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library("DESeq2")
library("mitch")
library("dplyr")
library("kableExtra")

download.file(url = "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE118089&format=file&file=GSE118089%5Fstan%5Fmziemann%5F2018%5Fcountmatrix%2Etsv%2Egz",
  destfile="GSE118089.tsv")

x <- read.table("GSE118089.tsv",header=TRUE,row.names=1)
head(x)
##                          CtrlPMX_rep1 CtrlPMX_rep2 CtrlPMX_rep3 CtrlPMX_rep4
## ENSMUSG00000000001_Gnai3         1093         1180         1189         1317
## ENSMUSG00000000003_Pbsn             0            0            0            0
## ENSMUSG00000000028_Cdc45           25           22           32           17
## ENSMUSG00000000031_H19              0            0            0            0
## ENSMUSG00000000037_Scml2            2            3            3            4
## ENSMUSG00000000049_Apoh           132          157          218          194
##                          CtrlPMX_rep5 CtrlPMX_rep6 CtrlUntr_rep1 CtrlUntr_rep2
## ENSMUSG00000000001_Gnai3         1116         1273          1293          1150
## ENSMUSG00000000003_Pbsn             0            0             0             0
## ENSMUSG00000000028_Cdc45           25           24            16            20
## ENSMUSG00000000031_H19              0            0             0             0
## ENSMUSG00000000037_Scml2            2            1             3             4
## ENSMUSG00000000049_Apoh            67           89           226           148
##                          CtrlUntr_rep3 CtrlUntr_rep4 CtrlUntr_rep5
## ENSMUSG00000000001_Gnai3           914          1089          1080
## ENSMUSG00000000003_Pbsn              0             0             0
## ENSMUSG00000000028_Cdc45            12            14            19
## ENSMUSG00000000031_H19               0             0             1
## ENSMUSG00000000037_Scml2             3             2             6
## ENSMUSG00000000049_Apoh            137           186            72
##                          CtrlUntr_rep6 DiabPMX_rep1 DiabPMX_rep2 DiabPMX_rep3
## ENSMUSG00000000001_Gnai3          1087         1126          834         1255
## ENSMUSG00000000003_Pbsn              0            0            0            0
## ENSMUSG00000000028_Cdc45            25           22           18           20
## ENSMUSG00000000031_H19               0            0            0            1
## ENSMUSG00000000037_Scml2             3            6            3            4
## ENSMUSG00000000049_Apoh            161           22           30           69
##                          DiabPMX_rep4 DiabPMX_rep5 DiabPMX_rep6 DiabUntr_rep1
## ENSMUSG00000000001_Gnai3         1187         1121          837          1205
## ENSMUSG00000000003_Pbsn             0            0            0             0
## ENSMUSG00000000028_Cdc45           17           18           20            14
## ENSMUSG00000000031_H19              1            0            2             0
## ENSMUSG00000000037_Scml2            4            8            5             3
## ENSMUSG00000000049_Apoh            64           51           22            50
##                          DiabUntr_rep2 DiabUntr_rep3 DiabUntr_rep4
## ENSMUSG00000000001_Gnai3          1465          1261          1071
## ENSMUSG00000000003_Pbsn              0             0             0
## ENSMUSG00000000028_Cdc45            37            28            24
## ENSMUSG00000000031_H19               2             0             0
## ENSMUSG00000000037_Scml2             2             8             2
## ENSMUSG00000000049_Apoh             31            21           123
##                          DiabUntr_rep5 DiabUntr_rep6
## ENSMUSG00000000001_Gnai3          1346          1460
## ENSMUSG00000000003_Pbsn              0             0
## ENSMUSG00000000028_Cdc45            25            25
## ENSMUSG00000000031_H19               0             1
## ENSMUSG00000000037_Scml2             3             4
## ENSMUSG00000000049_Apoh            168            37
ss <- as.data.frame(colnames(x))
colnames(ss) <- "samplename"
ss$grp <- sapply(strsplit(colnames(x),"_"),"[[",1)

# contrast 1 is non-diabetic versus diabetic
ss1 <- subset(ss,grp=="CtrlUntr" | grp=="DiabUntr")
ss1$trt <- as.factor(grepl("Diab",ss1$grp))
xx1 <- x[,which(colnames(x) %in% ss1$samplename)]
#xx2 <- xx2[which(rowMeans(xx2)>10),]
ss1
##       samplename      grp   trt
## 7  CtrlUntr_rep1 CtrlUntr FALSE
## 8  CtrlUntr_rep2 CtrlUntr FALSE
## 9  CtrlUntr_rep3 CtrlUntr FALSE
## 10 CtrlUntr_rep4 CtrlUntr FALSE
## 11 CtrlUntr_rep5 CtrlUntr FALSE
## 12 CtrlUntr_rep6 CtrlUntr FALSE
## 19 DiabUntr_rep1 DiabUntr  TRUE
## 20 DiabUntr_rep2 DiabUntr  TRUE
## 21 DiabUntr_rep3 DiabUntr  TRUE
## 22 DiabUntr_rep4 DiabUntr  TRUE
## 23 DiabUntr_rep5 DiabUntr  TRUE
## 24 DiabUntr_rep6 DiabUntr  TRUE
head(xx1)
##                          CtrlUntr_rep1 CtrlUntr_rep2 CtrlUntr_rep3
## ENSMUSG00000000001_Gnai3          1293          1150           914
## ENSMUSG00000000003_Pbsn              0             0             0
## ENSMUSG00000000028_Cdc45            16            20            12
## ENSMUSG00000000031_H19               0             0             0
## ENSMUSG00000000037_Scml2             3             4             3
## ENSMUSG00000000049_Apoh            226           148           137
##                          CtrlUntr_rep4 CtrlUntr_rep5 CtrlUntr_rep6
## ENSMUSG00000000001_Gnai3          1089          1080          1087
## ENSMUSG00000000003_Pbsn              0             0             0
## ENSMUSG00000000028_Cdc45            14            19            25
## ENSMUSG00000000031_H19               0             1             0
## ENSMUSG00000000037_Scml2             2             6             3
## ENSMUSG00000000049_Apoh            186            72           161
##                          DiabUntr_rep1 DiabUntr_rep2 DiabUntr_rep3
## ENSMUSG00000000001_Gnai3          1205          1465          1261
## ENSMUSG00000000003_Pbsn              0             0             0
## ENSMUSG00000000028_Cdc45            14            37            28
## ENSMUSG00000000031_H19               0             2             0
## ENSMUSG00000000037_Scml2             3             2             8
## ENSMUSG00000000049_Apoh             50            31            21
##                          DiabUntr_rep4 DiabUntr_rep5 DiabUntr_rep6
## ENSMUSG00000000001_Gnai3          1071          1346          1460
## ENSMUSG00000000003_Pbsn              0             0             0
## ENSMUSG00000000028_Cdc45            24            25            25
## ENSMUSG00000000031_H19               0             0             1
## ENSMUSG00000000037_Scml2             2             3             4
## ENSMUSG00000000049_Apoh            123           168            37
# contrast 2 is diabetic versus diabetic treated with PMC53
ss2 <- subset(ss,grp=="DiabUntr" | grp=="DiabPMX")
ss2$trt <- as.factor(grepl("PMX",ss2$grp))
xx2 <- x[,which(colnames(x) %in% ss2$samplename)]
#xx2 <- xx2[which(rowMeans(xx2)>10),]
ss2
##       samplename      grp   trt
## 13  DiabPMX_rep1  DiabPMX  TRUE
## 14  DiabPMX_rep2  DiabPMX  TRUE
## 15  DiabPMX_rep3  DiabPMX  TRUE
## 16  DiabPMX_rep4  DiabPMX  TRUE
## 17  DiabPMX_rep5  DiabPMX  TRUE
## 18  DiabPMX_rep6  DiabPMX  TRUE
## 19 DiabUntr_rep1 DiabUntr FALSE
## 20 DiabUntr_rep2 DiabUntr FALSE
## 21 DiabUntr_rep3 DiabUntr FALSE
## 22 DiabUntr_rep4 DiabUntr FALSE
## 23 DiabUntr_rep5 DiabUntr FALSE
## 24 DiabUntr_rep6 DiabUntr FALSE
head(xx2)
##                          DiabPMX_rep1 DiabPMX_rep2 DiabPMX_rep3 DiabPMX_rep4
## ENSMUSG00000000001_Gnai3         1126          834         1255         1187
## ENSMUSG00000000003_Pbsn             0            0            0            0
## ENSMUSG00000000028_Cdc45           22           18           20           17
## ENSMUSG00000000031_H19              0            0            1            1
## ENSMUSG00000000037_Scml2            6            3            4            4
## ENSMUSG00000000049_Apoh            22           30           69           64
##                          DiabPMX_rep5 DiabPMX_rep6 DiabUntr_rep1 DiabUntr_rep2
## ENSMUSG00000000001_Gnai3         1121          837          1205          1465
## ENSMUSG00000000003_Pbsn             0            0             0             0
## ENSMUSG00000000028_Cdc45           18           20            14            37
## ENSMUSG00000000031_H19              0            2             0             2
## ENSMUSG00000000037_Scml2            8            5             3             2
## ENSMUSG00000000049_Apoh            51           22            50            31
##                          DiabUntr_rep3 DiabUntr_rep4 DiabUntr_rep5
## ENSMUSG00000000001_Gnai3          1261          1071          1346
## ENSMUSG00000000003_Pbsn              0             0             0
## ENSMUSG00000000028_Cdc45            28            24            25
## ENSMUSG00000000031_H19               0             0             0
## ENSMUSG00000000037_Scml2             8             2             3
## ENSMUSG00000000049_Apoh             21           123           168
##                          DiabUntr_rep6
## ENSMUSG00000000001_Gnai3          1460
## ENSMUSG00000000003_Pbsn              0
## ENSMUSG00000000028_Cdc45            25
## ENSMUSG00000000031_H19               1
## ENSMUSG00000000037_Scml2             4
## ENSMUSG00000000049_Apoh             37

DESeq2

Run DE analysis

# contrast 1
dds <- DESeqDataSetFromMatrix(countData = xx1 , colData = ss1, design = ~ trt )
res <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
z<- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge,10)
##                              baseMean log2FoldChange      lfcSE      stat
## ENSMUSG00000025453_Nnt     2908.02070       1.245600 0.04873625  25.55799
## ENSMUSG00000029456_Acad10   739.10509      -2.428930 0.09504866 -25.55459
## ENSMUSG00000017929_B4galt5 1709.27639      -1.860560 0.08006758 -23.23737
## ENSMUSG00000023067_Cdkn1a   341.48531       3.578513 0.19339272  18.50387
## ENSMUSG00000073643_Wdfy1    839.65967       1.295093 0.07001707  18.49682
## ENSMUSG00000038776_Ephx1    834.28830       2.221397 0.12019941  18.48094
## ENSMUSG00000053279_Aldh1a1  583.42567       2.846482 0.16715139  17.02937
## ENSMUSG00000034457_Eda2r     78.00454       3.149025 0.18968483  16.60135
## ENSMUSG00000017390_Aldoc    126.46764       1.969574 0.14044646  14.02366
## ENSMUSG00000028553_Angptl3  115.77935       2.361842 0.17035149  13.86452
##                                   pvalue          padj CtrlUntr_rep1
## ENSMUSG00000025453_Nnt     4.475752e-144 5.093687e-140     10.825867
## ENSMUSG00000029456_Acad10  4.882050e-144 5.093687e-140     10.486111
## ENSMUSG00000017929_B4galt5 1.909154e-119 1.327944e-115     11.673091
## ENSMUSG00000023067_Cdkn1a   1.921843e-76  9.140405e-73      7.471190
## ENSMUSG00000073643_Wdfy1    2.190158e-76  9.140405e-73      9.239526
## ENSMUSG00000038776_Ephx1    2.940404e-76  1.022623e-72      8.445448
## ENSMUSG00000053279_Aldh1a1  4.974004e-65  1.482751e-61      8.056788
## ENSMUSG00000034457_Eda2r    6.813907e-62  1.777322e-58      6.441342
## ENSMUSG00000017390_Aldoc    1.116970e-44  2.589756e-41      7.073569
## ENSMUSG00000028553_Angptl3  1.039151e-43  2.168396e-40      6.943748
##                            CtrlUntr_rep2 CtrlUntr_rep3 CtrlUntr_rep4
## ENSMUSG00000025453_Nnt         10.883609     10.902008     10.784909
## ENSMUSG00000029456_Acad10      10.272884     10.434241     10.380339
## ENSMUSG00000017929_B4galt5     11.392385     11.320996     11.466744
## ENSMUSG00000023067_Cdkn1a       7.064772      6.885848      7.079425
## ENSMUSG00000073643_Wdfy1        9.087368      9.234962      9.195653
## ENSMUSG00000038776_Ephx1        8.647438      8.616624      8.705959
## ENSMUSG00000053279_Aldh1a1      7.834841      7.669067      7.931195
## ENSMUSG00000034457_Eda2r        6.514404      6.570329      6.413605
## ENSMUSG00000017390_Aldoc        7.039712      7.080032      6.940763
## ENSMUSG00000028553_Angptl3      7.014046      6.811798      7.026135
##                            CtrlUntr_rep5 CtrlUntr_rep6 DiabUntr_rep1
## ENSMUSG00000025453_Nnt         10.854254     10.751312     12.093445
## ENSMUSG00000029456_Acad10      10.305639     10.483572      8.312555
## ENSMUSG00000017929_B4galt5     11.272372     11.488567      9.719292
## ENSMUSG00000023067_Cdkn1a       7.004038      6.876049      9.697477
## ENSMUSG00000073643_Wdfy1        9.141127      9.239482     10.363835
## ENSMUSG00000038776_Ephx1        8.821032      8.367145     10.437900
## ENSMUSG00000053279_Aldh1a1      8.083080      7.542202     10.231231
## ENSMUSG00000034457_Eda2r        6.422391      6.454757      7.911158
## ENSMUSG00000017390_Aldoc        7.175417      7.112012      8.179015
## ENSMUSG00000028553_Angptl3      6.779467      6.635185      7.897905
##                            DiabUntr_rep2 DiabUntr_rep3 DiabUntr_rep4
## ENSMUSG00000025453_Nnt         12.128164     11.967625     12.035900
## ENSMUSG00000029456_Acad10       8.126028      8.159646      8.484397
## ENSMUSG00000017929_B4galt5      9.807044      9.716448      9.803545
## ENSMUSG00000023067_Cdkn1a       9.847079      9.503857      9.092454
## ENSMUSG00000073643_Wdfy1       10.431679     10.407184     10.190536
## ENSMUSG00000038776_Ephx1       10.645217     10.775516     10.167569
## ENSMUSG00000053279_Aldh1a1     10.281855     10.312555      9.479411
## ENSMUSG00000034457_Eda2r        7.974223      7.735623      7.727002
## ENSMUSG00000017390_Aldoc        8.427913      8.127210      8.136706
## ENSMUSG00000028553_Angptl3      8.198151      8.353478      8.005746
##                            DiabUntr_rep5 DiabUntr_rep6
## ENSMUSG00000025453_Nnt         11.959817     12.009150
## ENSMUSG00000029456_Acad10       8.468857      8.481024
## ENSMUSG00000017929_B4galt5      9.547557      9.653268
## ENSMUSG00000023067_Cdkn1a       9.319871      9.479027
## ENSMUSG00000073643_Wdfy1       10.196129     10.411032
## ENSMUSG00000038776_Ephx1       10.613216     10.440216
## ENSMUSG00000053279_Aldh1a1     10.250941     10.099048
## ENSMUSG00000034457_Eda2r        7.957011      7.792649
## ENSMUSG00000017390_Aldoc        7.997879      8.312550
## ENSMUSG00000028553_Angptl3      8.290578      8.201983
dge1 <- dge

# contrast 2
dds <- DESeqDataSetFromMatrix(countData = xx2 , colData = ss2, design = ~ trt )
res <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
z<- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge,10)
##                             baseMean log2FoldChange      lfcSE      stat
## ENSMUSG00000025453_Nnt     2988.3007      -1.156044 0.04725936 -24.46170
## ENSMUSG00000029456_Acad10   638.9908       2.162476 0.11051542  19.56719
## ENSMUSG00000073643_Wdfy1    836.6600      -1.354469 0.07912676 -17.11772
## ENSMUSG00000056999_Ide     1793.0178       1.157567 0.08160812  14.18446
## ENSMUSG00000017929_B4galt5 1211.5587       1.172109 0.09120944  12.85075
## ENSMUSG00000053279_Aldh1a1  658.3801      -1.865866 0.15568745 -11.98469
## ENSMUSG00000023087_Noct     326.3234      -2.076479 0.17821774 -11.65136
## ENSMUSG00000069456_Rdh16   2739.2636      -1.074212 0.09621504 -11.16470
## ENSMUSG00000028553_Angptl3  124.9328      -1.842438 0.16904237 -10.89927
## ENSMUSG00000017390_Aldoc    141.2130      -1.365104 0.12582063 -10.84961
##                                   pvalue          padj DiabPMX_rep1
## ENSMUSG00000025453_Nnt     3.778368e-132 7.319832e-128    10.865124
## ENSMUSG00000029456_Acad10   2.944649e-85  2.852334e-81     9.992488
## ENSMUSG00000073643_Wdfy1    1.094881e-65  7.070374e-62     9.008366
## ENSMUSG00000056999_Ide      1.143410e-45  5.537819e-42    11.367345
## ENSMUSG00000017929_B4galt5  8.518583e-38  3.300610e-34    11.015373
## ENSMUSG00000053279_Aldh1a1  4.274291e-33  1.380097e-29     8.787975
## ENSMUSG00000023087_Noct     2.258293e-31  6.249988e-28     7.497448
## ENSMUSG00000069456_Rdh16    6.069706e-29  1.469855e-25    10.791058
## ENSMUSG00000028553_Angptl3  1.161853e-27  2.500953e-24     6.893295
## ENSMUSG00000017390_Aldoc    2.002884e-27  3.880187e-24     7.239468
##                            DiabPMX_rep2 DiabPMX_rep3 DiabPMX_rep4 DiabPMX_rep5
## ENSMUSG00000025453_Nnt        10.914253    10.896440    10.987304    10.999448
## ENSMUSG00000029456_Acad10     10.104074    10.191074    10.436843    10.087527
## ENSMUSG00000073643_Wdfy1       9.122613     9.224170     9.301376     9.014461
## ENSMUSG00000056999_Ide        11.461997    11.081605    11.396441    11.420112
## ENSMUSG00000017929_B4galt5    10.903271    10.804816    10.678911    10.484287
## ENSMUSG00000053279_Aldh1a1     8.554285     8.624737     8.487722     8.143909
## ENSMUSG00000023087_Noct        7.551428     7.862917     7.659562     8.000887
## ENSMUSG00000069456_Rdh16      10.717401    10.874074    10.950176    10.819799
## ENSMUSG00000028553_Angptl3     7.008650     7.120368     7.273984     7.189476
## ENSMUSG00000017390_Aldoc       7.412681     7.276321     7.312747     7.324672
##                            DiabPMX_rep6 DiabUntr_rep1 DiabUntr_rep2
## ENSMUSG00000025453_Nnt        10.889002     12.101595     12.137937
## ENSMUSG00000029456_Acad10     10.065203      8.295439      8.106785
## ENSMUSG00000073643_Wdfy1       9.117871     10.366900     10.436566
## ENSMUSG00000056999_Ide        11.204640     10.154003     10.199522
## ENSMUSG00000017929_B4galt5    10.808371      9.718428      9.808253
## ENSMUSG00000053279_Aldh1a1     8.593863     10.233613     10.285989
## ENSMUSG00000023087_Noct        7.647917      9.213709      9.304922
## ENSMUSG00000069456_Rdh16      10.992068     11.926795     11.926221
## ENSMUSG00000028553_Angptl3     6.759323      7.873130      8.180223
## ENSMUSG00000017390_Aldoc       7.422691      8.159601      8.413867
##                            DiabUntr_rep3 DiabUntr_rep4 DiabUntr_rep5
## ENSMUSG00000025453_Nnt         11.974613     12.047500     11.970626
## ENSMUSG00000029456_Acad10       8.139191      8.472774      8.456475
## ENSMUSG00000073643_Wdfy1       10.409545     10.196035     10.201036
## ENSMUSG00000056999_Ide         10.365046     10.380938     10.176444
## ENSMUSG00000017929_B4galt5      9.714689      9.806543      9.547941
## ENSMUSG00000053279_Aldh1a1     10.314453      9.479795     10.256153
## ENSMUSG00000023087_Noct         9.089563      8.985992      9.034613
## ENSMUSG00000069456_Rdh16       12.013290     11.451540     11.912987
## ENSMUSG00000028553_Angptl3      8.336309      7.985580      8.275249
## ENSMUSG00000017390_Aldoc        8.106173      8.119085      7.977095
##                            DiabUntr_rep6
## ENSMUSG00000025453_Nnt         12.021026
## ENSMUSG00000029456_Acad10       8.469598
## ENSMUSG00000073643_Wdfy1       10.418008
## ENSMUSG00000056999_Ide         10.107143
## ENSMUSG00000017929_B4galt5      9.655412
## ENSMUSG00000053279_Aldh1a1     10.104311
## ENSMUSG00000023087_Noct         9.841711
## ENSMUSG00000069456_Rdh16       12.064578
## ENSMUSG00000028553_Angptl3      8.185806
## ENSMUSG00000017390_Aldoc        8.298338
dge2 <- dge

Mitch pathway analysis

gt <- as.data.frame(rownames(x))

head(gt)
##                rownames(x)
## 1 ENSMUSG00000000001_Gnai3
## 2  ENSMUSG00000000003_Pbsn
## 3 ENSMUSG00000000028_Cdc45
## 4   ENSMUSG00000000031_H19
## 5 ENSMUSG00000000037_Scml2
## 6  ENSMUSG00000000049_Apoh
gt$gene <- sapply(strsplit(rownames(x),"_"),"[[",2)

xl <- list("diab"=dge1,"PMX"=dge2)

m <- mitch_import(x=xl,DEtype = "deseq2", geneTable = gt)
## Note: Mean no. genes in input = 48526
## Note: no. genes in output = 28178
## Note: estimated proportion of input genes in output = 0.581
head(m)
##                     diab       PMX
## 0610006L08Rik -5.5412223 1.7043723
## 0610007P14Rik -0.5043192 0.5154128
## 0610009B22Rik -1.1468556 0.7601283
## 0610009E02Rik -1.0617246 0.7364015
## 0610009L18Rik -3.4134523 2.9613348
## 0610009O20Rik -1.1413048 1.7889029
# reactome converted to human on 22nd Sept
genesets <- gmt_import("ReactomePathways_mouse.gmt")
head(genesets)
## $`2-LTR-circle-formation`
## [1] "Banf1" "Hmga1" "Lig4"  "Psip1" "Xrcc4" "Xrcc5" "Xrcc6"
## 
## $`5-Phosphoribose-1-diphosphate-biosynthesis`
## [1] "Prps1l1" "Prps1l3" "Prps2"  
## 
## $`A-tetrasaccharide-linker-sequence-is-required-for-GAG-synthesis`
##  [1] "Agrn"    "B3galt6" "B3gat1"  "B3gat2"  "B3gat3"  "B4galt7" "Bcan"   
##  [8] "Bgn"     "Cspg4"   "Cspg5"   "Dcn"     "Gm43720" "Gpc1"    "Gpc3"   
## [15] "Gpc4"    "Gpc5"    "Gpc6"    "Hspg2"   "Ncan"    "Sdc1"    "Sdc2"   
## [22] "Sdc3"    "Sdc4"    "Vcan"    "Xylt1"   "Xylt2"  
## 
## $`ABC-transporter-disorders`
##  [1] "Abca1"  "Abca12" "Abca3"  "Abcb11" "Abcb4"  "Abcb6"  "Abcc2"  "Abcc6" 
##  [9] "Abcc8"  "Abcc9"  "Abcd1"  "Abcd4"  "Abcg5"  "Abcg8"  "Apoa1"  "Cftr"  
## [17] "Derl1"  "Derl2"  "Derl3"  "Erlec1" "Erlin1" "Erlin2" "Kcnj11" "Lmbrd1"
## [25] "Os9"    "Psma1"  "Psma2"  "Psma3"  "Psma4"  "Psma5"  "Psma6"  "Psma7" 
## [33] "Psma8"  "Psmb1"  "Psmb10" "Psmb11" "Psmb2"  "Psmb3"  "Psmb4"  "Psmb5" 
## [41] "Psmb6"  "Psmb7"  "Psmb8"  "Psmb9"  "Psmc1"  "Psmc2"  "Psmc3"  "Psmc4" 
## [49] "Psmc5"  "Psmc6"  "Psmd1"  "Psmd10" "Psmd11" "Psmd12" "Psmd13" "Psmd14"
## [57] "Psmd2"  "Psmd3"  "Psmd4"  "Psmd5"  "Psmd6"  "Psmd7"  "Psmd8"  "Psmd9" 
## [65] "Psme1"  "Psme2b" "Psme3"  "Psme4"  "Psmf1"  "Rnf185" "Rnf5"   "Rps27a"
## [73] "Sel1l"  "Uba52"  "Ubb"    "Vcp"   
## 
## $`ABC-transporters-in-lipid-homeostasis`
##  [1] "Abca12" "Abca2"  "Abca3"  "Abca5"  "Abca6"  "Abca7"  "Abca9"  "Abcd1" 
##  [9] "Abcd2"  "Abcd3"  "Abcg1"  "Abcg4"  "Abcg5"  "Abcg8"  "Apoa1"  "Pex19" 
## [17] "Pex3"  
## 
## $`ABC-family-proteins-mediated-transport`
##  [1] "Abca12"  "Abca2"   "Abca3"   "Abca4"   "Abca5"   "Abca6"   "Abca7"  
##  [8] "Abca8b"  "Abca9"   "Abcb10"  "Abcb1a"  "Abcb4"   "Abcb5"   "Abcb6"  
## [15] "Abcb7"   "Abcb8"   "Abcb9"   "Abcc2"   "Abcc3"   "Abcc4"   "Abcc5"  
## [22] "Abcc6"   "Abcc9"   "Abcd1"   "Abcd2"   "Abcd3"   "Abcf1"   "Abcg1"  
## [29] "Abcg4"   "Abcg5"   "Abcg8"   "Apoa1"   "Cftr"    "Derl1"   "Derl2"  
## [36] "Derl3"   "Eif2s1"  "Eif2s2"  "Eif2s3x" "Erlec1"  "Erlin1"  "Erlin2" 
## [43] "Kcnj11"  "Os9"     "Pex19"   "Pex3"    "Psma1"   "Psma2"   "Psma3"  
## [50] "Psma4"   "Psma5"   "Psma6"   "Psma7"   "Psma8"   "Psmb1"   "Psmb10" 
## [57] "Psmb11"  "Psmb2"   "Psmb3"   "Psmb4"   "Psmb5"   "Psmb6"   "Psmb7"  
## [64] "Psmb8"   "Psmb9"   "Psmc1"   "Psmc2"   "Psmc3"   "Psmc4"   "Psmc5"  
## [71] "Psmc6"   "Psmd1"   "Psmd10"  "Psmd11"  "Psmd12"  "Psmd13"  "Psmd14" 
## [78] "Psmd2"   "Psmd3"   "Psmd4"   "Psmd5"   "Psmd6"   "Psmd7"   "Psmd8"  
## [85] "Psmd9"   "Psme1"   "Psme2b"  "Psme3"   "Psme4"   "Psmf1"   "Rnf185" 
## [92] "Rnf5"    "Rps27a"  "Sel1l"   "Uba52"   "Ubb"     "Vcp"
res <- mitch_calc(m,genesets , priority = "significance")
## Note: When prioritising by significance (ie: small 
##             p-values), large effect sizes might be missed.
head(res$enrichment_result,50) %>% kbl() %>%   kable_paper("hover", full_width = F)
set setSize pMANOVA s.diab s.PMX p.diab p.PMX s.dist SD p.adjustMANOVA
1096 Response-of-EIF2AK4-(GCN2)-to-amino-acid-deficiency 85 0.0e+00 0.6652396 -0.7566319 0.0000000 0.0000000 1.0074897 1.0054150 0.00e+00
1431 Viral-mRNA-Translation 73 0.0e+00 0.7014620 -0.8022903 0.0000000 0.0000000 1.0657010 1.0633135 0.00e+00
860 Peptide-chain-elongation 73 0.0e+00 0.6989898 -0.7890377 0.0000000 0.0000000 1.0541192 1.0521944 0.00e+00
429 Formation-of-a-pool-of-free-40S-subunits 85 0.0e+00 0.6529024 -0.7251457 0.0000000 0.0000000 0.9757653 0.9744272 0.00e+00
470 GTP-hydrolysis-and-joining-of-the-60S-ribosomal-subunit 96 0.0e+00 0.5778998 -0.6840451 0.0000000 0.0000000 0.8954808 0.8923298 0.00e+00
381 Eukaryotic-Translation-Elongation 77 0.0e+00 0.6654239 -0.7529034 0.0000000 0.0000000 1.0048147 1.0029089 0.00e+00
630 L13a-mediated-translational-silencing-of-Ceruloplasmin-expression 95 0.0e+00 0.5736949 -0.6707032 0.0000000 0.0000000 0.8825920 0.8799223 0.00e+00
182 Cellular-response-to-starvation 138 0.0e+00 0.5169261 -0.5210817 0.0000000 0.0000000 0.7339882 0.7339824 0.00e+00
156 Cap-dependent-Translation-Initiation 103 0.0e+00 0.5505129 -0.6299890 0.0000000 0.0000000 0.8366305 0.8347409 0.00e+00
382 Eukaryotic-Translation-Initiation 103 0.0e+00 0.5505129 -0.6299890 0.0000000 0.0000000 0.8366305 0.8347409 0.00e+00
383 Eukaryotic-Translation-Termination 77 0.0e+00 0.6462038 -0.7227561 0.0000000 0.0000000 0.9695132 0.9680008 0.00e+00
791 Nonsense-Mediated-Decay-(NMD)-independent-of-the-Exon-Junction-Complex-(EJC) 79 0.0e+00 0.6165137 -0.6895551 0.0000000 0.0000000 0.9249732 0.9235301 0.00e+00
1150 Selenocysteine-synthesis 76 0.0e+00 0.6236080 -0.6963243 0.0000000 0.0000000 0.9347483 0.9333331 0.00e+00
1129 SRP-dependent-cotranslational-protein-targeting-to-membrane 96 0.0e+00 0.4649397 -0.6332259 0.0000000 0.0000000 0.7855851 0.7765204 0.00e+00
566 Influenza-Viral-RNA-Transcription-and-Replication 118 0.0e+00 0.4887061 -0.5160120 0.0000000 0.0000000 0.7107053 0.7104430 0.00e+00
1067 Regulation-of-expression-of-SLITs-and-ROBOs 149 0.0e+00 0.3794938 -0.4750703 0.0000000 0.0000000 0.6080356 0.6042681 0.00e+00
790 Nonsense-Mediated-Decay-(NMD)-enhanced-by-the-Exon-Junction-Complex-(EJC) 99 0.0e+00 0.5070057 -0.5638953 0.0000000 0.0000000 0.7583091 0.7572413 0.00e+00
792 Nonsense-Mediated-Decay-(NMD) 99 0.0e+00 0.5070057 -0.5638953 0.0000000 0.0000000 0.7583091 0.7572413 0.00e+00
1149 Selenoamino-acid-metabolism 91 0.0e+00 0.4964672 -0.5696762 0.0000000 0.0000000 0.7556525 0.7538772 0.00e+00
565 Influenza-Infection 134 0.0e+00 0.4225862 -0.4537648 0.0000000 0.0000000 0.6200658 0.6196738 0.00e+00
1341 The-citric-acid-(TCA)-cycle-and-respiratory-electron-transport 155 0.0e+00 0.3442179 -0.4344383 0.0000000 0.0000000 0.5542767 0.5505931 0.00e+00
1093 Respiratory-electron-transport 99 0.0e+00 0.4302327 -0.5274937 0.0000000 0.0000000 0.6806980 0.6772148 0.00e+00
1094 Respiratory-electron-transport,-ATP-synthesis-by-chemiosmotic-coupling,-and-heat-production-by-uncoupling-proteins. 107 0.0e+00 0.4064936 -0.4986485 0.0000000 0.0000000 0.6433408 0.6400321 0.00e+00
1237 Signaling-by-ROBO-receptors 192 0.0e+00 0.3064101 -0.3669988 0.0000000 0.0000000 0.4780954 0.4761720 0.00e+00
434 Formation-of-the-ternary-complex,-and-subsequently,-the-43S-complex 46 0.0e+00 0.6599007 -0.7006813 0.0000000 0.0000000 0.9625088 0.9620767 0.00e+00
670 Major-pathway-of-rRNA-processing-in-the-nucleolus-and-cytosol 162 0.0e+00 0.2996837 -0.3900095 0.0000000 0.0000000 0.4918513 0.4876867 0.00e+00
1105 Ribosomal-scanning-and-start-codon-recognition 53 0.0e+00 0.5447782 -0.6408124 0.0000000 0.0000000 0.8410849 0.8383392 0.00e+00
1473 rRNA-processing 191 0.0e+00 0.2648030 -0.3437727 0.0000000 0.0000000 0.4339359 0.4303280 0.00e+00
1382 Translation 255 0.0e+00 0.2128970 -0.2984143 0.0000000 0.0000000 0.3665736 0.3615517 0.00e+00
56 Activation-of-the-mRNA-upon-binding-of-the-cap-binding-complex-and-eIFs,-and-subsequent-binding-to-43S 54 0.0e+00 0.5405782 -0.6230674 0.0000000 0.0000000 0.8248866 0.8228217 0.00e+00
1475 rRNA-processing-in-the-nucleus-and-cytosol 172 0.0e+00 0.2657084 -0.3551643 0.0000000 0.0000000 0.4435568 0.4390233 0.00e+00
1383 Translation-initiation-complex-formation 53 0.0e+00 0.5323478 -0.6166413 0.0000000 0.0000000 0.8146415 0.8124580 0.00e+00
682 Metabolism-of-amino-acids-and-derivatives 335 0.0e+00 0.1471135 -0.2427661 0.0000035 0.0000000 0.2838622 0.2756865 0.00e+00
217 Complex-I-biogenesis 54 0.0e+00 0.5002449 -0.5675026 0.0000000 0.0000000 0.7565079 0.7550116 0.00e+00
678 Metabolism 1892 0.0e+00 0.0909057 -0.0946164 0.0000000 0.0000000 0.1312102 0.1311839 0.00e+00
387 Extracellular-matrix-organization 274 0.0e+00 -0.0423271 0.2123752 0.2271889 0.0000000 0.2165521 0.1801017 0.00e+00
184 Cellular-responses-to-stress 635 0.0e+00 0.0905011 -0.1428677 0.0000939 0.0000000 0.1691202 0.1650167 2.00e-07
778 Neuronal-System 352 0.0e+00 0.1214022 0.0232887 0.0000882 0.4520067 0.1236157 0.0693767 3.00e-07
183 Cellular-responses-to-stimuli 639 0.0e+00 0.0891441 -0.1405230 0.0001138 0.0000000 0.1664133 0.1623992 3.00e-07
489 Gluconeogenesis 30 0.0e+00 0.6001670 -0.5652255 0.0000000 0.0000001 0.8244272 0.8240569 5.00e-07
680 Metabolism-of-RNA 646 0.0e+00 0.0408922 -0.1214044 0.0751630 0.0000001 0.1281062 0.1147611 1.30e-06
105 Axon-guidance 493 0.0e+00 0.1528173 -0.1094820 0.0000000 0.0000299 0.1879879 0.1854736 1.40e-06
616 Ion-channel-transport 167 2.0e-07 0.2003208 -0.0275987 0.0000078 0.5379436 0.2022130 0.1611634 5.70e-06
557 Immunoregulatory-interactions-between-a-Lymphoid-and-a-non-Lymphoid-cell 94 2.0e-07 -0.2581655 0.3300657 0.0000150 0.0000000 0.4190379 0.4159422 5.70e-06
774 Nervous-system-development 517 2.0e-07 0.1416926 -0.1041920 0.0000000 0.0000478 0.1758771 0.1738666 7.00e-06
1160 Separation-of-Sister-Chromatids 167 5.0e-07 0.1642928 -0.2406914 0.0002455 0.0000001 0.2914180 0.2863671 1.74e-05
1400 Transport-of-inorganic-cations/anions-and-amino-acids/oligopeptides 103 7.0e-07 0.2986352 -0.1643918 0.0000002 0.0039173 0.3408925 0.3274096 2.15e-05
116 Bicarbonate-transporters 10 1.1e-06 0.6222025 0.1039584 0.0006552 0.5691401 0.6308275 0.3664539 3.36e-05
1403 Transport-of-small-molecules 677 1.2e-06 0.1171570 -0.0857387 0.0000002 0.0001346 0.1451788 0.1434690 3.51e-05
878 Phosphorylation-of-CD3-and-TCR-zeta-chains 17 1.4e-06 -0.5234523 0.7270485 0.0001859 0.0000002 0.8958805 0.8842376 4.02e-05
unlink("pmx_report.html")
mitch_report(res,outfile = "pmx_report.html")
## Dataset saved as " /tmp/Rtmp3MaRhj/pmx_report.rds ".
## 
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## /usr/bin/pandoc +RTS -K512m -RTS /home/mdz/projects/vpa-senescence/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp3MaRhj/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header /tmp/Rtmp3MaRhj/rmarkdown-str2c07635fcd4ec8.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'
## 
## Output created: /tmp/Rtmp3MaRhj/mitch_report.html
## [1] TRUE
mitch_plots(res,"pmx_report.pdf")
## png 
##   2

Look at senescence and cell cycle pathways

# senescence
sen <- res$enrichment_result[grep("senescen",res$enrichment_result$set,ignore.case = TRUE),]
sen %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pMANOVA s.diab s.PMX p.diab p.PMX s.dist SD p.adjustMANOVA
1155 Senescence-Associated-Secretory-Phenotype-(SASP) 48 0.0229467 0.2252022 -0.1850079 0.0069282 0.0265307 0.2914514 0.2900623 0.0903303
815 Oncogene-Induced-Senescence 31 0.7355955 -0.0148105 -0.0481470 0.8864896 0.6425978 0.0503734 0.0235725 0.8372907
427 Formation-of-Senescence-Associated-Heterochromatin-Foci-(SAHF) 11 0.7468897 -0.0756495 0.1315434 0.6639360 0.4499285 0.1517448 0.1465074 0.8449546
255 DNA-Damage/Telomere-Stress-Induced-Senescence 28 0.7740845 0.0099873 0.0496498 0.9271040 0.6492285 0.0506444 0.0280456 0.8638503
824 Oxidative-Stress-Induced-Senescence 62 0.7860083 -0.0488632 0.0229487 0.5056224 0.7545526 0.0539839 0.0507787 0.8745228
177 Cellular-Senescence 124 0.9537872 -0.0037689 -0.0087449 0.9421785 0.8663480 0.0095225 0.0035186 0.9680609
dim(sen)
## [1]  6 10
# cell cycle
cc <- res$enrichment_result[grep("cell-",res$enrichment_result$set,ignore.case = TRUE),]
cc %>% kbl() %>%  kable_paper("hover", full_width = F)
set setSize pMANOVA s.diab s.PMX p.diab p.PMX s.dist SD p.adjustMANOVA
338 Downstream-signaling-events-of-B-Cell-Receptor-(BCR) 79 0.0000033 0.0476953 -0.2682978 0.4634198 0.0000370 0.2725042 0.2234408 0.0000874
168 Cell-Cycle-Checkpoints 249 0.0000104 0.1533172 -0.1680226 0.0000301 0.0000048 0.2274593 0.2272215 0.0001967
18 APC:Cdc20-mediated-degradation-of-cell-cycle-proteins-prior-to-satisfation-of-the-cell-cycle-checkpoint 71 0.0000341 0.1502713 -0.3014511 0.0284917 0.0000111 0.3368297 0.3194160 0.0004871
13 APC/C-mediated-degradation-of-cell-cycle-proteins 85 0.0000682 0.1517125 -0.2707608 0.0155588 0.0000157 0.3103676 0.2987337 0.0008286
1077 Regulation-of-mitotic-cell-cycle 85 0.0000682 0.1517125 -0.2707608 0.0155588 0.0000157 0.3103676 0.2987337 0.0008286
169 Cell-Cycle,-Mitotic 479 0.0000944 0.1141548 -0.0849284 0.0000178 0.0014113 0.1422819 0.1407731 0.0010762
167 Cell-Cycle 603 0.0001233 0.1007879 -0.0709600 0.0000222 0.0028271 0.1232621 0.1214442 0.0013441
581 Integrin-cell-surface-interactions 64 0.0007604 -0.0696789 0.2407191 0.3348441 0.0008619 0.2506010 0.2194846 0.0060520
1254 Signaling-by-the-B-Cell-Receptor-(BCR) 120 0.0026570 -0.0658351 -0.0785462 0.2125894 0.1369622 0.1024879 0.0089882 0.0166852
80 Antigen-activates-B-Cell-Receptor-(BCR)-leading-to-generation-of-second-messengers 42 0.0040520 -0.2791272 0.2626308 0.0017415 0.0032178 0.3832584 0.3830808 0.0233663
173 Cell-surface-interactions-at-the-vascular-wall 126 0.0305363 -0.0183701 0.1109317 0.7215760 0.0313915 0.1124425 0.0914302 0.1120168
1320 TP53-Regulates-Transcription-of-Genes-Involved-in-G2-Cell-Cycle-Arrest 18 0.0347393 0.3151515 -0.1000039 0.0206002 0.4625448 0.3306377 0.2935592 0.1234621
1315 TP53-Regulates-Transcription-of-Cell-Cycle-Genes 46 0.0469361 0.1989688 -0.0856057 0.0195152 0.3149678 0.2166032 0.2012246 0.1467497
1078 Regulation-of-necroptotic-cell-death 26 0.0572414 0.1325825 -0.2630009 0.2418396 0.0202406 0.2945294 0.2797196 0.1682956
86 Apoptotic-cleavage-of-cell-adhesion–proteins 10 0.0609162 0.3434820 -0.0441210 0.0599667 0.8090716 0.3463041 0.2740767 0.1739458
359 ERBB2-Regulates-Cell-Motility 14 0.0613171 0.0805283 -0.3144161 0.6018439 0.0416231 0.3245648 0.2792679 0.1747538
27 Aberrant-regulation-of-mitotic-cell-cycle-due-to-RB1-defects 36 0.0721409 0.2170007 -0.1188437 0.0242100 0.2170843 0.2474129 0.2374779 0.1938210
323 Diseases-of-mitotic-cell-cycle 38 0.0752894 0.2101840 -0.1180133 0.0249149 0.2079449 0.2410487 0.2320706 0.1979290
759 Nef-mediates-down-modulation-of-cell-surface-receptors-by-recruiting-them-to-clathrin-adapters 20 0.0869923 0.0299524 0.1864088 0.8165953 0.1488890 0.1887999 0.1106314 0.2151091
95 Assembly-and-cell-surface-presentation-of-NMDA-receptors 25 0.0982708 0.1220062 0.0745967 0.2909053 0.5184362 0.1430041 0.0335236 0.2359079
1065 Regulation-of-beta-cell-development 35 0.1036214 -0.2014802 0.1753504 0.0390807 0.0725285 0.2670993 0.2664595 0.2442248
920 Programmed-Cell-Death 185 0.1236421 0.0682549 -0.0861350 0.1089975 0.0431084 0.1098999 0.1091702 0.2738976
1153 Sema4D-induced-cell-migration-and-growth-cone-collapse 19 0.1522459 0.2110366 -0.0374247 0.1112102 0.7775891 0.2143293 0.1756887 0.3146840
174 Cell-Cell-communication 110 0.2015524 0.0840235 -0.0197332 0.1276586 0.7205071 0.0863097 0.0733671 0.3760791
176 Cell-extracellular-matrix-interactions 16 0.2043060 -0.1323725 -0.0702542 0.3592250 0.6265343 0.1498604 0.0439243 0.3770629
171 Cell-junction-organization 78 0.2166522 0.1143010 -0.0830477 0.0807968 0.2045566 0.1412857 0.1395466 0.3891862
665 MET-promotes-cell-motility 29 0.2297951 -0.0701035 0.1719703 0.5134062 0.1088721 0.1857103 0.1711721 0.4059075
408 FOXO-mediated-transcription-of-cell-cycle-genes 15 0.2624920 0.2357017 -0.2069784 0.1139383 0.1650938 0.3136803 0.3130221 0.4405585
175 Cell-cell-junction-organization 53 0.2737503 0.0970036 -0.0057634 0.2216984 0.9421192 0.0971747 0.0726672 0.4527878
1424 VEGFR2-mediated-cell-proliferation 19 0.2806390 0.2059751 -0.1068687 0.1200475 0.4199006 0.2320488 0.2212140 0.4589589
1319 TP53-Regulates-Transcription-of-Genes-Involved-in-G1-Cell-Cycle-Arrest 13 0.3668777 0.2191673 -0.1074301 0.1711884 0.5023700 0.2440810 0.2309392 0.5426274
755 NRIF-signals-cell-death-from-the-nucleus 15 0.4664591 0.1221816 -0.1840689 0.4125587 0.2170201 0.2209292 0.2165518 0.6371358
170 Cell-death-signalling-via-NRAGE,-NRIF-and-NADE 73 0.5410144 0.0489997 0.0078546 0.4689508 0.9075815 0.0496253 0.0290940 0.6905972
409 FOXO-mediated-transcription-of-cell-death-genes 15 0.7171385 -0.0906935 0.1211211 0.5430380 0.4166117 0.1513130 0.1497755 0.8238754
324 Diseases-of-programmed-cell-death 42 0.7998377 -0.0435775 0.0001997 0.6249917 0.9982126 0.0435779 0.0309551 0.8865815
1316 TP53-Regulates-Transcription-of-Cell-Death-Genes 37 0.8402658 0.0054984 0.0368905 0.9538359 0.6977050 0.0372980 0.0221976 0.9076340
1322 TP53-regulates-transcription-of-several-additional-cell-death-genes-whose-specific-roles-in-p53-dependent-apoptosis-remain-uncertain 13 0.8641843 0.0767647 -0.0816698 0.6317382 0.6101046 0.1120838 0.1120301 0.9270790
172 Cell-recruitment-(pro-inflammatory-response) 24 0.8996479 -0.0445363 0.0530312 0.7056241 0.6528508 0.0692517 0.0689907 0.9469305
1321 TP53-regulates-transcription-of-additional-cell-cycle-genes-whose-exact-role-in-the-p53-pathway-remain-uncertain 19 0.9331779 0.0487887 -0.0384452 0.7127091 0.7716903 0.0621158 0.0616837 0.9575218
349 EGR2-and-SOX10-mediated-initiation-of-Schwann-cell-myelination 27 0.9344126 -0.0397815 0.0201572 0.7204485 0.8561069 0.0445968 0.0423830 0.9576760
dim(cc)
## [1] 40 10
custom_sets <- genesets[union(grep("cell-",names(genesets),ignore.case = TRUE), grep("senescen",names(genesets),ignore.case = TRUE))]

length(custom_sets)
## [1] 76
res2 <- mitch_calc(m, custom_sets , priority = "significance",resrows = 100)
## Note: When prioritising by significance (ie: small 
##             p-values), large effect sizes might be missed.
res2$enrichment_result %>% kbl() %>% kable_paper("hover", full_width = F)
set setSize pMANOVA s.diab s.PMX p.diab p.PMX s.dist SD p.adjustMANOVA
19 Downstream-signaling-events-of-B-Cell-Receptor-(BCR) 79 0.0000033 0.0476953 -0.2682978 0.4634198 0.0000370 0.2725042 0.2234408 0.0001518
8 Cell-Cycle-Checkpoints 249 0.0000104 0.1533172 -0.1680226 0.0000301 0.0000048 0.2274593 0.2272215 0.0002381
2 APC:Cdc20-mediated-degradation-of-cell-cycle-proteins-prior-to-satisfation-of-the-cell-cycle-checkpoint 71 0.0000341 0.1502713 -0.3014511 0.0284917 0.0000111 0.3368297 0.3194160 0.0005230
1 APC/C-mediated-degradation-of-cell-cycle-proteins 85 0.0000682 0.1517125 -0.2707608 0.0155588 0.0000157 0.3103676 0.2987337 0.0006275
30 Regulation-of-mitotic-cell-cycle 85 0.0000682 0.1517125 -0.2707608 0.0155588 0.0000157 0.3103676 0.2987337 0.0006275
9 Cell-Cycle,-Mitotic 479 0.0000944 0.1141548 -0.0849284 0.0000178 0.0014113 0.1422819 0.1407731 0.0007238
7 Cell-Cycle 603 0.0001233 0.1007879 -0.0709600 0.0000222 0.0028271 0.1232621 0.1214442 0.0008105
24 Integrin-cell-surface-interactions 64 0.0007604 -0.0696789 0.2407191 0.3348441 0.0008619 0.2506010 0.2194846 0.0043723
33 Signaling-by-the-B-Cell-Receptor-(BCR) 120 0.0026570 -0.0658351 -0.0785462 0.2125894 0.1369622 0.1024879 0.0089882 0.0135803
4 Antigen-activates-B-Cell-Receptor-(BCR)-leading-to-generation-of-second-messengers 42 0.0040520 -0.2791272 0.2626308 0.0017415 0.0032178 0.3832584 0.3830808 0.0186394
46 Senescence-Associated-Secretory-Phenotype-(SASP) 48 0.0229467 0.2252022 -0.1850079 0.0069282 0.0265307 0.2914514 0.2900623 0.0959590
13 Cell-surface-interactions-at-the-vascular-wall 126 0.0305363 -0.0183701 0.1109317 0.7215760 0.0313915 0.1124425 0.0914302 0.1170558
37 TP53-Regulates-Transcription-of-Genes-Involved-in-G2-Cell-Cycle-Arrest 18 0.0347393 0.3151515 -0.1000039 0.0206002 0.4625448 0.3306377 0.2935592 0.1229238
34 TP53-Regulates-Transcription-of-Cell-Cycle-Genes 46 0.0469361 0.1989688 -0.0856057 0.0195152 0.3149678 0.2166032 0.2012246 0.1542187
31 Regulation-of-necroptotic-cell-death 26 0.0572414 0.1325825 -0.2630009 0.2418396 0.0202406 0.2945294 0.2797196 0.1659169
5 Apoptotic-cleavage-of-cell-adhesion–proteins 10 0.0609162 0.3434820 -0.0441210 0.0599667 0.8090716 0.3463041 0.2740767 0.1659169
21 ERBB2-Regulates-Cell-Motility 14 0.0613171 0.0805283 -0.3144161 0.6018439 0.0416231 0.3245648 0.2792679 0.1659169
3 Aberrant-regulation-of-mitotic-cell-cycle-due-to-RB1-defects 36 0.0721409 0.2170007 -0.1188437 0.0242100 0.2170843 0.2474129 0.2374779 0.1822795
17 Diseases-of-mitotic-cell-cycle 38 0.0752894 0.2101840 -0.1180133 0.0249149 0.2079449 0.2410487 0.2320706 0.1822795
27 Nef-mediates-down-modulation-of-cell-surface-receptors-by-recruiting-them-to-clathrin-adapters 20 0.0869923 0.0299524 0.1864088 0.8165953 0.1488890 0.1887999 0.1106314 0.2000822
6 Assembly-and-cell-surface-presentation-of-NMDA-receptors 25 0.0982708 0.1220062 0.0745967 0.2909053 0.5184362 0.1430041 0.0335236 0.2152598
29 Regulation-of-beta-cell-development 35 0.1036214 -0.2014802 0.1753504 0.0390807 0.0725285 0.2670993 0.2664595 0.2166629
28 Programmed-Cell-Death 185 0.1236421 0.0682549 -0.0861350 0.1089975 0.0431084 0.1098999 0.1091702 0.2472841
32 Sema4D-induced-cell-migration-and-growth-cone-collapse 19 0.1522459 0.2110366 -0.0374247 0.1112102 0.7775891 0.2143293 0.1756887 0.2918047
14 Cell-Cell-communication 110 0.2015524 0.0840235 -0.0197332 0.1276586 0.7205071 0.0863097 0.0733671 0.3614645
16 Cell-extracellular-matrix-interactions 16 0.2043060 -0.1323725 -0.0702542 0.3592250 0.6265343 0.1498604 0.0439243 0.3614645
11 Cell-junction-organization 78 0.2166522 0.1143010 -0.0830477 0.0807968 0.2045566 0.1412857 0.1395466 0.3691112
25 MET-promotes-cell-motility 29 0.2297951 -0.0701035 0.1719703 0.5134062 0.1088721 0.1857103 0.1711721 0.3775206
22 FOXO-mediated-transcription-of-cell-cycle-genes 15 0.2624920 0.2357017 -0.2069784 0.1139383 0.1650938 0.3136803 0.3130221 0.4163666
15 Cell-cell-junction-organization 53 0.2737503 0.0970036 -0.0057634 0.2216984 0.9421192 0.0971747 0.0726672 0.4164320
40 VEGFR2-mediated-cell-proliferation 19 0.2806390 0.2059751 -0.1068687 0.1200475 0.4199006 0.2320488 0.2212140 0.4164320
36 TP53-Regulates-Transcription-of-Genes-Involved-in-G1-Cell-Cycle-Arrest 13 0.3668777 0.2191673 -0.1074301 0.1711884 0.5023700 0.2440810 0.2309392 0.5273866
26 NRIF-signals-cell-death-from-the-nucleus 15 0.4664591 0.1221816 -0.1840689 0.4125587 0.2170201 0.2209292 0.2165518 0.6502156
10 Cell-death-signalling-via-NRAGE,-NRIF-and-NADE 73 0.5410144 0.0489997 0.0078546 0.4689508 0.9075815 0.0496253 0.0290940 0.7319607
23 FOXO-mediated-transcription-of-cell-death-genes 15 0.7171385 -0.0906935 0.1211211 0.5430380 0.4166117 0.1513130 0.1497755 0.9198134
44 Oncogene-Induced-Senescence 31 0.7355955 -0.0148105 -0.0481470 0.8864896 0.6425978 0.0503734 0.0235725 0.9198134
43 Formation-of-Senescence-Associated-Heterochromatin-Foci-(SAHF) 11 0.7468897 -0.0756495 0.1315434 0.6639360 0.4499285 0.1517448 0.1465074 0.9198134
42 DNA-Damage/Telomere-Stress-Induced-Senescence 28 0.7740845 0.0099873 0.0496498 0.9271040 0.6492285 0.0506444 0.0280456 0.9198134
45 Oxidative-Stress-Induced-Senescence 62 0.7860083 -0.0488632 0.0229487 0.5056224 0.7545526 0.0539839 0.0507787 0.9198134
18 Diseases-of-programmed-cell-death 42 0.7998377 -0.0435775 0.0001997 0.6249917 0.9982126 0.0435779 0.0309551 0.9198134
35 TP53-Regulates-Transcription-of-Cell-Death-Genes 37 0.8402658 0.0054984 0.0368905 0.9538359 0.6977050 0.0372980 0.0221976 0.9427372
39 TP53-regulates-transcription-of-several-additional-cell-death-genes-whose-specific-roles-in-p53-dependent-apoptosis-remain-uncertain 13 0.8641843 0.0767647 -0.0816698 0.6317382 0.6101046 0.1120838 0.1120301 0.9464875
12 Cell-recruitment-(pro-inflammatory-response) 24 0.8996479 -0.0445363 0.0530312 0.7056241 0.6528508 0.0692517 0.0689907 0.9537872
38 TP53-regulates-transcription-of-additional-cell-cycle-genes-whose-exact-role-in-the-p53-pathway-remain-uncertain 19 0.9331779 0.0487887 -0.0384452 0.7127091 0.7716903 0.0621158 0.0616837 0.9537872
20 EGR2-and-SOX10-mediated-initiation-of-Schwann-cell-myelination 27 0.9344126 -0.0397815 0.0201572 0.7204485 0.8561069 0.0445968 0.0423830 0.9537872
41 Cellular-Senescence 124 0.9537872 -0.0037689 -0.0087449 0.9421785 0.8663480 0.0095225 0.0035186 0.9537872
unlink("pmx_cell_senescence_report.html")
mitch_report(res2,outfile = "pmx_cell_senescence_report.html")
## Dataset saved as " /tmp/Rtmp3MaRhj/pmx_cell_senescence_report.rds ".
## 
## 
## processing file: mitch.Rmd
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## output file: /home/mdz/projects/vpa-senescence/mitch.knit.md
## /usr/bin/pandoc +RTS -K512m -RTS /home/mdz/projects/vpa-senescence/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/Rtmp3MaRhj/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header /tmp/Rtmp3MaRhj/rmarkdown-str2c07631750072c.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'
## 
## Output created: /tmp/Rtmp3MaRhj/mitch_report.html
## [1] TRUE
mitch_plots(res2,"pmx_cell_senescence_report.pdf")
## png 
##   2

Session information

For reproducibility

sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
## 
## 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] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] pkgload_1.2.1               GGally_2.1.2               
##  [3] ggplot2_3.3.5               reshape2_1.4.4             
##  [5] beeswarm_0.4.0              gplots_3.1.1               
##  [7] gtools_3.9.2                tibble_3.1.4               
##  [9] echarts4r_0.4.1             kableExtra_1.3.4           
## [11] dplyr_1.0.7                 mitch_1.5.1                
## [13] DESeq2_1.32.0               SummarizedExperiment_1.22.0
## [15] Biobase_2.52.0              MatrixGenerics_1.4.3       
## [17] matrixStats_0.60.1          GenomicRanges_1.44.0       
## [19] GenomeInfoDb_1.28.4         IRanges_2.26.0             
## [21] S4Vectors_0.30.0            BiocGenerics_0.38.0        
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_2.0-2       ellipsis_0.3.2         rprojroot_2.0.2       
##  [4] XVector_0.32.0         rstudioapi_0.13        farver_2.1.0          
##  [7] bit64_4.0.5            AnnotationDbi_1.54.1   fansi_0.5.0           
## [10] xml2_1.3.2             splines_4.1.1          cachem_1.0.6          
## [13] geneplotter_1.70.0     knitr_1.34             jsonlite_1.7.2        
## [16] annotate_1.70.0        png_0.1-7              shiny_1.6.0           
## [19] compiler_4.1.1         httr_1.4.2             assertthat_0.2.1      
## [22] Matrix_1.3-4           fastmap_1.1.0          later_1.3.0           
## [25] htmltools_0.5.2        tools_4.1.1            gtable_0.3.0          
## [28] glue_1.4.2             GenomeInfoDbData_1.2.6 Rcpp_1.0.7            
## [31] jquerylib_0.1.4        vctrs_0.3.8            Biostrings_2.60.2     
## [34] svglite_2.0.0          xfun_0.25              stringr_1.4.0         
## [37] testthat_3.0.4         rvest_1.0.1            mime_0.11             
## [40] lifecycle_1.0.0        XML_3.99-0.7           zlibbioc_1.38.0       
## [43] MASS_7.3-54            scales_1.1.1           promises_1.2.0.1      
## [46] RColorBrewer_1.1-2     yaml_2.2.1             memoise_2.0.0         
## [49] gridExtra_2.3          sass_0.4.0             reshape_0.8.8         
## [52] stringi_1.7.4          RSQLite_2.2.8          highr_0.9             
## [55] genefilter_1.74.0      desc_1.3.0             caTools_1.18.2        
## [58] BiocParallel_1.26.2    rlang_0.4.11           pkgconfig_2.0.3       
## [61] systemfonts_1.0.2      bitops_1.0-7           evaluate_0.14         
## [64] lattice_0.20-45        purrr_0.3.4            labeling_0.4.2        
## [67] htmlwidgets_1.5.4      bit_4.0.4              tidyselect_1.1.1      
## [70] plyr_1.8.6             magrittr_2.0.1         R6_2.5.1              
## [73] generics_0.1.0         DelayedArray_0.18.0    DBI_1.1.1             
## [76] pillar_1.6.2           withr_2.4.2            survival_3.2-13       
## [79] KEGGREST_1.32.0        RCurl_1.98-1.4         crayon_1.4.1          
## [82] KernSmooth_2.23-20     utf8_1.2.2             rmarkdown_2.10        
## [85] locfit_1.5-9.4         grid_4.1.1             blob_1.2.2            
## [88] digest_0.6.27          webshot_0.5.2          xtable_1.8-4          
## [91] httpuv_1.6.2           munsell_0.5.0          viridisLite_0.4.0     
## [94] bslib_0.3.0