggal3ko)## x
## Zxdb 0.01695960
## Zxdc -0.50202819
## Zyg11b 0.15306581
## Zyx 0.11491587
## Zzef1 -0.09661108
## Zzz3 -0.47507801Mitch with mito genes - big increase.
mt_tg_v_tggal3ko <- mitch_calc(x=m_tg_v_tggal3ko,genesets=mtgenelist,priority="effect",cores=4,minsetsize=3)## Note: Enrichments with large effect sizes may not be
## statistically significant.mt_tg_v_tggal3ko$enrichment_result## set setSize pANOVA s.dist p.adjustANOVA
## 2 mt tRNA 7 2.006395e-04 0.8114488 3.009592e-04
## 1 mtgenes 16 2.070088e-06 0.6852551 6.210264e-06
## 3 mt mRNA 9 2.313995e-03 0.5864619 2.313995e-03Mitch with reactome.
mr_tg_v_tggal3ko <- mitch_calc(x=m_tg_v_tggal3ko,genesets=reactome,priority="effect",cores=4,minsetsize=3)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mr_tg_v_tggal3ko$enrichment_result)## set
## 110 REACTOME_BETA_OXIDATION_OF_LAUROYL_COA_TO_DECANOYL_COA_COA
## 620 REACTOME_MITOCHONDRIAL_FATTY_ACID_BETA_OXIDATION_OF_SATURATED_FATTY_ACIDS
## 108 REACTOME_BETA_OXIDATION_OF_DECANOYL_COA_TO_OCTANOYL_COA_COA
## 111 REACTOME_BETA_OXIDATION_OF_OCTANOYL_COA_TO_HEXANOYL_COA
## 109 REACTOME_BETA_OXIDATION_OF_HEXANOYL_COA_TO_BUTANOYL_COA
## 1229 REACTOME_UTILIZATION_OF_KETONE_BODIES
## setSize pANOVA s.dist p.adjustANOVA
## 110 5 3.437378e-04 0.9243570 4.493656e-03
## 620 9 2.026991e-06 0.9142820 5.915986e-05
## 108 6 1.194476e-04 0.9068399 1.921881e-03
## 111 5 4.696032e-04 0.9030971 5.893520e-03
## 109 5 4.802775e-04 0.9015486 5.967805e-03
## 1229 3 9.534769e-03 0.8641473 5.439152e-02# some subtle changes in electron transport
mr2_tg_v_tggal3ko <- mr_tg_v_tggal3ko$enrichment_result
head(mr2_tg_v_tggal3ko[grep("MITOC",mr2_tg_v_tggal3ko$set),])## set
## 620 REACTOME_MITOCHONDRIAL_FATTY_ACID_BETA_OXIDATION_OF_SATURATED_FATTY_ACIDS
## 621 REACTOME_MITOCHONDRIAL_IRON_SULFUR_CLUSTER_BIOGENESIS
## 619 REACTOME_MITOCHONDRIAL_FATTY_ACID_BETA_OXIDATION
## 624 REACTOME_MITOCHONDRIAL_TRANSLATION
## 622 REACTOME_MITOCHONDRIAL_PROTEIN_DEGRADATION
## 623 REACTOME_MITOCHONDRIAL_PROTEIN_IMPORT
## setSize pANOVA s.dist p.adjustANOVA
## 620 9 2.026991e-06 0.9142820 5.915986e-05
## 621 10 3.062275e-05 0.7612077 6.004931e-04
## 619 33 2.009804e-13 0.7387230 2.101920e-11
## 624 87 2.882071e-27 0.6701988 9.042497e-25
## 622 70 9.865125e-20 0.6279445 1.768676e-17
## 623 7 4.624990e-03 0.6181164 3.129309e-02head(mr2_tg_v_tggal3ko[grep("ELECTRON",mr2_tg_v_tggal3ko$set),])## set setSize
## 915 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT 124
## 46 REACTOME_AEROBIC_RESPIRATION_AND_RESPIRATORY_ELECTRON_TRANSPORT 202
## pANOVA s.dist p.adjustANOVA
## 915 2.202680e-45 0.7336944 9.214544e-43
## 46 8.163806e-58 0.6527460 5.122788e-55