Methods

Rats were kept in sedentary conditions or were trained. RNA was isolated from whole cell and mito fractions. Libraries were generated by the Deakin Genomics Facility and sequenced on the NovaSeq system.

Fastqc and MultiQC were run to summarise the QC checks that were done.

Reads were then were mapped to the rat genome (Ensembl version 99) with Kallisto then imported to R for analysis with DESeq2. Pathway level analysis was then done using mitch with Reactome gene sets.

Read counts

Import the Kallisto transcript counts. We can also include some info out of the Ensembl GTF file including gene name and gene class.

# libraries
library("reshape2")
library("DESeq2")
library("mitch")
library("gplots")
library("kableExtra")
library("beeswarm")

# import the 3 column table
tmp<-read.table("3col.tsv.gz",header=F)

# convert the 3 col table into a standard count matrix 
x<-as.matrix(acast(tmp, V2~V1, value.var="V3"))
# tidy up the column headers
colnames(x)<-sapply(strsplit(colnames(x),"_"),"[[",1)
head(x)
##                      1  10  11  12  13  14  15  16  17  18  19   2  20  21  22
## ENSRNOG00000000001   7  12   3   7   9   5   4   7  30   5   5   4   5   7   6
## ENSRNOG00000000007   8   5  10   6  12   2   6   3   9   8  19   7   5  21   5
## ENSRNOG00000000008   6  46  46  16  25  44  39  23  73  35  54  21  30  64  11
## ENSRNOG00000000009   1   1   0   0   0   1   0   0   0   0   0   0   0   2   0
## ENSRNOG00000000010   0   0   1   1   0   1   0   1   1   0   1   4   2   1   6
## ENSRNOG00000000012 280 679 336 529 703 446 307 349 376 481 501 325 319 906 598
##                     23  24  25  26  27  28  29   3  30  31  32 33 34 35 36 37
## ENSRNOG00000000001   8  11  10   4  13   7   8   8   5   5  11  7  8 22  9 10
## ENSRNOG00000000007   8   2  12   5  11  10   5   8   9   9   6 35 21 44 37 39
## ENSRNOG00000000008  38  14  35  44  62  29  31  32  34  28  22 11 10 17  5 17
## ENSRNOG00000000009   0   0   0   0   0   0   0   0   0   0   0 18  7 20 11 23
## ENSRNOG00000000010   3   3   0   0   0   2   2   1   0   4   0 19 11 21 16 30
## ENSRNOG00000000012 835 635 614 647 865 756 472 491 288 468 289 19 11 10  6  5
##                    38 39   4 40 41 42 43 44 45 46 47 48 49   5 50 51 52 53 54
## ENSRNOG00000000001 18 13   9 12  9  7 15 12 16  7  7 16 17   8 11 17 14 12  5
## ENSRNOG00000000007 48 32   6 17 27 17 44 34 43 39 49 31 41   8 51 53 49 35 44
## ENSRNOG00000000008 24 13  18 16  6  8 38 13 15  9 30  6 19  25 16 24 18 14 13
## ENSRNOG00000000009 29 26   0 22  5  8 20 10 22 13 21  7 22   0 27 36 33 22 33
## ENSRNOG00000000010 18 12   0 19 14 12 23 12 18 22 19 12 15   0 24 35 16 24 20
## ENSRNOG00000000012 18  2 249  7  4  6 19 14 13 10  6 11  4 833 15  8 11 10 15
##                    55 56 57 58 59   6 60 61 62 63 64   7   8   9 95 96
## ENSRNOG00000000001  7  7 11 17  8  10 17 15 17 17 15   6  10   4  0  0
## ENSRNOG00000000007 30 40 61 41 35  19 39 44 56 54 41   4   3   6  0  0
## ENSRNOG00000000008 25  5 20 11 11  27 22 25 15 18 20  15  14  26  0  0
## ENSRNOG00000000009 24 17 28 15 15   0 10 23 21 26 18   0   0   0  0  0
## ENSRNOG00000000010 23 22 14 26 25   0 24 27 24 28 32   0   2   0  0  0
## ENSRNOG00000000012  6  7 12  8  5 645 10 10  9 10  4 352 269 439  3 33
#dont forget gene names
g<-read.table("../ref2/Rattus_norvegicus.mRatBN7.2.106.gnames.txt",row.names=1)
g$gene_ID <- paste(g$V2,g$V3,g$V4,g$V5)
head(g)
##                          V2             V3                   gene_ID
## ENSRNOG00000066169       na protein_coding       na protein_coding  
## ENSRNOG00000070168    Olr56 protein_coding    Olr56 protein_coding  
## ENSRNOG00000070901     Irgq protein_coding     Irgq protein_coding  
## ENSRNOG00000018029    Doc2g protein_coding    Doc2g protein_coding  
## ENSRNOG00000031391 Ceacam16 protein_coding Ceacam16 protein_coding  
## ENSRNOG00000055342       U7          snRNA                U7 snRNA
g <- g[,ncol(g),drop=FALSE]
x<-merge(g,x,by=0)
rownames(x) <- x[,1]
x[,1]=NULL
# aggregate Tx data to genes
xx <- aggregate(. ~ gene_ID,x,sum)
# now round to integers so that DESeq2 doesn't fail
rownames(xx) <- xx[,1]
xx[,1]=NULL
x <- round(xx)
head(x)
##                                  1  10  11  12  13  14  15  16  17  18  19   2
## 0610030E20Rik protein_coding   101 103 115 125 108  76  79  83  72 106  95  69
## 0610040J01Rik protein_coding    23  59  58  57  42  53  53  48  45  52  54  32
## 1110032F04Rik protein_coding    12  11  18  13  22   8   9  16  13  15  15   4
## 1110038F14Rik protein_coding   183 330 292 403 305 278 235 234 244 260 287 139
## 1110065P20Rik protein_coding    55  82  81 101  78  73  72  59  93  55  55  34
## 1500009L16Rik protein_coding    16   5  14  16   5  10   5   1   9   4   4   6
##                                 20  21  22  23  24  25  26  27  28  29   3  30
## 0610030E20Rik protein_coding    69  88  85  87 111 165 102 138 132 111  79  81
## 0610040J01Rik protein_coding    56  64  46  41  49  71  43  54  52  48  44  53
## 1110032F04Rik protein_coding    27  17  32   9  32  15  12  19  18  19   9  15
## 1110038F14Rik protein_coding   244 341 205 274 273 203 189 289 284 194 246 237
## 1110065P20Rik protein_coding    93  80  47  74  83  63  76  71  71  37  62  71
## 1500009L16Rik protein_coding    10  16  19   7  15  26   7  22  12   3   7  10
##                                 31  32 33 34 35 36 37 38 39   4 40 41 42 43 44
## 0610030E20Rik protein_coding    84  45 18 23 18 20  9 18 18  74 10 11  2 23  7
## 0610040J01Rik protein_coding    59  33 14 11 25 12 18 13  9  33 20 12 15 31 10
## 1110032F04Rik protein_coding    13   6  8 11 15  8  9  1  6  10  0  4  7 11  4
## 1110038F14Rik protein_coding   255 159  6  3 10 11  9 14  4 196 14  2  7 12  6
## 1110065P20Rik protein_coding    67  48  9  1 10  8 19 10 16  65  6  9  7 18  7
## 1500009L16Rik protein_coding     8   3  9  9  6  2  3  8  3   7  2  8  1 13  7
##                                45 46 47 48 49   5 50 51 52 53 54 55 56 57 58 59
## 0610030E20Rik protein_coding   14 14 16  5 15 127 18 36  3 12 20 15 16  7 17 12
## 0610040J01Rik protein_coding   16 17 11 11 26  52 33 36 20 20 25 14 16 15 17 15
## 1110032F04Rik protein_coding   13  6  1  7  4  22  7  3  4  4  4  5  5  4  3  6
## 1110038F14Rik protein_coding   15 13  5  8  8 384 11 18 12 12 15 13 24 11 15  8
## 1110065P20Rik protein_coding   21 15  6  5  5 106 16 20 15 10 12  7  8  3  6 11
## 1500009L16Rik protein_coding    4  7  3  6  8   6 16  7  3 10 10  3  9 12  2 12
##                                  6 60 61 62 63 64   7   8   9 95 96
## 0610030E20Rik protein_coding    76  8 27 19 22 17  98  76  77  0  0
## 0610040J01Rik protein_coding    51 15 19 24 16 22  65  39  62  0  0
## 1110032F04Rik protein_coding    14  5  7  1  7  0   9   5   5  0  0
## 1110038F14Rik protein_coding   265 15 12 12  8 11 255 128 249  0  0
## 1110065P20Rik protein_coding    67 12 14 16  5 12  57  60  54  0  0
## 1500009L16Rik protein_coding     1 12 14 11  8  4   2  10   5  0  1
write.table(x,file="countmatrix_skeletal.tsv",quote=FALSE,sep="\t")

y <- x/colSums(x)*1000000

write.table(y,file="rpmmatrix_skeletal.tsv",quote=FALSE,sep="\t")

Samplesheet

samplesheet <- read.table("samplesheet.tsv",header=TRUE)
samplesheet$UDI <- gsub("UDI_0","",samplesheet$UDI)
samplesheet$UDI <- gsub("UDI_","",samplesheet$UDI)
samplesheet$label <- paste(samplesheet$fraction,samplesheet$Group)
samplesheet <- samplesheet[order(samplesheet$UDI),]
samplesheet %>% kbl() %>% kable_paper("hover", full_width = F)
UDI fraction Group RatID label
1 1 wholetissue T 1 wholetissue T
10 10 wholetissue T 10 wholetissue T
11 11 wholetissue S 11 wholetissue S
12 12 wholetissue S 12 wholetissue S
13 13 wholetissue T 13 wholetissue T
14 14 wholetissue T 14 wholetissue T
15 15 wholetissue S 15 wholetissue S
16 16 wholetissue S 16 wholetissue S
17 17 wholetissue T 17 wholetissue T
18 18 wholetissue T 18 wholetissue T
19 19 wholetissue S 19 wholetissue S
2 2 wholetissue T 2 wholetissue T
20 20 wholetissue S 20 wholetissue S
21 21 wholetissue T 21 wholetissue T
22 22 wholetissue T 22 wholetissue T
23 23 wholetissue S 23 wholetissue S
24 24 wholetissue S 24 wholetissue S
25 25 wholetissue T 25 wholetissue T
26 26 wholetissue T 26 wholetissue T
27 27 wholetissue T 27 wholetissue T
28 28 wholetissue T 28 wholetissue T
29 29 wholetissue S 29 wholetissue S
3 3 wholetissue S 3 wholetissue S
30 30 wholetissue S 30 wholetissue S
31 31 wholetissue S 31 wholetissue S
32 32 wholetissue S 32 wholetissue S
33 33 mito T 1 mito T
34 34 mito T 2 mito T
35 35 mito S 3 mito S
36 36 mito S 4 mito S
37 37 mito T 5 mito T
38 38 mito T 6 mito T
39 39 mito S 7 mito S
4 4 wholetissue S 4 wholetissue S
40 40 mito S 8 mito S
41 41 mito T 9 mito T
42 42 mito T 10 mito T
43 43 mito S 11 mito S
44 44 mito S 12 mito S
45 45 mito T 13 mito T
46 46 mito T 14 mito T
47 47 mito S 15 mito S
48 48 mito S 16 mito S
49 49 mito T 17 mito T
5 5 wholetissue T 5 wholetissue T
50 50 mito T 18 mito T
51 51 mito S 19 mito S
52 52 mito S 20 mito S
53 53 mito T 21 mito T
54 54 mito T 22 mito T
55 55 mito S 23 mito S
56 56 mito S 24 mito S
57 57 mito T 25 mito T
58 58 mito T 26 mito T
59 59 mito T 27 mito T
6 6 wholetissue T 6 wholetissue T
60 60 mito T 28 mito T
61 61 mito S 29 mito S
62 62 mito S 30 mito S
63 63 mito S 31 mito S
64 64 mito S 32 mito S
7 7 wholetissue S 7 wholetissue S
8 8 wholetissue S 8 wholetissue S
9 9 wholetissue T 9 wholetissue T

Overall clustering with multidimensional scaling

Sample 95 is a HUMAN mito control sample. Sample 96 is a RAT mito control sample. These should be excluded from main results.

ss <- samplesheet
ss <- ss[order(ss$UDI),]
colours = c('pink', 'lightblue','lightgreen','gray')
mds <- cmdscale(dist(t(x)))
XMAX=max(mds[,1])*1.1
XMIN=min(mds[,1])*1.1
plot( mds*1.05 , cex=2, pch=19, xlab="Coordinate 1", ylab="Coordinate 2",
  col = colours[as.factor(ss$label)] ,  type = "p" ,
  xlim=c(XMIN,XMAX),main="MDS plot",bty="n")
text(mds, labels=colnames(x) )
legend('topright', col=colours, legend=levels(as.factor(ss$label)), pch = 16, cex = 1)

x <- x[,which(! colnames(x) %in% c("95","96"))]

colours = c('pink', 'lightblue','lightgreen','gray')
mds <- cmdscale(dist(t(x)))
XMAX=max(mds[,1])*1.1
XMIN=min(mds[,1])*1.1
plot( mds*1.05 , cex=2, pch=19, xlab="Coordinate 1", ylab="Coordinate 2",
  col = colours[as.factor(ss$label)] ,  type = "p" ,
  xlim=c(XMIN,XMAX),main="MDS plot",bty="n")
text(mds, labels=colnames(x) )
legend('topright', col=colours, legend=levels(as.factor(ss$label)), pch = 16, cex = 1)

ss$nreads<-colSums(x)
par(mar=c(5,10,5,3))
barplot(colSums(x),horiz=TRUE,las=2,main="number of reads per sample",cex.names=0.5)

par(mai=c(1.02,0.82,0.82,0.42))

Check purity of mito fraction samples

Here I’m quantifying the mitochondrial read fraction. That is the number of mt reads divided by the total number of reads. We can see that purity is highly variable.

par(mar=c(5,10,5,3))
mtfrac <-  colSums(x[grep("^Mt",rownames(x)),]) / colSums(x) 
barplot(mtfrac,horiz=TRUE,las=2,main="Proportion mitochondrial reads",cex.names=1)

par(mai=c(1.02,0.82,0.82,0.42))

mylevels <- levels(as.factor(ss$label))
mylevels
## [1] "mito S"        "mito T"        "wholetissue S" "wholetissue T"
y <- x[,which(ss$label==mylevels[1])]
mitoS <- colSums(y[grep("^Mt",rownames(y)),]) / colSums(y)
y <- x[,which(ss$label==mylevels[2])] 
mitoT <- colSums(y[grep("^Mt",rownames(y)),]) / colSums(y)
y <- x[,which(ss$label==mylevels[3])] 
wholeS <- colSums(y[grep("^Mt",rownames(y)),]) / colSums(y)
y <- x[,which(ss$label==mylevels[4])] 
wholeT <- colSums(y[grep("^Mt",rownames(y)),]) / colSums(y)

dat <- list(mitoS,mitoT,wholeS,wholeT)
boxplot(dat,names=mylevels,ylab="mito frac")

median(wholeS)
## [1] 0.06734638
median(wholeT)
## [1] 0.07177643
table(mitoS>0.1)
## 
## FALSE  TRUE 
##     7     9
table(mitoT>0.1)
## 
## FALSE  TRUE 
##     9     7
ss$mtfrac <- mtfrac

ss %>% kbl() %>% kable_paper("hover", full_width = F)
UDI fraction Group RatID label nreads mtfrac
1 1 wholetissue T 1 wholetissue T 18837345 0.0597046
10 10 wholetissue T 10 wholetissue T 28530246 0.0941355
11 11 wholetissue S 11 wholetissue S 24424574 0.0843715
12 12 wholetissue S 12 wholetissue S 25587228 0.0692547
13 13 wholetissue T 13 wholetissue T 25708781 0.0800684
14 14 wholetissue T 14 wholetissue T 23791138 0.0882714
15 15 wholetissue S 15 wholetissue S 21212858 0.0782500
16 16 wholetissue S 16 wholetissue S 23445499 0.0823777
17 17 wholetissue T 17 wholetissue T 25983120 0.0583335
18 18 wholetissue T 18 wholetissue T 28395626 0.0651295
19 19 wholetissue S 19 wholetissue S 25942366 0.0590721
2 2 wholetissue T 2 wholetissue T 16662516 0.1077952
20 20 wholetissue S 20 wholetissue S 25890868 0.0555620
21 21 wholetissue T 21 wholetissue T 33404626 0.0637576
22 22 wholetissue T 22 wholetissue T 23143848 0.0608892
23 23 wholetissue S 23 wholetissue S 27554690 0.0681218
24 24 wholetissue S 24 wholetissue S 23717416 0.0504590
25 25 wholetissue T 25 wholetissue T 28953218 0.0687545
26 26 wholetissue T 26 wholetissue T 23386657 0.0566054
27 27 wholetissue T 27 wholetissue T 26538846 0.0747984
28 28 wholetissue T 28 wholetissue T 26311668 0.0639461
29 29 wholetissue S 29 wholetissue S 21082801 0.0848503
3 3 wholetissue S 3 wholetissue S 20802912 0.0645131
30 30 wholetissue S 30 wholetissue S 21972978 0.0664613
31 31 wholetissue S 31 wholetissue S 23183348 0.0740419
32 32 wholetissue S 32 wholetissue S 16939215 0.0665710
33 33 mito T 1 mito T 5067761 0.2172851
34 34 mito T 2 mito T 9474927 0.1887520
35 35 mito S 3 mito S 1207913 0.1446511
36 36 mito S 4 mito S 981493 0.1272235
37 37 mito T 5 mito T 1119147 0.1227524
38 38 mito T 6 mito T 683495 0.0598000
39 39 mito S 7 mito S 1571807 0.1674888
4 4 wholetissue S 4 wholetissue S 21452178 0.0616842
40 40 mito S 8 mito S 552568 0.0856293
41 41 mito T 9 mito T 435682 0.0708912
42 42 mito T 10 mito T 399371 0.0568219
43 43 mito S 11 mito S 1392807 0.0963034
44 44 mito S 12 mito S 541491 0.0713327
45 45 mito T 13 mito T 752464 0.0660111
46 46 mito T 14 mito T 889699 0.0982860
47 47 mito S 15 mito S 7917856 0.1545264
48 48 mito S 16 mito S 1111437 0.1224361
49 49 mito T 17 mito T 788973 0.0711609
5 5 wholetissue T 5 wholetissue T 26920394 0.1025539
50 50 mito T 18 mito T 1047898 0.0904945
51 51 mito S 19 mito S 1158187 0.0669745
52 52 mito S 20 mito S 2775819 0.1124724
53 53 mito T 21 mito T 1377429 0.0919474
54 54 mito T 22 mito T 1010427 0.0800859
55 55 mito S 23 mito S 658275 0.0422468
56 56 mito S 24 mito S 1846426 0.0988022
57 57 mito T 25 mito T 1572446 0.1144224
58 58 mito T 26 mito T 3484358 0.1297674
59 59 mito T 27 mito T 7149256 0.1264782
6 6 wholetissue T 6 wholetissue T 22443642 0.0780052
60 60 mito T 28 mito T 5622105 0.1382450
61 61 mito S 29 mito S 1501174 0.1045002
62 62 mito S 30 mito S 1196402 0.0987327
63 63 mito S 31 mito S 1232246 0.1057613
64 64 mito S 32 mito S 3768466 0.1434525
7 7 wholetissue S 7 wholetissue S 21910567 0.0794905
8 8 wholetissue S 8 wholetissue S 17831538 0.0504743
9 9 wholetissue T 9 wholetissue T 21603726 0.0965156 
mito <- subset(ss,fraction=="mito")

plot(mito$nreads,mito$mtfrac,pch=19,col="lightblue",xlab="No. reads", ylab="mtDNA proportion",cex=3)
text(mito$nreads,mito$mtfrac,labels=mito$RatID)
abline(h=0.1,v=2.5E6,col="red")

plot(mito$nreads,mito$mtfrac,pch=19,col="lightblue",xlab="No. reads", ylab="mtDNA proportion",cex=3)
text(mito$nreads,mito$mtfrac,labels=mito$Group)
abline(h=0.1,v=2.5E6,col="red")

Screen for mitochondrial enrichment

Mito fraction versus whole cell irrespective of training.

First exclude any mito fractions with mt reads less than 10%.

Also exclude mito fractions with less than 3m reads.

Instead of normalising by reads per million, we are normalising by reads per million (mito transcripts).

Then exclude any gene with detection less than 10 reads per sample across all samples. This way we can be sure the genes are real.

This cuts the number of genes down from 20k to 6.9k.

mito <- subset(mito,nreads>2.5e6 & mtfrac > 0.1)
ssm <- ss[which(ss$RatID %in% mito$RatID),]
xm <- x[,which(colnames(x) %in% rownames(ssm))]
xmito <- rbind(xm[grep("Mt-",rownames(xm) ),],xm[grep("Mt_",rownames(xm) ),])
xmnorm <- xm/colSums(xmito)*1e6

xmnorm <- xmnorm[which(apply(xm,1,min)>10),]
dim(xmnorm)
## [1] 6842   16

Now to calculate the mito enrichment factor.

This is defined as the normalised score (mito) / normalised score (whole cell).

This is calculated for each rat and a median enrichment factor is determined.

As anticipated, the list of top candidates includes mitochondrial transcripts, but also others such as protein coding and snRNAs

r1 <- xmnorm$`33`/xmnorm$`1`
r15 <- xmnorm$`47`/xmnorm$`15`
r2 <- xmnorm$`34`/xmnorm$`2`
r26 <- xmnorm$`58`/xmnorm$`26`
r27 <- xmnorm$`59`/xmnorm$`27`
r28 <- xmnorm$`60`/xmnorm$`28`
r32 <- xmnorm$`64`/xmnorm$`32`

xd <- data.frame(r1,r15,r2,r26,r27,r28,r32,row.names=rownames(xmnorm) )
xd$median <- apply(xd,1,median)

xd2 <- merge(xd,xmnorm,by=0)
rownames(xd2) <- xd2[,1] ; xd2[,1] = NULL
xd2 <- xd2[order(-xd2$median),]

head(xd2,30) %>% kbl() %>% kable_paper("hover", full_width = F)
r1 r15 r2 r26 r27 r28 r32 median 1 15 2 20 26 27 28 32 33 34 47 52 58 59 60 64
AY172581.19 Mt_tRNA 3.556701 1.8680200 21.4728998 11.827068 14.5652174 11.669903 15.885058 11.827068 10.586514 57.158627 13.496645 33.989958 18.082418 7.269465 10.438186 13.627510 37.653064 289.812100 106.773456 199.922750 213.861982 105.881345 121.812616 216.473780
Dnah1 protein_coding 13.846154 3.2359925 10.3206726 19.454545 6.3076923 10.352941 6.777778 10.320673 1.740209 10.238052 2.744518 3.018784 1.905197 3.714230 2.070160 5.830394 24.095202 28.325277 33.130259 28.980321 37.064749 23.428220 21.432244 39.517114
AY172581.21 Mt_tRNA 4.577381 2.8967938 2.5284792 7.834043 4.6573705 4.931159 6.664360 4.657370 57.333090 70.377295 75.670839 42.087542 33.570925 30.565302 59.599581 38.686185 262.435394 191.332144 203.868511 108.942650 262.996055 142.353937 293.895038 257.818659
Abca4 pseudogene 4.476191 6.9809013 2.4120231 3.322581 4.9545455 4.478261 7.047619 4.478261 7.166636 2.940555 4.226297 9.525987 4.428505 2.679031 4.966632 2.811107 32.079229 10.193926 20.527728 18.012775 14.714065 13.273378 22.241873 19.811610
AY172581.22 Mt_tRNA 4.468966 1.0351649 5.3553478 6.298508 2.6887755 3.320988 4.816754 4.468966 21.744594 69.215913 35.485046 21.281624 20.369664 30.701057 26.520854 85.291388 97.175842 190.034762 71.649882 32.449208 128.298479 82.548250 88.075428 410.827630
Bub1b protein_coding 2.105263 1.1468211 4.3995133 4.916667 2.4444444 5.454546 4.538462 4.399513 2.313708 4.318810 1.472485 9.896783 2.352444 3.622540 1.905197 1.857115 4.870965 6.478215 4.952903 15.698346 11.566185 8.855098 10.391986 8.428445
Cacna1d protein_coding 5.277778 19.7759493 1.0692702 5.370370 4.1764706 2.787234 4.346154 4.346154 2.819485 2.619344 7.144828 3.299180 8.342990 4.622573 4.951665 2.634882 14.880614 7.639752 51.800005 19.945042 44.804944 19.306040 13.801449 11.451602
Recql4 protein_coding 3.812500 2.6875075 3.7076308 3.578947 2.6000000 1.620690 3.333333 3.333333 1.621466 2.192933 1.309672 6.251725 2.849292 4.634994 3.942783 1.264255 6.181838 4.855779 5.893523 19.648278 10.197465 12.050985 6.390027 4.214183
Myo7a protein_coding 3.300000 2.2797950 4.3520909 3.439024 2.2888889 2.725000 4.714286 3.300000 4.015867 10.920589 3.136592 8.855098 7.101190 6.428475 4.870965 6.046334 13.252361 13.650736 24.896703 24.955277 24.421166 14.714065 13.273378 28.504148
Iqsec3 protein_coding 2.470588 9.5484712 1.8327541 3.200000 2.2800000 3.333333 5.090909 3.200000 2.428535 1.583063 2.591286 2.141796 8.531710 4.900926 3.622540 1.905197 5.999910 4.749190 15.115836 6.826974 27.301471 11.174111 12.075134 9.699187
Fgfr2 protein_coding 3.200000 5.5928116 1.6177228 3.611111 2.5000000 1.681818 3.071429 3.071429 6.698277 3.149217 3.707995 4.214699 1.896382 2.026832 6.892074 1.527950 21.434485 5.998509 17.612978 8.837272 6.848047 5.067081 11.591215 4.692991
U1 snRNA 2.859649 4.6548797 4.0179610 2.312500 3.0666667 2.559633 3.741935 3.066667 8.142735 7.793543 5.182572 10.708979 38.222060 11.762222 21.936493 10.738385 23.285365 20.823373 36.278006 39.623221 88.388514 36.070814 56.149373 40.182345
Sphkap protein_coding 4.066667 4.1210191 0.7908867 3.000000 1.0465116 2.937500 4.000000 3.000000 6.698277 2.699329 8.342990 3.942783 2.528510 4.357689 2.506209 1.527950 27.239658 6.598360 11.123986 5.982153 7.585529 4.560372 7.361988 6.111802
U6 snRNA 2.979452 2.5276195 4.8138591 2.389610 2.9393939 3.094972 3.130682 2.979452 28.621407 25.559033 13.336382 19.285425 28.129820 35.630185 23.961340 60.063237 85.276109 64.199462 64.603511 83.855885 67.219310 104.731149 74.159677 188.038884
Kif15 protein_coding 2.687500 5.0243146 0.6417070 2.840000 2.2400000 4.052632 3.285714 2.840000 2.285680 2.191934 6.262275 4.283591 8.531710 4.900926 3.823792 2.424797 6.142765 4.018546 11.012966 10.575116 24.230056 10.978074 15.496422 7.967189
Trank1 protein_coding 3.285714 4.4332188 1.4903715 2.270270 1.1000000 2.840000 5.809524 2.840000 2.999955 2.191934 3.023167 4.685178 12.626931 9.801852 5.031306 3.637195 9.856995 4.505642 9.717323 10.976703 28.666545 10.782037 14.288908 21.130371
Arhgef16 protein_coding 2.538461 2.8243130 4.6080962 3.166667 1.6296296 2.157895 2.823529 2.823529 1.740209 4.095221 3.332630 3.220036 3.117596 3.857085 2.313708 3.670989 4.417454 15.357078 11.566185 11.672629 9.872386 6.285620 4.992739 10.365145
Dnhd1 protein_coding 2.740000 2.8672698 3.1283058 2.822222 3.0731707 2.244444 2.760870 2.822222 8.659988 4.714215 3.653223 14.899895 6.023800 13.992004 8.821666 9.257603 23.728367 11.428400 13.516927 29.151969 17.000504 42.999817 19.799740 25.559033
Carmil3 protein_coding 4.416667 0.6947276 3.2467962 1.260000 1.2678571 2.880000 2.777778 2.777778 1.799553 11.741985 2.855119 2.949928 5.067081 8.771731 2.728483 12.056898 7.948024 9.269988 8.157482 6.531983 6.384521 11.121301 7.858031 33.491383
AY172581.2 Mt_tRNA 3.763407 1.0234423 3.6221154 2.766208 1.8044693 2.134650 3.361809 2.766208 42.434328 215.681624 104.487739 141.077814 88.158679 102.284180 67.828181 85.944324 159.697643 378.466647 220.737699 105.858674 243.865264 184.568660 144.789420 288.928406
Map4k1 protein_coding 2.913044 1.4207193 2.8024406 2.750000 1.7419355 1.075472 4.111111 2.750000 3.983595 4.285650 2.191934 4.750691 3.212694 10.579320 10.389963 3.622540 11.604384 6.142765 6.088706 13.388312 8.834907 18.428493 11.174111 14.892665
Rfx8 protein_coding 3.181818 1.3599820 4.3389004 3.750000 2.0454545 1.933333 2.733333 2.733333 1.649590 3.398996 1.495538 1.580319 1.216099 3.446037 1.637090 6.698277 5.248695 6.488992 4.622573 4.003474 4.560372 7.048712 3.165040 18.308623
AY172581.6 Mt_tRNA 7.862500 3.7185057 1.9824117 4.378049 2.6694215 1.676471 2.012821 2.669421 8.428366 20.977714 24.435535 8.840284 36.617245 18.145489 42.023947 21.209453 66.268026 48.441290 78.005747 15.170365 160.312086 48.437958 70.451912 42.690821
Scn2a protein_coding 2.851852 3.8241002 1.1505975 1.575000 1.6388889 3.318182 2.666667 2.666667 5.830394 2.677245 5.119026 9.605815 8.050089 6.235191 3.142810 3.653223 16.627419 5.889938 10.238052 18.231444 12.678891 10.218786 10.428415 9.741929
Mroh7 protein_coding 1.960000 1.4743871 2.6570660 2.933333 1.9310345 3.833333 2.857143 2.657066 3.346556 10.238052 3.724704 5.836315 5.195993 4.142795 2.191934 6.046334 6.559249 9.896783 15.094852 15.093917 15.241579 7.999880 8.402414 17.275241
Insc protein_coding 2.700000 3.2991962 2.8037132 2.631579 2.1666667 2.166667 1.880000 2.631579 4.025045 2.424797 2.285680 3.287901 4.102870 3.212694 6.142831 4.900926 10.867620 6.408391 7.999880 7.062899 10.797026 6.960836 13.309467 9.213741
Cdc45 protein_coding 1.333333 1.9238199 2.6237482 3.363636 2.1923077 2.607143 4.000000 2.607143 4.486615 3.582056 2.128174 4.855779 2.401065 11.610346 4.198956 5.870993 5.982153 5.583792 6.891229 12.687682 8.076309 25.453451 10.947278 23.483971
Vav1 protein_coding 2.833333 2.9704696 1.7744108 2.600000 1.5483871 2.950000 2.166667 2.600000 5.561993 1.631496 1.685673 1.621466 3.132761 3.383319 8.931035 3.599105 15.758980 2.991077 4.846310 6.181838 8.145178 5.238687 26.346554 7.798061
Trpm1 protein_coding 1.243902 1.2984896 3.6563617 2.600000 5.6153846 1.634146 4.157895 2.600000 12.668984 5.030447 1.264255 1.418783 4.699141 1.418811 18.308623 2.849292 15.758980 4.622573 6.531983 7.296596 12.217768 7.967170 29.918969 11.847055
Cep41 protein_coding 1.421053 0.7149905 2.6738322 2.666667 0.9411765 2.571429 3.545454 2.571429 2.849292 4.943994 2.311287 2.528510 1.520124 2.662847 1.527950 4.912070 4.048993 6.179992 3.534909 5.478438 4.053664 2.506209 3.929015 17.415519 
# mito genes
rbind(xd2[grep("Mt-",rownames(xd2) ),],xd2[grep("Mt_",rownames(xd2) ),]) %>% kbl() %>% kable_paper("hover", full_width = F)
r1 r15 r2 r26 r27 r28 r32 median 1 15 2 20 26 27 28 32 33 34 47 52 58 59 60 64
Mt-nd6 protein_coding 1.1369234 0.8699888 2.1482486 0.4690199 1.1578503 1.2160677 0.8041597 1.1369234 3.767326e+03 1.047355e+04 6033.57238 1.914174e+04 8602.69312 9.537947e+03 7.123013e+03 4.670644e+03 4283.16086 12961.61333 9.111868e+03 6089.59319 4034.83421 11043.51496 8.662066e+03 3755.94366
Mt-nd5 protein_coding 0.9486163 0.5145339 1.3064538 0.5054752 0.6743493 0.5734612 0.6476679 0.6476679 1.022810e+04 2.017576e+04 17122.64891 3.916929e+04 13886.54780 3.318192e+04 1.616813e+04 8.312476e+03 9702.54717 22369.94930 1.038111e+04 13953.84977 7019.30500 22376.20703 9.271794e+03 5383.72400
Mt-nd1 protein_coding 1.0805666 0.6461820 1.1563177 0.4221072 0.4961123 0.5250087 0.5646383 0.5646383 1.329915e+04 1.694102e+04 16350.89759 8.857240e+03 15972.47190 1.447867e+04 3.245701e+04 1.273182e+04 14370.61685 18906.83238 1.094698e+04 2354.01537 6742.09469 7183.04737 1.704021e+04 7188.87404
Mt-cyb protein_coding 1.0443196 0.6271076 1.3784268 0.3634736 0.4752000 0.4637670 0.5496341 0.5496341 3.972684e+04 2.169428e+04 21251.80828 1.467315e+04 22050.72097 2.183889e+04 7.813093e+04 1.598603e+04 41487.52414 29294.06104 1.360465e+04 3418.15119 8014.85555 10377.83922 3.623455e+04 8786.46568
Mt-nd3 protein_coding 0.9923033 0.4362767 0.9406466 0.3998078 0.7539716 0.5216859 0.3677097 0.5216859 6.075848e+02 1.065127e+03 782.27690 1.329762e+03 557.13461 1.632628e+03 1.120940e+03 1.086963e+03 602.90837 735.84611 4.646900e+02 242.71714 222.74675 1230.95509 5.847785e+02 399.68693
Mt-nd2 protein_coding 0.8792986 0.6012676 0.8153250 0.4283192 0.4830796 0.4937795 0.4981790 0.4981790 1.440666e+04 1.422803e+04 15628.90324 1.609550e+04 9889.11336 5.939362e+04 1.835799e+04 2.765856e+04 12667.75352 12742.63553 8.554853e+03 3575.57668 4235.69683 28691.84445 9.064796e+03 13778.91078
Mt-co2 protein_coding 1.0807717 0.3276505 2.2334365 0.3933792 0.5325516 0.4950733 0.4626619 0.4950733 8.161721e+03 2.907377e+04 13107.84966 6.876914e+03 6468.53368 1.452019e+04 9.459104e+03 2.916965e+04 8820.95711 29275.55041 9.526035e+03 1767.63902 2544.58651 7732.75043 4.682950e+03 13495.68765
Mt-co1 protein_coding 1.0466041 0.8363324 1.0675704 0.3056117 0.4187980 0.4462375 0.4685079 0.4685079 1.332441e+05 1.205034e+05 136899.01304 9.734017e+04 73884.89173 1.047562e+05 1.418008e+05 5.664862e+04 139453.86819 146149.33278 1.007809e+05 20329.14883 22580.08987 43871.68126 6.327683e+04 26540.32928
Mt-nd4 protein_coding 0.7371841 1.1529481 0.6223015 0.3096842 0.4678186 0.4650555 0.4487498 0.4678186 1.621512e+03 2.348997e+03 3995.67992 1.759107e+03 6759.90072 3.914327e+03 6.738046e+03 1.968400e+03 1195.35298 2486.51777 2.708272e+03 474.75602 2093.43471 1831.19476 3.133565e+03 883.31932
Mt-co3 protein_coding 1.2753139 0.5898128 1.5235678 0.3448433 0.4484976 0.4056546 0.4356036 0.4484976 1.156795e+04 2.333037e+04 15185.11591 1.533734e+04 10675.08402 2.830786e+04 1.585143e+04 2.748849e+04 14752.76692 23135.55404 1.376055e+04 2652.53164 3681.23142 12696.00650 6.430206e+03 11974.08408
Mt-nd4l protein_coding 0.6968066 0.7852922 0.3379897 0.3589272 0.3699514 0.3959591 0.2704320 0.3699514 2.017541e+03 2.369237e+03 4471.91208 1.934443e+03 3524.61996 3.310281e+03 2.599776e+03 2.373530e+03 1405.83606 1511.46028 1.860543e+03 291.28422 1265.08193 1224.64334 1.029405e+03 641.87832
Mt-atp6 protein_coding 0.8072471 0.2738409 1.3552379 0.2599370 0.3355892 0.3394591 0.3566967 0.3394591 1.665353e+04 6.291863e+04 35256.28019 3.311344e+04 24950.81127 3.020698e+04 2.248985e+04 2.724435e+04 13443.51623 47780.64633 1.722969e+04 4918.60454 6485.63828 10137.13366 7.634384e+03 9717.97089
Mt-atp8 protein_coding 0.6187845 0.6642837 0.1909117 0.1326042 0.1055570 0.1189034 0.1532550 0.1532550 1.293214e+03 2.511876e+02 1128.77559 3.930547e+02 308.26748 3.993873e+02 4.913736e+02 2.414162e+02 800.22078 215.49643 1.668598e+02 28.00735 40.87757 42.15811 5.842599e+01 36.99823
AY172581.19 Mt_tRNA 3.5567010 1.8680200 21.4728998 11.8270677 14.5652174 11.6699029 15.8850575 11.8270677 1.058651e+01 5.715863e+01 13.49664 3.398996e+01 18.08242 7.269465e+00 1.043819e+01 1.362751e+01 37.65306 289.81210 1.067735e+02 199.92275 213.86198 105.88135 1.218126e+02 216.47378
AY172581.21 Mt_tRNA 4.5773810 2.8967938 2.5284792 7.8340426 4.6573705 4.9311594 6.6643599 4.6573705 5.733309e+01 7.037729e+01 75.67084 4.208754e+01 33.57093 3.056530e+01 5.959958e+01 3.868619e+01 262.43539 191.33214 2.038685e+02 108.94265 262.99606 142.35394 2.938950e+02 257.81866
AY172581.22 Mt_tRNA 4.4689655 1.0351649 5.3553478 6.2985075 2.6887755 3.3209877 4.8167539 4.4689655 2.174459e+01 6.921591e+01 35.48505 2.128162e+01 20.36966 3.070106e+01 2.652085e+01 8.529139e+01 97.17584 190.03476 7.164988e+01 32.44921 128.29848 82.54825 8.807543e+01 410.82763
AY172581.2 Mt_tRNA 3.7634069 1.0234423 3.6221154 2.7662083 1.8044693 2.1346499 3.3618090 2.7662083 4.243433e+01 2.156816e+02 104.48774 1.410778e+02 88.15868 1.022842e+02 6.782818e+01 8.594432e+01 159.69764 378.46665 2.207377e+02 105.85867 243.86526 184.56866 1.447894e+02 288.92841
AY172581.6 Mt_tRNA 7.8625000 3.7185057 1.9824117 4.3780488 2.6694215 1.6764706 2.0128205 2.6694215 8.428366e+00 2.097771e+01 24.43554 8.840284e+00 36.61725 1.814549e+01 4.202395e+01 2.120945e+01 66.26803 48.44129 7.800575e+01 15.17036 160.31209 48.43796 7.045191e+01 42.69082
AY172581.8 Mt_tRNA 2.7966102 1.4806176 2.0400512 9.3636364 1.7111111 2.3673469 7.5714286 2.3673469 1.195831e+01 5.012417e+00 15.27950 1.875517e+01 3.29918 1.390498e+01 6.661943e+00 2.212446e+00 33.44273 31.17097 7.421473e+00 31.70518 30.89232 23.79297 1.577113e+01 16.75138
AY172581.24 Mt_rRNA 1.0266671 0.8752098 2.1614359 0.4597052 0.7828493 0.7200970 0.5540846 0.7828493 5.698407e+05 3.972429e+05 213528.11708 4.252197e+05 447742.96323 4.514723e+05 1.486831e+06 3.950397e+05 585036.69370 461527.34435 3.476708e+05 110488.09667 205829.75481 353434.79433 1.070662e+06 218885.43508
AY172581.9 Mt_rRNA 0.8060800 0.6516189 3.2520250 0.4061220 0.6664038 0.5938419 0.4656528 0.6516189 2.468506e+05 6.048980e+05 264046.73291 1.328060e+06 559352.07408 7.619957e+05 5.608908e+05 3.778608e+05 198981.33519 858686.58502 3.941630e+05 285489.43860 227165.16532 507796.81333 3.330805e+05 175951.93215

Investigate top candidates

To dig deeper into this set of genes, I ran a heatmap. It shows that there is some enrichment as one might expect.

I also show the barplot.

candidates <- rownames(head(xd2,30))
candidates_df <- xmnorm[which(rownames(xmnorm) %in% candidates),]
heatmap.2(as.matrix(candidates_df),trace="none",scale="row",margin=c(5,15),Colv="none",dendrogram="row")

par(mfrow=c(1,2))
sapply(1:nrow(candidates_df),function(i) {
  wc <- as.numeric(candidates_df[i,1:7])
  mit <- as.numeric(candidates_df[i,8:14])
  wtest <- wilcox.test(wc,mit,paired=TRUE)
  HEAD = paste("paired Wilcox p =",signif(wtest$p.value,2))
  nam <- rownames(candidates_df)[i]
  dat <- list("whole cell"=wc,"mito"=mit)
  boxplot(dat,ylab="normalised expression",cex=0,col="white",main=nam)
  beeswarm(dat,add=TRUE,cex=1.5,pch=19)
  mtext(HEAD)
  vec <- as.numeric(candidates_df[i,])
  barplot(as.numeric(vec),names.arg=colnames(candidates_df),main=nam ,ylab="normalised expression")
  mtext(HEAD)
} )

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par(mfrow=c(1,1))

Differential analysis of sed vs trained mito fractions

Now we’d like to know which transcripts are differentially regulated in the mito fractions comparing sedentary and trained sample groups. Using DESeq2 I show that there are no consistent differences observed at the FDR level. I used the mitochondrial fraction of reads as a covariate and it didn’t improve the p-values at all.

# covariate
run_de_cov <- function(ss,xx){

y <- round(xx)

# MDS
colours = c('yellow', 'orange')
mds <- cmdscale(dist(t(y)))
XMAX=max(mds[,1])*1.1
XMIN=min(mds[,1])*1.1
plot( mds*1.05 , cex=2 , pch=19, xlab="Coordinate 1", ylab="Coordinate 2",
  col = colours[as.factor(ss$trt)] ,  type = "p" ,
  xlim=c(XMIN,XMAX),main="MDS plot",bty="n")
text(mds, labels=colnames(y) )
legend('topright', col=colours, legend=c("ctrl","trt"), pch = 16, cex = 1.5)

# DE
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ mtfrac + trt)
#dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ trt)
dds <- DESeq(dds)
de <- DESeq2::results(dds)
de <- de[order(de$pvalue),]
up <- rownames(subset(de, log2FoldChange>0 & padj<0.05 ))
dn <- rownames(subset(de, log2FoldChange<0 & padj<0.05 ))
str(up)
str(dn)

# MA plot
sig <-subset(de, padj < 0.05 )
GENESUP <- length(up)
GENESDN <- length(dn)
SUBHEADER = paste(GENESUP, "up, ", GENESDN, "down")
ns <-subset(de, padj > 0.05 )
plot(log2(de$baseMean),de$log2FoldChange,
     xlab="log2 basemean", ylab="log2 foldchange",
     pch=19, cex=0.5, col="dark gray",
     main="smear plot")
points(log2(sig$baseMean),sig$log2FoldChange,
       pch=19, cex=0.5, col="red")
mtext(SUBHEADER)

# heatmap
yn <- y/colSums(y)*1000000
yf <- yn[which(rownames(yn) %in% rownames(de)[1:20]),]
mycols <- gsub("S","yellow",ss$trt)
mycols <- gsub("T","orange",mycols)
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(  as.matrix(yf), col=colfunc(25),scale="row",
    ColSideColors =mycols ,trace="none",
    margin = c(10,15), cexRow=0.8, cexCol=0.8 , main="Top 20 genes by p-val")
mtext("yellow=sed, orange=trn")

de <- merge(as.data.frame(de),yn,by=0)
rownames(de) <- de[,1]
de[,1]=NULL

de <- de[order(de$pvalue),]

return(de)
}

ss1 <- subset(ss,nreads>2e6 & fraction=="mito")
ss1$trt <- factor(ss1$Group)
x1 <- x[,which(colnames(x) %in% ss1$UDI)]
dim(x1)
## [1] 19938     8
x1 <- x1[which(rowMeans(x1)>10),]
dim(x1)
## [1] 14399     8
de1 <- run_de_cov(ss1,x1)
## converting counts to integer mode
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing

##  chr(0) 
##  chr(0)

head(de1,20)  %>% kbl(caption="top 20 genes by p-value") %>% kable_paper("hover", full_width = F)
top 20 genes by p-value
baseMean log2FoldChange lfcSE stat pvalue padj 33 34 47 52 58 59 60 64
Obscn protein_coding 876.825294 -0.9979465 0.2905475 -3.434711 0.0005932 0.9997206 111.141806 37.6905045 160.8086796 773.949261 278.887997 121.6734313 59.8270533 161.5108378
St8sia5 protein_coding 21.851523 1.3413550 0.4294442 3.123468 0.0017873 0.9997206 5.152493 3.7403492 1.0717679 2.525791 6.652030 14.1807456 4.9378396 1.6912241
Lrrc29 protein_coding 13.114260 1.5919602 0.5325210 2.989479 0.0027945 0.9997206 3.470625 3.9996975 0.1266113 1.162717 4.161629 4.5418526 4.6443276 1.4032171
Gata3 protein_coding 20.217508 1.3163191 0.4411174 2.984056 0.0028445 0.9997206 6.908568 2.5322254 0.6342090 3.567111 9.885209 3.0366757 5.0515816 3.7598432
Tifab protein_coding 14.521850 -1.4735780 0.5045511 -2.920573 0.0034939 0.9997206 2.774419 4.5418526 3.9298156 3.087078 2.602968 1.4544354 0.7596676 2.1140302
Slu7 protein_coding 28.329347 -0.9642324 0.3389020 -2.845166 0.0044388 0.9997206 7.480698 2.3221638 5.4725467 13.014842 9.090222 2.9120592 2.0083286 8.9177763
Atpsckmt protein_coding 17.871816 1.2967511 0.4568337 2.838563 0.0045317 0.9997206 4.557975 5.3433560 1.4290239 1.683860 8.098124 5.0905241 3.6717269 0.9513136
Fcho1 protein_coding 46.478271 -0.8111672 0.2881923 -2.814674 0.0048827 0.9997206 7.090231 2.7482392 13.6739237 13.358390 7.681003 3.9290079 11.8579670 25.4526203
Mks1 protein_coding 34.540589 0.9054514 0.3251192 2.784983 0.0053531 0.9997206 5.894723 4.2096513 5.2059367 11.999092 6.837009 4.8622694 8.5214307 6.9463629
RNase_MRP ribozyme 122.272114 -1.6891331 0.6187080 -2.730097 0.0063316 0.9997206 8.816538 3.0366757 33.5368887 120.025764 23.998185 17.3457441 4.7565678 19.4209351
Snrnp70 protein_coding 43.429113 0.8234324 0.3023866 2.723112 0.0064670 0.9997206 13.453528 4.9378396 3.0653437 7.926912 17.633075 10.8963070 7.1564072 8.9657799
Gm12169 protein_coding 9.978901 -1.5977405 0.5894242 -2.710680 0.0067145 0.9997206 4.274685 1.4290239 1.2628954 5.205937 1.818044 0.5064451 0.3171045 3.5671105
Etv4 protein_coding 27.736411 -0.9773083 0.3605662 -2.710482 0.0067185 0.9997206 10.411874 6.5449595 4.3047832 2.748239 4.954320 4.2746848 3.0366757 7.8580158
Magel2 protein_coding 12.293622 -1.4208962 0.5305043 -2.678388 0.0073978 0.9997206 2.671678 0.5358840 2.2451474 3.470625 4.363306 1.0128902 0.7399106 7.5305667
Pmpcb protein_coding 31.587927 0.9477570 0.3557676 2.663978 0.0077223 0.9997206 4.630616 9.2549986 6.5449595 3.291893 3.276747 8.9177763 12.5568867 3.7511877
Kctd7 protein_coding 16.981527 -1.2243307 0.4602606 -2.660081 0.0078122 0.9997206 4.627499 0.3636089 3.7983381 2.959642 3.368938 4.0075170 1.7862798 4.0693296
Rnpep protein_coding 32.878798 0.9928234 0.3739498 2.654964 0.0079317 0.9997206 3.065344 4.9543202 6.6791951 2.143536 5.753190 15.6178102 19.2712697 3.7983381
Ybx1 protein_coding 27.676074 0.9340399 0.3579120 2.609692 0.0090624 0.9997206 4.954320 6.6791951 3.5725597 3.227399 11.568748 12.7263102 5.1910621 1.6912241
Slc22a20 protein_coding 24.182140 1.0293425 0.3948514 2.606911 0.0091363 0.9997206 2.972592 6.9463629 3.0366757 2.666113 10.701092 11.2718747 4.5580058 1.4798211
Gja4 protein_coding 11.560493 -1.4822458 0.5752507 -2.576695 0.0099750 0.9997206 2.545262 1.0128902 2.9596422 3.963456 2.404510 1.6076519 0.2806434 2.8921871

Session info

sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 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] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] beeswarm_0.4.0              kableExtra_1.3.4           
##  [3] gplots_3.1.3                mitch_1.8.0                
##  [5] DESeq2_1.36.0               SummarizedExperiment_1.26.1
##  [7] Biobase_2.56.0              MatrixGenerics_1.8.0       
##  [9] matrixStats_0.62.0          GenomicRanges_1.48.0       
## [11] GenomeInfoDb_1.32.2         IRanges_2.30.0             
## [13] S4Vectors_0.34.0            BiocGenerics_0.42.0        
## [15] reshape2_1.4.4             
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           bit64_4.0.5            webshot_0.5.3         
##  [4] RColorBrewer_1.1-3     httr_1.4.3             tools_4.2.1           
##  [7] bslib_0.3.1            utf8_1.2.2             R6_2.5.1              
## [10] KernSmooth_2.23-20     DBI_1.1.3              colorspace_2.0-3      
## [13] gridExtra_2.3          tidyselect_1.1.2       GGally_2.1.2          
## [16] bit_4.0.4              compiler_4.2.1         rvest_1.0.2           
## [19] cli_3.3.0              xml2_1.3.3             DelayedArray_0.22.0   
## [22] sass_0.4.1             caTools_1.18.2         scales_1.2.0          
## [25] genefilter_1.78.0      systemfonts_1.0.4      stringr_1.4.0         
## [28] digest_0.6.29          svglite_2.1.0          rmarkdown_2.14        
## [31] XVector_0.36.0         pkgconfig_2.0.3        htmltools_0.5.2       
## [34] highr_0.9              fastmap_1.1.0          htmlwidgets_1.5.4     
## [37] rlang_1.0.2            rstudioapi_0.13        RSQLite_2.2.14        
## [40] shiny_1.7.1            jquerylib_0.1.4        generics_0.1.2        
## [43] jsonlite_1.8.0         gtools_3.9.2.2         BiocParallel_1.30.3   
## [46] dplyr_1.0.9            RCurl_1.98-1.7         magrittr_2.0.3        
## [49] GenomeInfoDbData_1.2.8 Matrix_1.4-1           Rcpp_1.0.8.3          
## [52] munsell_0.5.0          fansi_1.0.3            lifecycle_1.0.1       
## [55] stringi_1.7.6          yaml_2.3.5             MASS_7.3-57           
## [58] zlibbioc_1.42.0        plyr_1.8.7             grid_4.2.1            
## [61] blob_1.2.3             promises_1.2.0.1       parallel_4.2.1        
## [64] crayon_1.5.1           lattice_0.20-45        Biostrings_2.64.0     
## [67] echarts4r_0.4.4        splines_4.2.1          annotate_1.74.0       
## [70] KEGGREST_1.36.2        locfit_1.5-9.5         knitr_1.39            
## [73] pillar_1.7.0           tcltk_4.2.1            geneplotter_1.74.0    
## [76] codetools_0.2-18       XML_3.99-0.10          glue_1.6.2            
## [79] evaluate_0.15          png_0.1-7              vctrs_0.4.1           
## [82] httpuv_1.6.5           gtable_0.3.0           purrr_0.3.4           
## [85] reshape_0.8.9          assertthat_0.2.1       cachem_1.0.6          
## [88] ggplot2_3.3.6          xfun_0.31              mime_0.12             
## [91] xtable_1.8-4           later_1.3.0            viridisLite_0.4.0     
## [94] survival_3.3-1         tibble_3.1.7           AnnotationDbi_1.58.0  
## [97] memoise_2.0.1          ellipsis_0.3.2