Transcriptome sequencing of whole cell and mito fractions of sedentary and trained rats
Mark Ziemann
2020-12-04
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 MiSeq system generating paired end 150 bp reads.
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")
# 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
## ENSRNOT00000000008.4 0.0000 5.17195 10.0000 6.12831 5.26551 3.0000
## ENSRNOT00000000009.5 10.0209 47.86210 53.7012 35.32830 34.89250 51.1897
## ENSRNOT00000000010.5 1.0000 1.00000 0.0000 0.00000 0.00000 0.0000
## ENSRNOT00000000011.5 0.0000 0.00000 1.0000 1.00000 0.00000 1.0000
## ENSRNOT00000000013.5 299.6020 674.72100 346.8700 553.52400 741.68800 466.5910
## ENSRNOT00000000018.7 0.0000 0.00000 2.0000 2.00000 1.00000 0.0000
## 15 16 17 18 19 2
## ENSRNOT00000000008.4 3.90088 0.0000 0.000 4.07072 16.4054 1.3407
## ENSRNOT00000000009.5 49.16160 27.3429 83.000 51.24440 63.8893 23.2207
## ENSRNOT00000000010.5 0.00000 0.0000 0.000 0.00000 0.0000 0.0000
## ENSRNOT00000000011.5 0.00000 1.0000 1.000 0.00000 1.0000 4.0000
## ENSRNOT00000000013.5 341.29800 372.3020 416.936 509.46900 513.9120 320.8710
## ENSRNOT00000000018.7 0.00000 0.0000 3.000 0.00000 1.0000 1.0000
## 20 21 22 23 24 25
## ENSRNOT00000000008.4 0.0000 20.9863 0.0000 6.54813 3.93027 13.1300
## ENSRNOT00000000009.5 42.3625 68.9222 17.6006 36.76790 24.36560 47.0854
## ENSRNOT00000000010.5 0.0000 2.0000 0.0000 0.00000 0.00000 0.0000
## ENSRNOT00000000011.5 2.0000 1.0000 6.0000 3.00000 3.00000 0.0000
## ENSRNOT00000000013.5 346.0990 936.4710 645.0050 845.56600 657.88000 653.9830
## ENSRNOT00000000018.7 0.0000 0.0000 0.0000 2.00000 4.00000 1.0000
## 26 27 28 29 3 30
## ENSRNOT00000000008.4 3.92594 10.0000 0.0000 0.0000 7.0000 6.53318
## ENSRNOT00000000009.5 63.18030 76.1159 43.9129 42.8444 37.8331 54.25200
## ENSRNOT00000000010.5 0.00000 0.0000 0.0000 0.0000 1.0000 0.00000
## ENSRNOT00000000011.5 0.00000 0.0000 2.0000 2.0000 1.0000 0.00000
## ENSRNOT00000000013.5 699.78700 850.1850 771.5110 475.2300 491.6540 343.60600
## ENSRNOT00000000018.7 0.00000 0.0000 0.0000 0.0000 3.0000 0.00000
## 31 32 33 34 35 36
## ENSRNOT00000000008.4 8.0000 0.0000 40.0898 19.1593 25.9218 11.4780
## ENSRNOT00000000009.5 41.2632 33.1978 28.8229 35.7135 70.6359 35.9501
## ENSRNOT00000000010.5 0.0000 1.0000 19.0000 11.0000 37.0000 15.0000
## ENSRNOT00000000011.5 4.0000 0.0000 35.0000 20.0000 34.0000 32.0000
## ENSRNOT00000000013.5 479.0410 319.9350 111.7250 72.2435 153.0400 62.8931
## ENSRNOT00000000018.7 0.0000 0.0000 14.0000 15.0000 29.0000 18.0000
## 37 38 39 4 40 41
## ENSRNOT00000000008.4 13.7003 22.6135 14.9332 6.50394 8.52695 12.2591
## ENSRNOT00000000009.5 50.6873 54.7243 35.2301 27.46480 34.80920 26.7389
## ENSRNOT00000000010.5 40.0000 41.0000 35.0000 0.00000 29.00000 10.0000
## ENSRNOT00000000011.5 47.0000 33.0000 14.0000 0.00000 28.00000 22.0000
## ENSRNOT00000000013.5 52.7508 85.5584 68.9108 294.70900 55.07810 72.7735
## ENSRNOT00000000018.7 27.0000 17.0000 13.0000 0.00000 14.00000 15.0000
## 42 43 44 45 46 47
## ENSRNOT00000000008.4 4.69899 13.6271 16.2942 24.1949 12.9460 32.2802
## ENSRNOT00000000009.5 33.73670 114.9440 47.4423 53.6637 41.5453 67.1163
## ENSRNOT00000000010.5 16.00000 35.0000 13.0000 32.0000 18.0000 25.0000
## ENSRNOT00000000011.5 21.00000 49.0000 29.0000 44.0000 39.0000 37.0000
## ENSRNOT00000000013.5 65.41440 121.1980 114.3950 131.3720 124.3370 131.1560
## ENSRNOT00000000018.7 11.00000 24.0000 15.0000 21.0000 18.0000 10.0000
## 48 49 5 50 51 52
## ENSRNOT00000000008.4 16.3970 36.6810 0.0000 32.6092 20.9075 23.8737
## ENSRNOT00000000009.5 36.0636 49.3216 32.9113 53.3994 60.2464 36.7990
## ENSRNOT00000000010.5 20.0000 38.0000 0.0000 42.0000 46.0000 40.0000
## ENSRNOT00000000011.5 25.0000 43.0000 0.0000 48.0000 56.0000 33.0000
## ENSRNOT00000000013.5 114.0700 78.2890 824.7450 130.2330 150.1700 93.1834
## ENSRNOT00000000018.7 14.0000 25.0000 0.0000 22.0000 29.0000 17.0000
## 53 54 55 56 57 58
## ENSRNOT00000000008.4 19.3599 24.1487 14.2184 32.6797 9.35056 10.1055
## ENSRNOT00000000009.5 39.9345 36.7259 56.0723 34.3157 55.51630 40.9847
## ENSRNOT00000000010.5 30.0000 40.0000 41.0000 29.0000 43.00000 27.0000
## ENSRNOT00000000011.5 54.0000 33.0000 53.0000 42.0000 33.00000 34.0000
## ENSRNOT00000000013.5 120.6350 129.7970 127.9770 122.6540 145.20200 117.3270
## ENSRNOT00000000018.7 18.0000 15.0000 24.0000 21.0000 17.00000 15.0000
## 59 6 60 61 62 63
## ENSRNOT00000000008.4 31.4681 15.3007 17.6768 18.0824 21.5445 30.3799
## ENSRNOT00000000009.5 41.2987 34.8236 53.4337 70.0980 50.4952 49.1870
## ENSRNOT00000000010.5 24.0000 0.0000 24.0000 34.0000 33.0000 41.0000
## ENSRNOT00000000011.5 34.0000 0.0000 50.0000 47.0000 45.0000 48.0000
## ENSRNOT00000000013.5 96.5047 645.2290 100.4960 139.6330 153.3700 129.0280
## ENSRNOT00000000018.7 14.0000 0.0000 25.0000 14.0000 19.0000 25.0000
## 64 7 8 9 95 96
## ENSRNOT00000000008.4 29.0847 1.67668 0.0000 0.0000 0 0.0000
## ENSRNOT00000000009.5 48.8472 19.33470 22.7956 30.2034 0 0.0000
## ENSRNOT00000000010.5 24.0000 0.00000 1.0000 0.0000 0 0.0000
## ENSRNOT00000000011.5 51.0000 0.00000 3.0000 0.0000 0 0.0000
## ENSRNOT00000000013.5 155.7100 400.78800 316.4870 461.2840 6 28.8082
## ENSRNOT00000000018.7 33.0000 0.00000 3.0000 0.0000 0 0.0000#dont forget gene names
g<-read.table("../ref/Rattus_norvegicus.Rnor_6.0.cdna+ncrna.gene_names.tsv",row.names=1)
g$gene_ID <- paste(g$V2,g$V3,g$V4)
head(g)## V2 V3 V4
## ENSRNOT00000047550.4 MT ENSRNOG00000030644.4 Mt-nd1
## ENSRNOT00000040993.4 MT ENSRNOG00000031033.4 Mt-nd2
## ENSRNOT00000050156.3 MT ENSRNOG00000034234.3 Mt-co1
## ENSRNOT00000043693.3 MT ENSRNOG00000030371.3 Mt-co2
## ENSRNOT00000046201.3 MT ENSRNOG00000033299.3 Mt-atp8
## ENSRNOT00000046108.3 MT ENSRNOG00000031979.3 Mt-atp6
## gene_ID
## ENSRNOT00000047550.4 MT ENSRNOG00000030644.4 Mt-nd1
## ENSRNOT00000040993.4 MT ENSRNOG00000031033.4 Mt-nd2
## ENSRNOT00000050156.3 MT ENSRNOG00000034234.3 Mt-co1
## ENSRNOT00000043693.3 MT ENSRNOG00000030371.3 Mt-co2
## ENSRNOT00000046201.3 MT ENSRNOG00000033299.3 Mt-atp8
## ENSRNOT00000046108.3 MT ENSRNOG00000031979.3 Mt-atp6g[,1:3]=NULL
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
## 1 ENSRNOG00000000417.7 Numa1 3301 4795 4460 4965 5104 4177 3716 4657
## 1 ENSRNOG00000001466.6 LOC100361492 0 0 0 0 0 0 0 0
## 1 ENSRNOG00000001488.6 Psmb1 1525 1671 1845 1952 1631 1286 1407 1245
## 1 ENSRNOG00000001489.5 Tbp 189 181 195 156 154 156 120 159
## 1 ENSRNOG00000001490.4 Pdcd2 95 114 123 125 120 93 107 96
## 1 ENSRNOG00000001492.7 Slc8a2 188 150 155 192 199 173 158 192
## 17 18 19 2 20 21 22 23
## 1 ENSRNOG00000000417.7 Numa1 4314 4337 3901 2782 3403 5194 3945 4823
## 1 ENSRNOG00000001466.6 LOC100361492 1 0 0 0 0 0 0 0
## 1 ENSRNOG00000001488.6 Psmb1 1756 1851 1947 1204 2112 2717 1679 1861
## 1 ENSRNOG00000001489.5 Tbp 188 197 210 147 165 241 183 234
## 1 ENSRNOG00000001490.4 Pdcd2 190 159 175 59 171 227 124 137
## 1 ENSRNOG00000001492.7 Slc8a2 250 262 188 106 210 365 253 246
## 24 25 26 27 28 29 3 30
## 1 ENSRNOG00000000417.7 Numa1 4317 5347 4593 5806 4751 3516 3178 3746
## 1 ENSRNOG00000001466.6 LOC100361492 0 0 0 0 0 0 0 0
## 1 ENSRNOG00000001488.6 Psmb1 1707 1830 1644 1567 1776 1343 1737 1470
## 1 ENSRNOG00000001489.5 Tbp 200 201 194 153 155 135 127 160
## 1 ENSRNOG00000001490.4 Pdcd2 152 177 118 129 161 62 143 112
## 1 ENSRNOG00000001492.7 Slc8a2 323 300 266 194 248 123 110 217
## 31 32 33 34 35 36 37 38 39 4
## 1 ENSRNOG00000000417.7 Numa1 3497 2733 192 137 252 173 206 219 166 3357
## 1 ENSRNOG00000001466.6 LOC100361492 0 0 20 24 23 13 15 19 24 0
## 1 ENSRNOG00000001488.6 Psmb1 1762 1083 27 15 30 28 16 29 23 1649
## 1 ENSRNOG00000001489.5 Tbp 171 128 19 23 31 18 28 45 39 168
## 1 ENSRNOG00000001490.4 Pdcd2 123 84 21 12 33 12 13 30 20 85
## 1 ENSRNOG00000001492.7 Slc8a2 155 183 250 238 360 245 335 349 229 185
## 40 41 42 43 44 45 46 47 48 49
## 1 ENSRNOG00000000417.7 Numa1 168 154 144 336 190 188 199 205 162 229
## 1 ENSRNOG00000001466.6 LOC100361492 15 18 13 17 8 25 12 10 17 21
## 1 ENSRNOG00000001488.6 Psmb1 23 18 11 43 19 23 28 22 21 20
## 1 ENSRNOG00000001489.5 Tbp 17 25 21 54 26 38 25 34 24 30
## 1 ENSRNOG00000001490.4 Pdcd2 19 13 14 31 28 18 24 18 25 19
## 1 ENSRNOG00000001492.7 Slc8a2 227 241 191 482 286 323 305 339 250 340
## 5 50 51 52 53 54 55 56 57 58
## 1 ENSRNOG00000000417.7 Numa1 4574 243 296 243 214 232 249 265 237 235
## 1 ENSRNOG00000001466.6 LOC100361492 0 31 44 11 26 28 29 37 32 14
## 1 ENSRNOG00000001488.6 Psmb1 1879 37 40 24 24 29 29 22 29 43
## 1 ENSRNOG00000001489.5 Tbp 150 43 42 36 44 45 41 44 26 36
## 1 ENSRNOG00000001490.4 Pdcd2 128 29 28 35 26 33 28 30 20 20
## 1 ENSRNOG00000001492.7 Slc8a2 151 378 501 384 364 336 355 420 408 403
## 59 6 60 61 62 63 64 7 8 9
## 1 ENSRNOG00000000417.7 Numa1 185 4385 281 255 250 300 247 3642 3364 3272
## 1 ENSRNOG00000001466.6 LOC100361492 30 0 24 24 31 31 23 0 1 0
## 1 ENSRNOG00000001488.6 Psmb1 34 1459 45 25 37 34 19 1518 1201 1268
## 1 ENSRNOG00000001489.5 Tbp 25 150 37 34 46 42 25 178 88 143
## 1 ENSRNOG00000001490.4 Pdcd2 19 105 24 35 25 28 22 108 66 78
## 1 ENSRNOG00000001492.7 Slc8a2 345 183 382 442 448 440 419 147 164 127
## 95 96
## 1 ENSRNOG00000000417.7 Numa1 2 32
## 1 ENSRNOG00000001466.6 LOC100361492 0 0
## 1 ENSRNOG00000001488.6 Psmb1 5 0
## 1 ENSRNOG00000001489.5 Tbp 0 0
## 1 ENSRNOG00000001490.4 Pdcd2 0 0
## 1 ENSRNOG00000001492.7 Slc8a2 0 0Samplesheet
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## UDI fraction Group RatID label
## 1 1 wholetissue T 1 wholetissue T
## 2 2 wholetissue T 2 wholetissue T
## 3 3 wholetissue S 3 wholetissue S
## 4 4 wholetissue S 4 wholetissue S
## 5 5 wholetissue T 5 wholetissue T
## 6 6 wholetissue T 6 wholetissue T
## 7 7 wholetissue S 7 wholetissue S
## 8 8 wholetissue S 8 wholetissue S
## 9 9 wholetissue T 9 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
## 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
## 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
## 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
## 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
## 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 SOverall 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)
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="number of reads per sample",cex.names=0.5)
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$label==mylevels[4])]
wholeT <- colSums(y[grep("^MT",rownames(y)),]) / colSums(y)
boxplot(mitoS,mitoT,wholeS,wholeT,names=mylevels,ylab="mito frac")
median(wholeS)## [1] 0.3338761median(wholeT)## [1] 0.3550418table(mitoS>0.4)##
## FALSE TRUE
## 14 2table(mitoT>0.4)##
## FALSE TRUE
## 11 5ss$mtfrac <- mtfracFunctions
run_de <- 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$trt)] , type = "p" ,
xlim=c(XMIN,XMAX),main="MDS plot",bty="n")
text(mds, labels=colnames(y) )
legend('topright', col=colours, legend=c("ctrl","trt"), pch = 16, cex = 1.5)
# DE
dds <- DESeqDataSetFromMatrix(countData=y, colData = ss, design = ~ trt)
dds <- DESeq(dds)
de <- DESeq2::results(dds)
de <- de[order(de$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)[1:50]),]
mycols <- gsub("0","yellow",ss$trt)
mycols <- gsub("1","orange",mycols)
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2( as.matrix(yf), col=colfunc(25),scale="row",
ColSideColors =mycols ,trace="none",
margin = c(10,15), cexRow=0.6, cexCol=0.8 , main="Top 50 genes by p-val")
mtext("yellow=ctrl, orange=trt")
return(de)
}Set up contrasts
Whole tissue S versus T
Mito fraction S versus T
Whole versus mito fraction in S
Whole versus mito fraction in T
ss1 <- subset(samplesheet,fraction=="wholetissue")
ss1$trt <- grepl("T",ss1$Group)*1
x1 <- x[,which(colnames(x) %in% rownames(ss1))]
ss2 <- subset(samplesheet,fraction=="mito")
ss2$trt <- grepl("T",ss2$Group)*1
x2 <- x[,which(colnames(x) %in% rownames(ss2))]
ss3 <- subset(samplesheet,Group=="S")
ss3$trt <- grepl("mito",ss3$fraction)*1
x3 <- x[,which(colnames(x) %in% rownames(ss3))]
ss4 <- subset(samplesheet,Group=="T")
ss4$trt <- grepl("mito",ss4$fraction)*1
x4 <- x[,which(colnames(x) %in% rownames(ss4))]DE
Here, were using DESeq2 to perform differential expression analysis for the specified contrasts. The run_de function does the analysis and generate the charts. Here we actually run the analysis.
Whole tissue S versus T: 1 DEG
Mito fraction S versus T: 0 DEGs
Whole versus mito fraction in S: 22754 DEGs
Whole versus mito fraction in T: 22406 DEGs
These results suggest that:
The differences between S and T are very subtle.
Intra-group variation is fairly large.
ss1## UDI fraction Group RatID label trt
## 1 1 wholetissue T 1 wholetissue T 1
## 2 2 wholetissue T 2 wholetissue T 1
## 3 3 wholetissue S 3 wholetissue S 0
## 4 4 wholetissue S 4 wholetissue S 0
## 5 5 wholetissue T 5 wholetissue T 1
## 6 6 wholetissue T 6 wholetissue T 1
## 7 7 wholetissue S 7 wholetissue S 0
## 8 8 wholetissue S 8 wholetissue S 0
## 9 9 wholetissue T 9 wholetissue T 1
## 10 10 wholetissue T 10 wholetissue T 1
## 11 11 wholetissue S 11 wholetissue S 0
## 12 12 wholetissue S 12 wholetissue S 0
## 13 13 wholetissue T 13 wholetissue T 1
## 14 14 wholetissue T 14 wholetissue T 1
## 15 15 wholetissue S 15 wholetissue S 0
## 16 16 wholetissue S 16 wholetissue S 0
## 17 17 wholetissue T 17 wholetissue T 1
## 18 18 wholetissue T 18 wholetissue T 1
## 19 19 wholetissue S 19 wholetissue S 0
## 20 20 wholetissue S 20 wholetissue S 0
## 21 21 wholetissue T 21 wholetissue T 1
## 22 22 wholetissue T 22 wholetissue T 1
## 23 23 wholetissue S 23 wholetissue S 0
## 24 24 wholetissue S 24 wholetissue S 0
## 25 25 wholetissue T 25 wholetissue T 1
## 26 26 wholetissue T 26 wholetissue T 1
## 27 27 wholetissue T 27 wholetissue T 1
## 28 28 wholetissue T 28 wholetissue T 1
## 29 29 wholetissue S 29 wholetissue S 0
## 30 30 wholetissue S 30 wholetissue S 0
## 31 31 wholetissue S 31 wholetissue S 0
## 32 32 wholetissue S 32 wholetissue S 0de1 <- run_de(ss1,x1)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 187 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing
## chr "8 ENSRNOG00000054774.1 SNORD14"
## chr(0)
as.data.frame(de1[1:20,])## baseMean log2FoldChange lfcSE
## 8 ENSRNOG00000054774.1 SNORD14 2.559404 3.0038415 0.61757231
## 6 ENSRNOG00000013097.4 LOC691485 6.418021 -1.2981181 0.33416843
## 4 ENSRNOG00000009863.8 Prrt3 34.415892 -0.5955203 0.16136270
## 14 ENSRNOG00000052298.1 AABR07014974.1 225.654033 -0.2661652 0.07212133
## 17 ENSRNOG00000059621.1 AABR07027502.1 2.331437 2.3852231 0.65165708
## 1 ENSRNOG00000029784.4 Pak1 253.982556 -0.3028753 0.08642951
## 18 ENSRNOG00000016275.4 Ttr 11.727925 -3.0817896 0.89633403
## 3 ENSRNOG00000028616.6 Pck1 22.651521 -1.6601164 0.50334305
## 1 ENSRNOG00000020188.8 LOC102549471 24.421430 -0.5559625 0.17168992
## X ENSRNOG00000057147.1 Praf2 52.230594 0.3109798 0.09642761
## 17 ENSRNOG00000055942.1 AC120310.1 6.646284 1.0329367 0.32106516
## Y ENSRNOG00000062090.1 Sry 14.618619 0.7028882 0.21860668
## 1 ENSRNOG00000017538.8 Lrrc27 12.502304 -0.5724669 0.18090780
## 1 ENSRNOG00000018257.5 Hpx 9.278214 -2.4935056 0.79209321
## 10 ENSRNOG00000004834.5 Llgl2 199.988997 -0.2241675 0.07147975
## 4 ENSRNOG00000008031.7 Cacna2d4 8.089351 -0.9252866 0.29680104
## 1 ENSRNOG00000031003.5 Zfp764 86.932883 -0.2517188 0.08097268
## 5 ENSRNOG00000019589.2 Tas1r3 48.207189 -0.4108980 0.13388012
## 4 ENSRNOG00000023337.2 Sema3a 19.472169 0.9205249 0.30303588
## 20 ENSRNOG00000001242.6 Gstt3 65.356944 -0.2870963 0.09529135
## stat pvalue padj
## 8 ENSRNOG00000054774.1 SNORD14 4.863951 1.150654e-06 0.03139673
## 6 ENSRNOG00000013097.4 LOC691485 -3.884622 1.024892e-04 0.99890044
## 4 ENSRNOG00000009863.8 Prrt3 -3.690570 2.237525e-04 0.99890044
## 14 ENSRNOG00000052298.1 AABR07014974.1 -3.690520 2.237962e-04 0.99890044
## 17 ENSRNOG00000059621.1 AABR07027502.1 3.660243 2.519765e-04 0.99890044
## 1 ENSRNOG00000029784.4 Pak1 -3.504304 4.578019e-04 0.99890044
## 18 ENSRNOG00000016275.4 Ttr -3.438216 5.855613e-04 0.99890044
## 3 ENSRNOG00000028616.6 Pck1 -3.298181 9.731344e-04 0.99890044
## 1 ENSRNOG00000020188.8 LOC102549471 -3.238178 1.202959e-03 0.99890044
## X ENSRNOG00000057147.1 Praf2 3.225008 1.259690e-03 0.99890044
## 17 ENSRNOG00000055942.1 AC120310.1 3.217218 1.294401e-03 0.99890044
## Y ENSRNOG00000062090.1 Sry 3.215310 1.303038e-03 0.99890044
## 1 ENSRNOG00000017538.8 Lrrc27 -3.164412 1.553964e-03 0.99890044
## 1 ENSRNOG00000018257.5 Hpx -3.147995 1.643944e-03 0.99890044
## 10 ENSRNOG00000004834.5 Llgl2 -3.136098 1.712120e-03 0.99890044
## 4 ENSRNOG00000008031.7 Cacna2d4 -3.117532 1.823724e-03 0.99890044
## 1 ENSRNOG00000031003.5 Zfp764 -3.108688 1.879201e-03 0.99890044
## 5 ENSRNOG00000019589.2 Tas1r3 -3.069149 2.146696e-03 0.99890044
## 4 ENSRNOG00000023337.2 Sema3a 3.037676 2.384100e-03 0.99890044
## 20 ENSRNOG00000001242.6 Gstt3 -3.012827 2.588264e-03 0.99890044write.table(de1,file="de1.tsv",quote=FALSE,sep="\t")
ss2## UDI fraction Group RatID label trt
## 33 33 mito T 1 mito T 1
## 34 34 mito T 2 mito T 1
## 35 35 mito S 3 mito S 0
## 36 36 mito S 4 mito S 0
## 37 37 mito T 5 mito T 1
## 38 38 mito T 6 mito T 1
## 39 39 mito S 7 mito S 0
## 40 40 mito S 8 mito S 0
## 41 41 mito T 9 mito T 1
## 42 42 mito T 10 mito T 1
## 43 43 mito S 11 mito S 0
## 44 44 mito S 12 mito S 0
## 45 45 mito T 13 mito T 1
## 46 46 mito T 14 mito T 1
## 47 47 mito S 15 mito S 0
## 48 48 mito S 16 mito S 0
## 49 49 mito T 17 mito T 1
## 50 50 mito T 18 mito T 1
## 51 51 mito S 19 mito S 0
## 52 52 mito S 20 mito S 0
## 53 53 mito T 21 mito T 1
## 54 54 mito T 22 mito T 1
## 55 55 mito S 23 mito S 0
## 56 56 mito S 24 mito S 0
## 57 57 mito T 25 mito T 1
## 58 58 mito T 26 mito T 1
## 59 59 mito T 27 mito T 1
## 60 60 mito T 28 mito T 1
## 61 61 mito S 29 mito S 0
## 62 62 mito S 30 mito S 0
## 63 63 mito S 31 mito S 0
## 64 64 mito S 32 mito S 0de2 <- run_de(ss2,x2)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 43 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing
## chr(0)
## chr(0)
as.data.frame(de1[1:20,])## baseMean log2FoldChange lfcSE
## 8 ENSRNOG00000054774.1 SNORD14 2.559404 3.0038415 0.61757231
## 6 ENSRNOG00000013097.4 LOC691485 6.418021 -1.2981181 0.33416843
## 4 ENSRNOG00000009863.8 Prrt3 34.415892 -0.5955203 0.16136270
## 14 ENSRNOG00000052298.1 AABR07014974.1 225.654033 -0.2661652 0.07212133
## 17 ENSRNOG00000059621.1 AABR07027502.1 2.331437 2.3852231 0.65165708
## 1 ENSRNOG00000029784.4 Pak1 253.982556 -0.3028753 0.08642951
## 18 ENSRNOG00000016275.4 Ttr 11.727925 -3.0817896 0.89633403
## 3 ENSRNOG00000028616.6 Pck1 22.651521 -1.6601164 0.50334305
## 1 ENSRNOG00000020188.8 LOC102549471 24.421430 -0.5559625 0.17168992
## X ENSRNOG00000057147.1 Praf2 52.230594 0.3109798 0.09642761
## 17 ENSRNOG00000055942.1 AC120310.1 6.646284 1.0329367 0.32106516
## Y ENSRNOG00000062090.1 Sry 14.618619 0.7028882 0.21860668
## 1 ENSRNOG00000017538.8 Lrrc27 12.502304 -0.5724669 0.18090780
## 1 ENSRNOG00000018257.5 Hpx 9.278214 -2.4935056 0.79209321
## 10 ENSRNOG00000004834.5 Llgl2 199.988997 -0.2241675 0.07147975
## 4 ENSRNOG00000008031.7 Cacna2d4 8.089351 -0.9252866 0.29680104
## 1 ENSRNOG00000031003.5 Zfp764 86.932883 -0.2517188 0.08097268
## 5 ENSRNOG00000019589.2 Tas1r3 48.207189 -0.4108980 0.13388012
## 4 ENSRNOG00000023337.2 Sema3a 19.472169 0.9205249 0.30303588
## 20 ENSRNOG00000001242.6 Gstt3 65.356944 -0.2870963 0.09529135
## stat pvalue padj
## 8 ENSRNOG00000054774.1 SNORD14 4.863951 1.150654e-06 0.03139673
## 6 ENSRNOG00000013097.4 LOC691485 -3.884622 1.024892e-04 0.99890044
## 4 ENSRNOG00000009863.8 Prrt3 -3.690570 2.237525e-04 0.99890044
## 14 ENSRNOG00000052298.1 AABR07014974.1 -3.690520 2.237962e-04 0.99890044
## 17 ENSRNOG00000059621.1 AABR07027502.1 3.660243 2.519765e-04 0.99890044
## 1 ENSRNOG00000029784.4 Pak1 -3.504304 4.578019e-04 0.99890044
## 18 ENSRNOG00000016275.4 Ttr -3.438216 5.855613e-04 0.99890044
## 3 ENSRNOG00000028616.6 Pck1 -3.298181 9.731344e-04 0.99890044
## 1 ENSRNOG00000020188.8 LOC102549471 -3.238178 1.202959e-03 0.99890044
## X ENSRNOG00000057147.1 Praf2 3.225008 1.259690e-03 0.99890044
## 17 ENSRNOG00000055942.1 AC120310.1 3.217218 1.294401e-03 0.99890044
## Y ENSRNOG00000062090.1 Sry 3.215310 1.303038e-03 0.99890044
## 1 ENSRNOG00000017538.8 Lrrc27 -3.164412 1.553964e-03 0.99890044
## 1 ENSRNOG00000018257.5 Hpx -3.147995 1.643944e-03 0.99890044
## 10 ENSRNOG00000004834.5 Llgl2 -3.136098 1.712120e-03 0.99890044
## 4 ENSRNOG00000008031.7 Cacna2d4 -3.117532 1.823724e-03 0.99890044
## 1 ENSRNOG00000031003.5 Zfp764 -3.108688 1.879201e-03 0.99890044
## 5 ENSRNOG00000019589.2 Tas1r3 -3.069149 2.146696e-03 0.99890044
## 4 ENSRNOG00000023337.2 Sema3a 3.037676 2.384100e-03 0.99890044
## 20 ENSRNOG00000001242.6 Gstt3 -3.012827 2.588264e-03 0.99890044write.table(de2,file="de2.tsv",quote=FALSE,sep="\t")
ss3## UDI fraction Group RatID label trt
## 3 3 wholetissue S 3 wholetissue S 0
## 4 4 wholetissue S 4 wholetissue S 0
## 7 7 wholetissue S 7 wholetissue S 0
## 8 8 wholetissue S 8 wholetissue S 0
## 11 11 wholetissue S 11 wholetissue S 0
## 12 12 wholetissue S 12 wholetissue S 0
## 15 15 wholetissue S 15 wholetissue S 0
## 16 16 wholetissue S 16 wholetissue S 0
## 19 19 wholetissue S 19 wholetissue S 0
## 20 20 wholetissue S 20 wholetissue S 0
## 23 23 wholetissue S 23 wholetissue S 0
## 24 24 wholetissue S 24 wholetissue S 0
## 29 29 wholetissue S 29 wholetissue S 0
## 30 30 wholetissue S 30 wholetissue S 0
## 31 31 wholetissue S 31 wholetissue S 0
## 32 32 wholetissue S 32 wholetissue S 0
## 35 35 mito S 3 mito S 1
## 36 36 mito S 4 mito S 1
## 39 39 mito S 7 mito S 1
## 40 40 mito S 8 mito S 1
## 43 43 mito S 11 mito S 1
## 44 44 mito S 12 mito S 1
## 47 47 mito S 15 mito S 1
## 48 48 mito S 16 mito S 1
## 51 51 mito S 19 mito S 1
## 52 52 mito S 20 mito S 1
## 55 55 mito S 23 mito S 1
## 56 56 mito S 24 mito S 1
## 61 61 mito S 29 mito S 1
## 62 62 mito S 30 mito S 1
## 63 63 mito S 31 mito S 1
## 64 64 mito S 32 mito S 1de3 <- run_de(ss3,x3)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 1021 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing
## chr [1:15793] "8 ENSRNOG00000031483.3 AABR07071482.1" ...
## chr [1:6961] "7 ENSRNOG00000028837.4 AC128207.1" ...
head(de3,20)## log2 fold change (MLE): trt
## Wald test p-value: trt
## DataFrame with 20 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## 7 ENSRNOG00000028837.4 AC128207.1 651.49961 -3.88988 0.490167
## 5 ENSRNOG00000032994.5 Myom3 905.26082 -2.34838 0.307146
## 20 ENSRNOG00000050647.2 Hspa1b 466.92249 -2.52965 0.331401
## 8 ENSRNOG00000031483.3 AABR07071482.1 5.28289 5.99866 0.829018
## 12 ENSRNOG00000001452.3 Fzd9 157.59935 -1.71999 0.241920
## ... ... ... ...
## 13 ENSRNOG00000003209.6 Pcp4l1 163.67894 2.08555 0.323851
## 11 ENSRNOG00000055575.1 Olr1545 3.74962 5.44399 0.847647
## 1 ENSRNOG00000013840.7 Ankrd2 1234.76921 -3.23027 0.503943
## 17 ENSRNOG00000022595.1 LOC100362965 3.54889 5.59743 0.874369
## 15 ENSRNOG00000010137.6 Xpo4 729.70955 -1.73268 0.271168
## stat pvalue padj
## <numeric> <numeric> <numeric>
## 7 ENSRNOG00000028837.4 AC128207.1 -7.93581 2.09123e-15 6.77244e-11
## 5 ENSRNOG00000032994.5 Myom3 -7.64583 2.07604e-14 2.47209e-10
## 20 ENSRNOG00000050647.2 Hspa1b -7.63320 2.29003e-14 2.47209e-10
## 8 ENSRNOG00000031483.3 AABR07071482.1 7.23586 4.62582e-13 3.74518e-09
## 12 ENSRNOG00000001452.3 Fzd9 -7.10974 1.16263e-12 7.53034e-09
## ... ... ... ...
## 13 ENSRNOG00000003209.6 Pcp4l1 6.43983 1.19606e-10 2.42091e-07
## 11 ENSRNOG00000055575.1 Olr1545 6.42248 1.34075e-10 2.53741e-07
## 1 ENSRNOG00000013840.7 Ankrd2 -6.40999 1.45530e-10 2.53741e-07
## 17 ENSRNOG00000022595.1 LOC100362965 6.40168 1.53673e-10 2.53741e-07
## 15 ENSRNOG00000010137.6 Xpo4 -6.38967 1.66249e-10 2.53741e-07write.table(de3,file="de3.tsv",quote=FALSE,sep="\t")
ss4## UDI fraction Group RatID label trt
## 1 1 wholetissue T 1 wholetissue T 0
## 2 2 wholetissue T 2 wholetissue T 0
## 5 5 wholetissue T 5 wholetissue T 0
## 6 6 wholetissue T 6 wholetissue T 0
## 9 9 wholetissue T 9 wholetissue T 0
## 10 10 wholetissue T 10 wholetissue T 0
## 13 13 wholetissue T 13 wholetissue T 0
## 14 14 wholetissue T 14 wholetissue T 0
## 17 17 wholetissue T 17 wholetissue T 0
## 18 18 wholetissue T 18 wholetissue T 0
## 21 21 wholetissue T 21 wholetissue T 0
## 22 22 wholetissue T 22 wholetissue T 0
## 25 25 wholetissue T 25 wholetissue T 0
## 26 26 wholetissue T 26 wholetissue T 0
## 27 27 wholetissue T 27 wholetissue T 0
## 28 28 wholetissue T 28 wholetissue T 0
## 33 33 mito T 1 mito T 1
## 34 34 mito T 2 mito T 1
## 37 37 mito T 5 mito T 1
## 38 38 mito T 6 mito T 1
## 41 41 mito T 9 mito T 1
## 42 42 mito T 10 mito T 1
## 45 45 mito T 13 mito T 1
## 46 46 mito T 14 mito T 1
## 49 49 mito T 17 mito T 1
## 50 50 mito T 18 mito T 1
## 53 53 mito T 21 mito T 1
## 54 54 mito T 22 mito T 1
## 57 57 mito T 25 mito T 1
## 58 58 mito T 26 mito T 1
## 59 59 mito T 27 mito T 1
## 60 60 mito T 28 mito T 1de4 <- run_de(ss4,x4)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 1292 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing
## chr [1:15569] "18 ENSRNOG00000059935.1 7SK" ...
## chr [1:6837] "15 ENSRNOG00000001052.7 Slc25a30" ...
head(de4,20)## log2 fold change (MLE): trt
## Wald test p-value: trt
## DataFrame with 20 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## 18 ENSRNOG00000059935.1 7SK 5.38745 6.09288 0.786191
## 15 ENSRNOG00000050432.2 AABR07017850.1 6.00234 6.27404 0.866595
## 11 ENSRNOG00000037983.3 LOC103693605 7.42209 5.72409 0.813110
## X ENSRNOG00000058972.1 AABR07042321.1 4.52193 5.84441 0.839960
## 1 ENSRNOG00000060881.1 AABR07003167.3 4.51377 5.82571 0.849694
## ... ... ... ...
## 3 ENSRNOG00000042321.2 AABR07052588.1 5.71903 3.16993 0.502554
## X ENSRNOG00000046097.1 AABR07042071.1 3.11742 5.48103 0.869241
## 7 ENSRNOG00000051708.1 Olr1084 6.29478 5.94607 0.946000
## 3 ENSRNOG00000053580.1 AABR07053471.1 4.03582 5.68406 0.905828
## 4 ENSRNOG00000051718.1 AABR07059220.1 2.89649 5.14333 0.824434
## stat pvalue padj
## <numeric> <numeric> <numeric>
## 18 ENSRNOG00000059935.1 7SK 7.74987 9.19847e-15 2.98095e-10
## 15 ENSRNOG00000050432.2 AABR07017850.1 7.23987 4.49107e-13 7.27711e-09
## 11 ENSRNOG00000037983.3 LOC103693605 7.03975 1.92589e-12 2.08041e-08
## X ENSRNOG00000058972.1 AABR07042321.1 6.95797 3.45216e-12 2.79686e-08
## 1 ENSRNOG00000060881.1 AABR07003167.3 6.85624 7.06970e-12 4.10878e-08
## ... ... ... ...
## 3 ENSRNOG00000042321.2 AABR07052588.1 6.30763 2.83336e-10 5.47485e-07
## X ENSRNOG00000046097.1 AABR07042071.1 6.30554 2.87199e-10 5.47485e-07
## 7 ENSRNOG00000051708.1 Olr1084 6.28549 3.26828e-10 5.88418e-07
## 3 ENSRNOG00000053580.1 AABR07053471.1 6.27499 3.49657e-10 5.96386e-07
## 4 ENSRNOG00000051718.1 AABR07059220.1 6.23861 4.41462e-10 7.11522e-07write.table(de4,file="de4.tsv",quote=FALSE,sep="\t")Second pass DE analysis
Here we are going to exclude the mito samples with < 40% mito reads. Contrast 1 is not repeated.
Mito fraction S versus T: 0 DEGs
Whole versus mito fraction in S: 20 DEGs
Whole versus mito fraction in T: 0 DEGs
ss0 <- subset(ss, fraction=="mito" & mtfrac>0.4)
ss <- subset(ss, fraction!="mito")
ss <- rbind(ss,ss0)
ss2 <- subset(ss,fraction=="mito")
ss2$trt <- grepl("T",ss2$Group)*1
x2 <- x[,which(colnames(x) %in% rownames(ss2))]
ss3 <- subset(ss,Group=="S")
ss3$trt <- grepl("mito",ss3$fraction)*1
x3 <- x[,which(colnames(x) %in% rownames(ss3))]
ss4 <- subset(ss,Group=="T")
ss4$trt <- grepl("mito",ss4$fraction)*1
x4 <- x[,which(colnames(x) %in% rownames(ss4))]
ss2## UDI fraction Group RatID label mtfrac trt
## 33 33 mito T 1 mito T 0.6465534 1
## 34 34 mito T 2 mito T 0.8147126 1
## 47 47 mito S 15 mito S 0.7268125 0
## 58 58 mito T 26 mito T 0.4821588 1
## 59 59 mito T 27 mito T 0.6646957 1
## 60 60 mito T 28 mito T 0.5887804 1
## 64 64 mito S 32 mito S 0.5016151 0de2 <- run_de(ss2,x2)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing
## chr(0)
## chr(0)
as.data.frame(de2[1:20,])## baseMean log2FoldChange lfcSE
## 17 ENSRNOG00000056825.2 AABR07072539.4 11.661671 6.5676953 1.5385473
## 1 ENSRNOG00000020025.4 LOC108348052 17.782509 2.9172245 0.7530693
## 3 ENSRNOG00000050864.2 Cpne1 14.072848 -2.9272763 0.7602602
## 2 ENSRNOG00000020832.4 C2cd4d 15.822890 2.7222352 0.7431078
## 7 ENSRNOG00000050884.2 AABR07058745.1 50.195404 1.6687089 0.4779421
## 20 ENSRNOG00000045654.2 LOC108348108 7.187484 5.8570854 1.6931454
## 4 ENSRNOG00000049639.2 LOC100909784 52.027683 1.1890107 0.3485934
## 13 ENSRNOG00000056279.1 AC115369.1 199.904660 -0.5561194 0.1700077
## 6 ENSRNOG00000051280.1 AABR07064502.1 5.509108 5.4622037 1.6700748
## 12 ENSRNOG00000057927.1 AABR07035796.1 58.135739 -0.9607682 0.2975668
## 18 ENSRNOG00000051569.1 LOC103690147 16.613644 2.4049396 0.7510958
## 14 ENSRNOG00000050406.1 AABR07014914.1 102.689535 -0.7408943 0.2335580
## 5 ENSRNOG00000048618.1 AABR07049890.1 68.590808 0.9768229 0.3118632
## 2 ENSRNOG00000061049.1 AABR07011512.1 391.297383 0.4903412 0.1577683
## 1 ENSRNOG00000038697.3 AABR07003616.1 2.349799 -4.7978937 1.5525918
## MT ENSRNOG00000029070.3 AY172581.1 28.057647 1.9396338 0.6302285
## 17 ENSRNOG00000052667.1 AABR07027555.1 4.412753 5.1807393 1.6974583
## KL568337.1 ENSRNOG00000057871.1 5_8S_rRNA 5.377655 -3.1465703 1.0320391
## 13 ENSRNOG00000052639.1 LOC108352591 11.174558 2.3679557 0.7780118
## 8 ENSRNOG00000059264.1 U6 5.160780 5.3206380 1.7779022
## stat pvalue padj
## 17 ENSRNOG00000056825.2 AABR07072539.4 4.268764 1.965591e-05 0.6313871
## 1 ENSRNOG00000020025.4 LOC108348052 3.873780 1.071604e-04 0.9999479
## 3 ENSRNOG00000050864.2 Cpne1 -3.850361 1.179438e-04 0.9999479
## 2 ENSRNOG00000020832.4 C2cd4d 3.663311 2.489758e-04 0.9999479
## 7 ENSRNOG00000050884.2 AABR07058745.1 3.491445 4.804147e-04 0.9999479
## 20 ENSRNOG00000045654.2 LOC108348108 3.459293 5.415960e-04 0.9999479
## 4 ENSRNOG00000049639.2 LOC100909784 3.410881 6.475334e-04 0.9999479
## 13 ENSRNOG00000056279.1 AC115369.1 -3.271142 1.071140e-03 0.9999479
## 6 ENSRNOG00000051280.1 AABR07064502.1 3.270634 1.073066e-03 0.9999479
## 12 ENSRNOG00000057927.1 AABR07035796.1 -3.228748 1.243335e-03 0.9999479
## 18 ENSRNOG00000051569.1 LOC103690147 3.201908 1.365205e-03 0.9999479
## 14 ENSRNOG00000050406.1 AABR07014914.1 -3.172207 1.512852e-03 0.9999479
## 5 ENSRNOG00000048618.1 AABR07049890.1 3.132216 1.734925e-03 0.9999479
## 2 ENSRNOG00000061049.1 AABR07011512.1 3.107983 1.883688e-03 0.9999479
## 1 ENSRNOG00000038697.3 AABR07003616.1 -3.090248 1.999894e-03 0.9999479
## MT ENSRNOG00000029070.3 AY172581.1 3.077667 2.086276e-03 0.9999479
## 17 ENSRNOG00000052667.1 AABR07027555.1 3.052057 2.272790e-03 0.9999479
## KL568337.1 ENSRNOG00000057871.1 5_8S_rRNA -3.048887 2.296911e-03 0.9999479
## 13 ENSRNOG00000052639.1 LOC108352591 3.043599 2.337668e-03 0.9999479
## 8 ENSRNOG00000059264.1 U6 2.992649 2.765673e-03 0.9999479write.table(de2,file="de2b.tsv",quote=FALSE,sep="\t")
ss3## UDI fraction Group RatID label mtfrac trt
## 11 11 wholetissue S 11 wholetissue S 0.3521941 0
## 12 12 wholetissue S 12 wholetissue S 0.2845851 0
## 15 15 wholetissue S 15 wholetissue S 0.3354321 0
## 16 16 wholetissue S 16 wholetissue S 0.3323202 0
## 19 19 wholetissue S 19 wholetissue S 0.3574090 0
## 20 20 wholetissue S 20 wholetissue S 0.3501682 0
## 23 23 wholetissue S 23 wholetissue S 0.3802950 0
## 24 24 wholetissue S 24 wholetissue S 0.2678982 0
## 29 29 wholetissue S 29 wholetissue S 0.3670548 0
## 3 3 wholetissue S 3 wholetissue S 0.3273260 0
## 30 30 wholetissue S 30 wholetissue S 0.3000018 0
## 31 31 wholetissue S 31 wholetissue S 0.3738198 0
## 32 32 wholetissue S 32 wholetissue S 0.3556323 0
## 4 4 wholetissue S 4 wholetissue S 0.2853209 0
## 7 7 wholetissue S 7 wholetissue S 0.3214936 0
## 8 8 wholetissue S 8 wholetissue S 0.2543348 0
## 47 47 mito S 15 mito S 0.7268125 1
## 64 64 mito S 32 mito S 0.5016151 1de3 <- run_de(ss3,x3)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 5629 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing
## chr [1:18] "9 ENSRNOG00000046834.3 C3" "13 ENSRNOG00000004405.5 Pigr" ...
## chr [1:2] "14 ENSRNOG00000027791.5 AABR07015346.1" ...
head(de3,20)## log2 fold change (MLE): trt
## Wald test p-value: trt
## DataFrame with 20 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## 9 ENSRNOG00000046834.3 C3 189.9471 6.69434 0.804430
## 14 ENSRNOG00000027791.5 AABR07015346.1 154.4810 -23.40818 3.368853
## 13 ENSRNOG00000004405.5 Pigr 33.1470 5.57613 0.834008
## 2 ENSRNOG00000011200.3 Bhmt 23.2130 4.70473 0.753431
## 19 ENSRNOG00000014964.8 Hp 85.9235 3.64868 0.643773
## ... ... ... ...
## 3 ENSRNOG00000010128.7 Slc27a2 6.10714 4.33947 0.979589
## 4 ENSRNOG00000006675.5 Fabp1 52.46798 1.63489 0.373544
## 1 ENSRNOG00000031391.4 Ceacam16 40.66142 -2.34982 0.539710
## 16 ENSRNOG00000033386.5 Itih1 11.06883 8.01467 1.870598
## 2 ENSRNOG00000013736.6 C9 11.30135 3.22151 0.772705
## stat pvalue padj
## <numeric> <numeric> <numeric>
## 9 ENSRNOG00000046834.3 C3 8.32185 8.66016e-17 2.09264e-12
## 14 ENSRNOG00000027791.5 AABR07015346.1 -6.94841 3.69423e-12 4.46337e-08
## 13 ENSRNOG00000004405.5 Pigr 6.68594 2.29440e-11 1.84806e-07
## 2 ENSRNOG00000011200.3 Bhmt 6.24441 4.25413e-10 2.56992e-06
## 19 ENSRNOG00000014964.8 Hp 5.66765 1.44766e-08 6.24722e-05
## ... ... ... ...
## 3 ENSRNOG00000010128.7 Slc27a2 4.42989 9.42824e-06 0.0142390
## 4 ENSRNOG00000006675.5 Fabp1 4.37669 1.20497e-05 0.0171276
## 1 ENSRNOG00000031391.4 Ceacam16 -4.35386 1.33759e-05 0.0179564
## 16 ENSRNOG00000033386.5 Itih1 4.28455 1.83109e-05 0.0232876
## 2 ENSRNOG00000013736.6 C9 4.16913 3.05764e-05 0.0369424as.data.frame(de3[1:20,])## baseMean log2FoldChange lfcSE
## 9 ENSRNOG00000046834.3 C3 189.947150 6.694343 0.8044297
## 14 ENSRNOG00000027791.5 AABR07015346.1 154.480954 -23.408177 3.3688533
## 13 ENSRNOG00000004405.5 Pigr 33.147013 5.576134 0.8340084
## 2 ENSRNOG00000011200.3 Bhmt 23.212978 4.704729 0.7534309
## 19 ENSRNOG00000014964.8 Hp 85.923541 3.648683 0.6437731
## 16 ENSRNOG00000017381.8 Itih4 27.994702 6.468558 1.1437031
## 6 ENSRNOG00000005542.8 Apob 91.699635 9.580368 1.7184669
## 19 ENSRNOG00000015438.5 LOC501233 19.393163 5.876676 1.0579672
## 4 ENSRNOG00000006709.6 Pzp 51.516746 7.538147 1.3639175
## 1 ENSRNOG00000017223.7 Plg 27.573652 6.470047 1.2003796
## 1 ENSRNOG00000046727.2 Abcc2 36.686475 3.034077 0.5814550
## 6 ENSRNOG00000010478.7 LOC299282 196.409234 2.161695 0.4150939
## 2 ENSRNOG00000012436.6 Adh6 12.016400 3.126573 0.6145946
## 1 ENSRNOG00000024016.7 Cyp2c6v1 19.504106 2.998256 0.6171350
## 3 ENSRNOG00000018899.8 C5 6.163963 5.651434 1.2054685
## 3 ENSRNOG00000010128.7 Slc27a2 6.107139 4.339469 0.9795889
## 4 ENSRNOG00000006675.5 Fabp1 52.467979 1.634886 0.3735441
## 1 ENSRNOG00000031391.4 Ceacam16 40.661418 -2.349823 0.5397098
## 16 ENSRNOG00000033386.5 Itih1 11.068833 8.014672 1.8705981
## 2 ENSRNOG00000013736.6 C9 11.301354 3.221508 0.7727049
## stat pvalue padj
## 9 ENSRNOG00000046834.3 C3 8.321849 8.660158e-17 2.092641e-12
## 14 ENSRNOG00000027791.5 AABR07015346.1 -6.948411 3.694234e-12 4.463374e-08
## 13 ENSRNOG00000004405.5 Pigr 6.685944 2.294401e-11 1.848064e-07
## 2 ENSRNOG00000011200.3 Bhmt 6.244406 4.254128e-10 2.569918e-06
## 19 ENSRNOG00000014964.8 Hp 5.667654 1.447659e-08 6.247220e-05
## 16 ENSRNOG00000017381.8 Itih4 5.655802 1.551205e-08 6.247220e-05
## 6 ENSRNOG00000005542.8 Apob 5.574950 2.476003e-08 8.400314e-05
## 19 ENSRNOG00000015438.5 LOC501233 5.554687 2.781100e-08 8.400314e-05
## 4 ENSRNOG00000006709.6 Pzp 5.526835 3.260591e-08 8.754326e-05
## 1 ENSRNOG00000017223.7 Plg 5.390001 7.045743e-08 1.702533e-04
## 1 ENSRNOG00000046727.2 Abcc2 5.218078 1.807892e-07 3.849503e-04
## 6 ENSRNOG00000010478.7 LOC299282 5.207726 1.911688e-07 3.849503e-04
## 2 ENSRNOG00000012436.6 Adh6 5.087212 3.633657e-07 6.754130e-04
## 1 ENSRNOG00000024016.7 Cyp2c6v1 4.858347 1.183697e-06 2.043061e-03
## 3 ENSRNOG00000018899.8 C5 4.688164 2.756674e-06 4.440817e-03
## 3 ENSRNOG00000010128.7 Slc27a2 4.429887 9.428237e-06 1.423899e-02
## 4 ENSRNOG00000006675.5 Fabp1 4.376687 1.204969e-05 1.712756e-02
## 1 ENSRNOG00000031391.4 Ceacam16 -4.353863 1.337593e-05 1.795644e-02
## 16 ENSRNOG00000033386.5 Itih1 4.284550 1.831091e-05 2.328763e-02
## 2 ENSRNOG00000013736.6 C9 4.169131 3.057636e-05 3.694236e-02write.table(de3,file="de3b.tsv",quote=FALSE,sep="\t")
ss4## UDI fraction Group RatID label mtfrac trt
## 1 1 wholetissue T 1 wholetissue T 0.2601233 0
## 10 10 wholetissue T 10 wholetissue T 0.3723191 0
## 13 13 wholetissue T 13 wholetissue T 0.3195613 0
## 14 14 wholetissue T 14 wholetissue T 0.3552110 0
## 17 17 wholetissue T 17 wholetissue T 0.3617978 0
## 18 18 wholetissue T 18 wholetissue T 0.3904784 0
## 2 2 wholetissue T 2 wholetissue T 0.3441832 0
## 21 21 wholetissue T 21 wholetissue T 0.3680570 0
## 22 22 wholetissue T 22 wholetissue T 0.3551245 0
## 25 25 wholetissue T 25 wholetissue T 0.3205449 0
## 26 26 wholetissue T 26 wholetissue T 0.2691352 0
## 27 27 wholetissue T 27 wholetissue T 0.3549592 0
## 28 28 wholetissue T 28 wholetissue T 0.2879719 0
## 5 5 wholetissue T 5 wholetissue T 0.3747933 0
## 6 6 wholetissue T 6 wholetissue T 0.3510836 0
## 9 9 wholetissue T 9 wholetissue T 0.3754423 0
## 33 33 mito T 1 mito T 0.6465534 1
## 34 34 mito T 2 mito T 0.8147126 1
## 58 58 mito T 26 mito T 0.4821588 1
## 59 59 mito T 27 mito T 0.6646957 1
## 60 60 mito T 28 mito T 0.5887804 1de4 <- run_de(ss4,x4)## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 294 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testing
## chr(0)
## chr(0)
head(de4,20)## log2 fold change (MLE): trt
## Wald test p-value: trt
## DataFrame with 20 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## 17 ENSRNOG00000053995.1 AABR07027810.4 8.35074 3.02225 0.685593
## 2 ENSRNOG00000048082.2 AABR07013776.1 8.90530 3.51125 0.830426
## 5 ENSRNOG00000055453.1 AC127935.1 3.82793 2.87295 0.680451
## 3 ENSRNOG00000011715.3 AABR07053669.1 9.92304 2.66588 0.632305
## 2 ENSRNOG00000045639.2 RGD1560883 70.61063 1.18753 0.283671
## ... ... ... ...
## X ENSRNOG00000002413.6 Gpc4 905.96927 1.233723 0.303703
## 4 ENSRNOG00000006726.7 Zfp9 63.47564 1.747441 0.433260
## 1 ENSRNOG00000043141.3 Ap3s2 109.56215 -0.638875 0.158630
## 3 ENSRNOG00000016488.6 Pltp 212.51737 -0.591838 0.147572
## 2 ENSRNOG00000053898.1 AABR07013208.1 8.55656 1.789090 0.449492
## stat pvalue padj
## <numeric> <numeric> <numeric>
## 17 ENSRNOG00000053995.1 AABR07027810.4 4.40823 1.04218e-05 0.0900534
## 2 ENSRNOG00000048082.2 AABR07013776.1 4.22825 2.35516e-05 0.0900534
## 5 ENSRNOG00000055453.1 AC127935.1 4.22213 2.42007e-05 0.0900534
## 3 ENSRNOG00000011715.3 AABR07053669.1 4.21613 2.48533e-05 0.0900534
## 2 ENSRNOG00000045639.2 RGD1560883 4.18629 2.83550e-05 0.0900534
## ... ... ... ...
## X ENSRNOG00000002413.6 Gpc4 4.06227 4.85987e-05 0.0900534
## 4 ENSRNOG00000006726.7 Zfp9 4.03324 5.50127e-05 0.0928726
## 1 ENSRNOG00000043141.3 Ap3s2 -4.02745 5.63852e-05 0.0928726
## 3 ENSRNOG00000016488.6 Pltp -4.01051 6.05884e-05 0.0945435
## 2 ENSRNOG00000053898.1 AABR07013208.1 3.98025 6.88439e-05 0.0969047as.data.frame(de4[1:20,])## baseMean log2FoldChange lfcSE
## 17 ENSRNOG00000053995.1 AABR07027810.4 8.350737 3.0222524 0.68559281
## 2 ENSRNOG00000048082.2 AABR07013776.1 8.905297 3.5112469 0.83042551
## 5 ENSRNOG00000055453.1 AC127935.1 3.827934 2.8729506 0.68045088
## 3 ENSRNOG00000011715.3 AABR07053669.1 9.923043 2.6658784 0.63230489
## 2 ENSRNOG00000045639.2 RGD1560883 70.610633 1.1875298 0.28367109
## 8 ENSRNOG00000023809.5 Opcml 59.018462 1.0449219 0.25067282
## 1 ENSRNOG00000060312.1 LOC108348197 271.784529 -2.4832702 0.59722048
## 1 ENSRNOG00000015717.8 Ptpre 86.690130 0.9615012 0.23166209
## 7 ENSRNOG00000005551.6 Derl1 541.437086 0.2653631 0.06421305
## 5 ENSRNOG00000022953.4 Ccdc163 36.357350 1.6456259 0.39955397
## 3 ENSRNOG00000050251.1 MGC105649 13.324704 2.2234732 0.53985842
## 16 ENSRNOG00000045643.3 AABR07026271.2 5.871765 5.9098751 1.43960720
## 15 ENSRNOG00000047211.1 Fzd3 42.068994 1.2291144 0.30046287
## 9 ENSRNOG00000050660.1 LOC100911713 86.887371 -2.2825034 0.55950762
## 13 ENSRNOG00000058096.1 AABR07021704.1 181.540160 0.6374446 0.15634295
## X ENSRNOG00000002413.6 Gpc4 905.969267 1.2337233 0.30370324
## 4 ENSRNOG00000006726.7 Zfp9 63.475641 1.7474409 0.43325967
## 1 ENSRNOG00000043141.3 Ap3s2 109.562150 -0.6388746 0.15863006
## 3 ENSRNOG00000016488.6 Pltp 212.517369 -0.5918376 0.14757176
## 2 ENSRNOG00000053898.1 AABR07013208.1 8.556559 1.7890899 0.44949227
## stat pvalue padj
## 17 ENSRNOG00000053995.1 AABR07027810.4 4.408232 1.042177e-05 0.09005338
## 2 ENSRNOG00000048082.2 AABR07013776.1 4.228250 2.355158e-05 0.09005338
## 5 ENSRNOG00000055453.1 AC127935.1 4.222128 2.420068e-05 0.09005338
## 3 ENSRNOG00000011715.3 AABR07053669.1 4.216128 2.485326e-05 0.09005338
## 2 ENSRNOG00000045639.2 RGD1560883 4.186291 2.835496e-05 0.09005338
## 8 ENSRNOG00000023809.5 Opcml 4.168469 3.066524e-05 0.09005338
## 1 ENSRNOG00000060312.1 LOC108348197 -4.158046 3.209814e-05 0.09005338
## 1 ENSRNOG00000015717.8 Ptpre 4.150447 3.318265e-05 0.09005338
## 7 ENSRNOG00000005551.6 Derl1 4.132542 3.587733e-05 0.09005338
## 5 ENSRNOG00000022953.4 Ccdc163 4.118657 3.810865e-05 0.09005338
## 3 ENSRNOG00000050251.1 MGC105649 4.118623 3.811434e-05 0.09005338
## 16 ENSRNOG00000045643.3 AABR07026271.2 4.105200 4.039656e-05 0.09005338
## 15 ENSRNOG00000047211.1 Fzd3 4.090736 4.300057e-05 0.09005338
## 9 ENSRNOG00000050660.1 LOC100911713 -4.079486 4.513541e-05 0.09005338
## 13 ENSRNOG00000058096.1 AABR07021704.1 4.077220 4.557739e-05 0.09005338
## X ENSRNOG00000002413.6 Gpc4 4.062266 4.859870e-05 0.09005338
## 4 ENSRNOG00000006726.7 Zfp9 4.033242 5.501268e-05 0.09287265
## 1 ENSRNOG00000043141.3 Ap3s2 -4.027449 5.638517e-05 0.09287265
## 3 ENSRNOG00000016488.6 Pltp -4.010507 6.058844e-05 0.09454348
## 2 ENSRNOG00000053898.1 AABR07013208.1 3.980246 6.884394e-05 0.09690469write.table(de4,file="de4b.tsv",quote=FALSE,sep="\t")Session info
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## 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] gplots_3.1.0 mitch_1.2.2
## [3] DESeq2_1.30.0 SummarizedExperiment_1.20.0
## [5] Biobase_2.50.0 MatrixGenerics_1.2.0
## [7] matrixStats_0.57.0 GenomicRanges_1.42.0
## [9] GenomeInfoDb_1.26.0 IRanges_2.24.0
## [11] S4Vectors_0.28.0 BiocGenerics_0.36.0
## [13] reshape2_1.4.4
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.2 bit64_4.0.5 splines_4.0.3
## [4] gtools_3.8.2 shiny_1.5.0 blob_1.2.1
## [7] GenomeInfoDbData_1.2.4 yaml_2.2.1 pillar_1.4.6
## [10] RSQLite_2.2.1 lattice_0.20-41 glue_1.4.2
## [13] digest_0.6.27 RColorBrewer_1.1-2 promises_1.1.1
## [16] XVector_0.30.0 colorspace_2.0-0 htmltools_0.5.0
## [19] httpuv_1.5.4 Matrix_1.2-18 plyr_1.8.6
## [22] XML_3.99-0.5 pkgconfig_2.0.3 genefilter_1.72.0
## [25] zlibbioc_1.36.0 purrr_0.3.4 xtable_1.8-4
## [28] scales_1.1.1 later_1.1.0.1 BiocParallel_1.24.1
## [31] tibble_3.0.4 annotate_1.68.0 echarts4r_0.3.3
## [34] generics_0.1.0 ggplot2_3.3.2 ellipsis_0.3.1
## [37] survival_3.2-7 magrittr_1.5 crayon_1.3.4
## [40] mime_0.9 evaluate_0.14 memoise_1.1.0
## [43] GGally_2.0.0 MASS_7.3-53 beeswarm_0.2.3
## [46] tools_4.0.3 lifecycle_0.2.0 stringr_1.4.0
## [49] munsell_0.5.0 locfit_1.5-9.4 DelayedArray_0.16.0
## [52] AnnotationDbi_1.52.0 compiler_4.0.3 caTools_1.18.0
## [55] rlang_0.4.8 grid_4.0.3 RCurl_1.98-1.2
## [58] htmlwidgets_1.5.2 rmarkdown_2.5 bitops_1.0-6
## [61] gtable_0.3.0 DBI_1.1.0 reshape_0.8.8
## [64] R6_2.5.0 gridExtra_2.3 knitr_1.30
## [67] dplyr_1.0.2 fastmap_1.0.1 bit_4.0.4
## [70] KernSmooth_2.23-18 stringi_1.5.3 Rcpp_1.0.5
## [73] vctrs_0.3.4 geneplotter_1.68.0 tidyselect_1.1.0
## [76] xfun_0.19