Last updated: 2022-10-11
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Knit directory: DEPDC5_D62_Analysis/
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home = getwd()
filetarget= paste0(home,"/data/Countmatrix.RData")
load(filetarget)
Ntot= nrow(Countdata)
#merge non unique annotations
if(length(unique(rownames(Countdata))) != Ntot){
Countdata = Countdata %>% group_by(row.names(Countdata)) %>% summarise_each(sum)
Ntot= nrow(Countdata)
}
hgnc=gconvert(query=as.numeric(rownames(Countdata)),
organism = "hsapiens",
numeric_ns = "ENTREZGENE_ACC",
target = "HGNC")
Ids = hgnc %>% dplyr::select(name, input, description) %>% group_by(input) %>%
summarise(name=paste(name, sep="; ", collapse = ";"), description = dplyr::first(description))
rowdescription = data.frame(entrez_gene = Ids$input,
hgnc=Ids$name,
description=Ids$description)
rowdescription = rowdescription[match(row.names(Countdata), rowdescription$entrez_gene),]
rownames(rowdescription)=row.names(Countdata)
# load and parse sample information
SampleInfo=read.csv2(paste0(home,"/data/D62_Sample_info_CePTER_RNASeq.csv"),
row.names = 1)
SampleInfo$Row=gsub("[0-9]*","",SampleInfo$Position)
SampleInfo$Col=as.numeric(gsub("[A-Z]*","",SampleInfo$Position))
# set factors and relevel
SampleInfo$CellLine = as.factor(SampleInfo$CellLine)
SampleInfo$gRNA = paste0("sg",SampleInfo$gRNA)
SampleInfo$gRNA = factor(SampleInfo$gRNA, levels=c("sgNTC", "sg2.1", "sg2.2"),
labels=c("sgNTC", "sg2.1", "sg2.2"))
SampleInfo$gRNA = relevel(SampleInfo$gRNA,ref="sgNTC" )
SampleInfo$KO = factor(SampleInfo$KO, levels=c(T,F), labels=c("KO", "WT"))
SampleInfo$KO = relevel(SampleInfo$KO,ref="WT" )
SampleInfo$DIFF = factor(SampleInfo$DIFF, levels=c(TRUE,FALSE),
labels=c("DIFF", "noDIFF"))
SampleInfo$DIFF = relevel(SampleInfo$DIFF,ref="noDIFF")
SampleInfo$RAPA = factor(SampleInfo$RAPA, levels=c(T,F),
labels=c("RAPA", "noRAPA"))
SampleInfo$RAPA = relevel(SampleInfo$RAPA,ref="noRAPA")
SampleInfo$label = with(SampleInfo, paste(CellLine,gRNA,DIFF,RAPA, sep="_"))
SampleInfo$fastQID = rownames(SampleInfo)
SampleInfo = SampleInfo %>% dplyr::group_by(label) %>% mutate(replicate=seq(n())) %>% as.data.frame()
SampleInfo$label_rep=with(SampleInfo, paste(label,replicate,sep="_"))
rownames(SampleInfo)=SampleInfo$fastQID
# align datasets
checkfiles = all(rownames(SampleInfo) %in% colnames(Countdata))
IDs=intersect(rownames(SampleInfo), colnames(Countdata))
Countdata = Countdata[,IDs]
SampleInfo = SampleInfo[IDs, ]
SampleInfo$reads_per_sample = colSums(Countdata)
display_tab(head(Countdata))
DE10NGSUKBR112901 | DE80NGSUKBR112902 | DE53NGSUKBR112903 | DE26NGSUKBR112904 | DE96NGSUKBR112905 | DE69NGSUKBR112906 | DE42NGSUKBR112907 | DE15NGSUKBR112908 | DE85NGSUKBR112909 | DE58NGSUKBR112910 | DE31NGSUKBR112911 | DE04NGSUKBR112912 | DE74NGSUKBR112913 | DE47NGSUKBR112914 | DE20NGSUKBR112915 | DE90NGSUKBR112916 | DE63NGSUKBR112917 | DE36NGSUKBR112918 | DE09NGSUKBR112919 | DE79NGSUKBR112920 | DE52NGSUKBR112921 | DE25NGSUKBR112922 | DE95NGSUKBR112923 | DE68NGSUKBR112924 | DE41NGSUKBR112925 | DE14NGSUKBR112926 | DE84NGSUKBR112927 | DE57NGSUKBR112928 | DE30NGSUKBR112929 | DE03NGSUKBR112930 | DE73NGSUKBR112931 | DE46NGSUKBR112932 | DE19NGSUKBR112933 | DE89NGSUKBR112934 | DE62NGSUKBR112935 | DE35NGSUKBR112936 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
100287102 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
653635 | 46 | 82 | 133 | 121 | 84 | 78 | 31 | 21 | 32 | 48 | 26 | 47 | 59 | 18 | 113 | 66 | 71 | 99 | 101 | 59 | 55 | 75 | 77 | 42 | 27 | 32 | 41 | 85 | 90 | 112 | 75 | 0 | 32 | 25 | 42 | 19 |
102466751 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
100302278 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
645520 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
79501 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
display_tab(SampleInfo)
Plate | Position | Row | Col | CellLine | gRNA | KO | DIFF | RAPA | Conc | UV260_280 | UV260_230 | label | fastQID | replicate | label_rep | reads_per_sample | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DE10NGSUKBR112901 | 1 | A01 | A | 1 | D62 | sgNTC | WT | DIFF | RAPA | 4 | 2.5 | 0.588 | D62_sgNTC_DIFF_RAPA | DE10NGSUKBR112901 | 1 | D62_sgNTC_DIFF_RAPA_1 | 8167558 |
DE80NGSUKBR112902 | 1 | A02 | A | 2 | D62 | sgNTC | WT | DIFF | RAPA | 3.6 | 1.8 | 0.529 | D62_sgNTC_DIFF_RAPA | DE80NGSUKBR112902 | 2 | D62_sgNTC_DIFF_RAPA_2 | 7947513 |
DE53NGSUKBR112903 | 1 | A03 | A | 3 | D62 | sgNTC | WT | DIFF | RAPA | 4 | 2 | 0.714 | D62_sgNTC_DIFF_RAPA | DE53NGSUKBR112903 | 3 | D62_sgNTC_DIFF_RAPA_3 | 8927353 |
DE26NGSUKBR112904 | 1 | A04 | A | 4 | D62 | sgNTC | WT | DIFF | noRAPA | 14.8 | 1.682 | 1.276 | D62_sgNTC_DIFF_noRAPA | DE26NGSUKBR112904 | 1 | D62_sgNTC_DIFF_noRAPA_1 | 6192682 |
DE96NGSUKBR112905 | 1 | A05 | A | 5 | D62 | sgNTC | WT | DIFF | noRAPA | 10.4 | 2.167 | 1.529 | D62_sgNTC_DIFF_noRAPA | DE96NGSUKBR112905 | 2 | D62_sgNTC_DIFF_noRAPA_2 | 6316070 |
DE69NGSUKBR112906 | 1 | A06 | A | 6 | D62 | sgNTC | WT | DIFF | noRAPA | 8 | 2.22 | 1.33 | D62_sgNTC_DIFF_noRAPA | DE69NGSUKBR112906 | 3 | D62_sgNTC_DIFF_noRAPA_3 | 7211176 |
DE42NGSUKBR112907 | 1 | A07 | A | 7 | D62 | sg2.1 | KO | DIFF | RAPA | 4 | 2.5 | 1.25 | D62_sg2.1_DIFF_RAPA | DE42NGSUKBR112907 | 1 | D62_sg2.1_DIFF_RAPA_1 | 6472088 |
DE15NGSUKBR112908 | 1 | A08 | A | 8 | D62 | sg2.1 | KO | DIFF | RAPA | 5.2 | 1.857 | 1.444 | D62_sg2.1_DIFF_RAPA | DE15NGSUKBR112908 | 2 | D62_sg2.1_DIFF_RAPA_2 | 6381728 |
DE85NGSUKBR112909 | 1 | A09 | A | 9 | D62 | sg2.1 | KO | DIFF | RAPA | 5.6 | 2 | 1.556 | D62_sg2.1_DIFF_RAPA | DE85NGSUKBR112909 | 3 | D62_sg2.1_DIFF_RAPA_3 | 7515594 |
DE58NGSUKBR112910 | 1 | A10 | A | 10 | D62 | sg2.1 | KO | DIFF | noRAPA | 12.4 | 1.938 | 1.722 | D62_sg2.1_DIFF_noRAPA | DE58NGSUKBR112910 | 1 | D62_sg2.1_DIFF_noRAPA_1 | 8072060 |
DE31NGSUKBR112911 | 1 | A11 | A | 11 | D62 | sg2.1 | KO | DIFF | noRAPA | 13.6 | 2 | 0.895 | D62_sg2.1_DIFF_noRAPA | DE31NGSUKBR112911 | 2 | D62_sg2.1_DIFF_noRAPA_2 | 9132042 |
DE04NGSUKBR112912 | 1 | A12 | A | 12 | D62 | sg2.1 | KO | DIFF | noRAPA | 8.4 | 1.909 | 0.244 | D62_sg2.1_DIFF_noRAPA | DE04NGSUKBR112912 | 3 | D62_sg2.1_DIFF_noRAPA_3 | 9158749 |
DE74NGSUKBR112913 | 1 | B01 | B | 1 | D62 | sg2.2 | KO | DIFF | RAPA | 4.4 | 1.833 | 1.571 | D62_sg2.2_DIFF_RAPA | DE74NGSUKBR112913 | 1 | D62_sg2.2_DIFF_RAPA_1 | 8022580 |
DE47NGSUKBR112914 | 1 | B02 | B | 2 | D62 | sg2.2 | KO | DIFF | RAPA | 4.4 | 1.833 | 0.5 | D62_sg2.2_DIFF_RAPA | DE47NGSUKBR112914 | 2 | D62_sg2.2_DIFF_RAPA_2 | 5290014 |
DE20NGSUKBR112915 | 1 | B03 | B | 3 | D62 | sg2.2 | KO | DIFF | RAPA | 6 | 1.875 | 0.172 | D62_sg2.2_DIFF_RAPA | DE20NGSUKBR112915 | 3 | D62_sg2.2_DIFF_RAPA_3 | 7350899 |
DE90NGSUKBR112916 | 1 | B04 | B | 4 | D62 | sg2.2 | KO | DIFF | noRAPA | 5.2 | 1.857 | 0.334 | D62_sg2.2_DIFF_noRAPA | DE90NGSUKBR112916 | 1 | D62_sg2.2_DIFF_noRAPA_1 | 7142950 |
DE63NGSUKBR112917 | 1 | B05 | B | 5 | D62 | sg2.2 | KO | DIFF | noRAPA | 6 | 1.667 | 0.789 | D62_sg2.2_DIFF_noRAPA | DE63NGSUKBR112917 | 2 | D62_sg2.2_DIFF_noRAPA_2 | 6379496 |
DE36NGSUKBR112918 | 1 | B06 | B | 6 | D62 | sg2.2 | KO | DIFF | noRAPA | 4 | 1.667 | 1.25 | D62_sg2.2_DIFF_noRAPA | DE36NGSUKBR112918 | 3 | D62_sg2.2_DIFF_noRAPA_3 | 6345821 |
DE09NGSUKBR112919 | 1 | B07 | B | 7 | D62 | sgNTC | WT | noDIFF | RAPA | 22 | 2.037 | 1.25 | D62_sgNTC_noDIFF_RAPA | DE09NGSUKBR112919 | 1 | D62_sgNTC_noDIFF_RAPA_1 | 6954777 |
DE79NGSUKBR112920 | 1 | B08 | B | 8 | D62 | sgNTC | WT | noDIFF | RAPA | 14.8 | 2.176 | 0.698 | D62_sgNTC_noDIFF_RAPA | DE79NGSUKBR112920 | 2 | D62_sgNTC_noDIFF_RAPA_2 | 6247879 |
DE52NGSUKBR112921 | 1 | B09 | B | 9 | D62 | sgNTC | WT | noDIFF | RAPA | 19.2 | 2.087 | 1.371 | D62_sgNTC_noDIFF_RAPA | DE52NGSUKBR112921 | 3 | D62_sgNTC_noDIFF_RAPA_3 | 7651123 |
DE25NGSUKBR112922 | 1 | B10 | B | 10 | D62 | sgNTC | WT | noDIFF | noRAPA | 16.8 | 1.909 | 0.525 | D62_sgNTC_noDIFF_noRAPA | DE25NGSUKBR112922 | 1 | D62_sgNTC_noDIFF_noRAPA_1 | 8143934 |
DE95NGSUKBR112923 | 1 | B11 | B | 11 | D62 | sgNTC | WT | noDIFF | noRAPA | 18.8 | 1.958 | 1.343 | D62_sgNTC_noDIFF_noRAPA | DE95NGSUKBR112923 | 2 | D62_sgNTC_noDIFF_noRAPA_2 | 7710977 |
DE68NGSUKBR112924 | 1 | B12 | B | 12 | D62 | sgNTC | WT | noDIFF | noRAPA | 17.6 | 2 | 1.189 | D62_sgNTC_noDIFF_noRAPA | DE68NGSUKBR112924 | 3 | D62_sgNTC_noDIFF_noRAPA_3 | 9158766 |
DE41NGSUKBR112925 | 1 | C01 | C | 1 | D62 | sg2.1 | KO | noDIFF | RAPA | 26.8 | 2.03 | 1.914 | D62_sg2.1_noDIFF_RAPA | DE41NGSUKBR112925 | 1 | D62_sg2.1_noDIFF_RAPA_1 | 7883862 |
DE14NGSUKBR112926 | 1 | C02 | C | 2 | D62 | sg2.1 | KO | noDIFF | RAPA | 24.8 | 2.067 | 1.59 | D62_sg2.1_noDIFF_RAPA | DE14NGSUKBR112926 | 2 | D62_sg2.1_noDIFF_RAPA_2 | 7134773 |
DE84NGSUKBR112927 | 1 | C03 | C | 3 | D62 | sg2.1 | KO | noDIFF | RAPA | 20.8 | 2.167 | 1.268 | D62_sg2.1_noDIFF_RAPA | DE84NGSUKBR112927 | 3 | D62_sg2.1_noDIFF_RAPA_3 | 8617208 |
DE57NGSUKBR112928 | 1 | C04 | C | 4 | D62 | sg2.1 | KO | noDIFF | noRAPA | 14.4 | 2.571 | 0.184 | D62_sg2.1_noDIFF_noRAPA | DE57NGSUKBR112928 | 1 | D62_sg2.1_noDIFF_noRAPA_1 | 7544453 |
DE30NGSUKBR112929 | 1 | C05 | C | 5 | D62 | sg2.1 | KO | noDIFF | noRAPA | 16.4 | 2.158 | 0.911 | D62_sg2.1_noDIFF_noRAPA | DE30NGSUKBR112929 | 2 | D62_sg2.1_noDIFF_noRAPA_2 | 7622380 |
DE03NGSUKBR112930 | 1 | C06 | C | 6 | D62 | sg2.1 | KO | noDIFF | noRAPA | 12.8 | 2.286 | 0.711 | D62_sg2.1_noDIFF_noRAPA | DE03NGSUKBR112930 | 3 | D62_sg2.1_noDIFF_noRAPA_3 | 7939374 |
DE73NGSUKBR112931 | 1 | C07 | C | 7 | D62 | sg2.2 | KO | noDIFF | RAPA | 19.6 | 1.96 | 1.69 | D62_sg2.2_noDIFF_RAPA | DE73NGSUKBR112931 | 1 | D62_sg2.2_noDIFF_RAPA_1 | 7320431 |
DE46NGSUKBR112932 | 1 | C08 | C | 8 | D62 | sg2.2 | KO | noDIFF | RAPA | 18 | 2.045 | 1.607 | D62_sg2.2_noDIFF_RAPA | DE46NGSUKBR112932 | 2 | D62_sg2.2_noDIFF_RAPA_2 | 6532522 |
DE19NGSUKBR112933 | 1 | C09 | C | 9 | D62 | sg2.2 | KO | noDIFF | RAPA | 17.6 | 2 | 1.63 | D62_sg2.2_noDIFF_RAPA | DE19NGSUKBR112933 | 3 | D62_sg2.2_noDIFF_RAPA_3 | 7115292 |
DE89NGSUKBR112934 | 1 | C10 | C | 10 | D62 | sg2.2 | KO | noDIFF | noRAPA | 15.2 | 2.111 | 1.583 | D62_sg2.2_noDIFF_noRAPA | DE89NGSUKBR112934 | 1 | D62_sg2.2_noDIFF_noRAPA_1 | 7618310 |
DE62NGSUKBR112935 | 1 | C11 | C | 11 | D62 | sg2.2 | KO | noDIFF | noRAPA | 14.4 | 2.25 | 1.091 | D62_sg2.2_noDIFF_noRAPA | DE62NGSUKBR112935 | 2 | D62_sg2.2_noDIFF_noRAPA_2 | 7387684 |
DE35NGSUKBR112936 | 1 | C12 | C | 12 | D62 | sg2.2 | KO | noDIFF | noRAPA | 15.2 | 2.111 | 0.717 | D62_sg2.2_noDIFF_noRAPA | DE35NGSUKBR112936 | 3 | D62_sg2.2_noDIFF_noRAPA_3 | 7440730 |
Total number of samples overlapping between Counts and SampleInfo: 36
boxplot_counts = function(plotsubset,maintitle,colorcode){
vals=log2(plotsubset+1)
a =boxplot(vals, main = maintitle,
col = Dark8[as.factor(SampleInfo[,colorcode])], names=NA,
ylab = "log2 transformed", xlab="samples", xaxt="n")
legend(ncol(vals)*1.1, max(vals), legend = levels(SampleInfo[,colorcode]),
bg="white",xpd=T,box.col = "white",
pch = 16, col = Dark8[1:length(unique(SampleInfo[,colorcode]))])
}
barplot_counts = function(DF, maintitle, colorcode) {
vals=log2(DF[,"reads_per_sample"])
barplot(vals, main = maintitle,
col = Dark8[as.factor(DF[,colorcode])], names=NA, xaxt="n",
ylab = "log2 transformed", xlab="samples")
legend(length(vals)*1.25, max(vals), legend = levels(DF[,colorcode]), pch = 16,
bg ="white",xpd=T, box.col="white",
col = Dark8[1:length(unique(DF[,colorcode]))])
}
par(mar=c(3,5,5,7))
boxplot_counts(Countdata, "raw counts", "gRNA")
barplot_counts(SampleInfo, "total reads", "gRNA")
plot(density(log2(rowMeans(Countdata))), main="distribution of gene expression",
xlab="mean log2(counts +1)")
# remove genes wich were not detected in at least 50% of the samples
keeperidx = rowSums(Countdata>1)>nrow(SampleInfo)/2
Countdata_cl = Countdata[keeperidx, ]
rowdescription = rowdescription[row.names(Countdata_cl),]
fullmodel = as.formula("~gRNA+DIFF+RAPA")
ddsMat <- DESeqDataSetFromMatrix(countData = Countdata_cl,
colData = SampleInfo,
rowData = rowdescription,
design = fullmodel)
ddsMat = estimateSizeFactors(ddsMat)
ddsMat = estimateDispersions(ddsMat)
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
reads = as.data.frame(counts(ddsMat, normalized=T))
SDs = apply(reads, 1, sd)
keepvar = SDs>0
ddsMat <- ddsMat[keepvar,]
Nfilt = length(ddsMat)
reads = as.data.frame(counts(ddsMat, normalized=T))
SampleInfo$reads_per_sample_cl= colSums(reads)
hierarchical clustering based on the top 2000 genes by variance
log2_cpm = log2(reads+1)
varsset=apply(log2_cpm, 1, var)
cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:2000],])
rownames(cpm.sel.trans)=SampleInfo$label_rep
distance = dist(cpm.sel.trans)
hc = hclust(distance, method="ward.D2")
cutN=12 #number of different conditions (DIFF, RAPA, sgRNA)
clusters = cutree(hc, k=cutN)
Colors=sample(jetcolors(cutN))[clusters]
myLetters <- LETTERS[1:26]
numRow=match(SampleInfo$Row, myLetters)
numRow=numRow+(SampleInfo$Plate-1)*8
addRow=LETTERS[numRow]
Plotdata=data.frame(Rows=addRow, numRow = numRow, Cols = SampleInfo$Col,
Group=clusters, Colors=Colors)
par(mar=c(15,3,5,3))
plot(as.dendrogram(hc), main=paste("Similairtiy by gene expression, guessed",cutN,"clusters"), cex=0.7)
colored_dots(colors = Colors, dend = as.dendrogram(hc), rowLabels = "cluster")
Similarity based on hcluster plot
par(mar=c(2,5,8,3))
plot(0,0, type="n", ylab="", xlab="",
ylim=rev(range(Plotdata$numRow))+c(1,-1),
xlim=range(Plotdata$Cols)+c(-1,1), xaxt="n",yaxt="n" ,
main="plate similarity plot")
points(y=Plotdata$numRow, x=Plotdata$Cols, pch=16, cex=4, col=Plotdata$Colors)
text(y=Plotdata$numRow, x=Plotdata$Cols, labels = Plotdata$Group)
text(y=Plotdata$numRow, x=Plotdata$Cols, labels = Plotdata$Group)
axis(2, at=1:9, labels = c(paste0("P1_", LETTERS[1:8]), "P2_A"), las=1)
axis(3, at=1:12, labels = c(paste0("Col_", 1:12)), las=3)
abline(h=8.5)
sampleDistMatrix <- as.matrix(distance)
#colors for plotting heatmap
colors <- colorRampPalette(brewer.pal(9, "Spectral"))(255)
cellcol = Dark8[1:nlevels(SampleInfo$CellLine)]
names(cellcol) = levels(SampleInfo$CellLine)
gRNAcol = Dark8[c(1:nlevels(SampleInfo$gRNA))+nlevels(SampleInfo$CellLine)]
names(gRNAcol) = levels(SampleInfo$gRNA)
diffcol = brewer.pal(3,"Set1")[1:nlevels(SampleInfo$DIFF)]
names(diffcol) = levels(SampleInfo$DIFF)
rapacol = brewer.pal(3,"Set2")[1:nlevels(SampleInfo$RAPA)]
names(rapacol) = levels(SampleInfo$RAPA)
ann_colors = list(
DIFF = diffcol,
RAPA = rapacol,
gRNA = gRNAcol
#,CellLine=cellcol
)
labels = SampleInfo[,c("gRNA","DIFF", "RAPA")] %>%
mutate_all(as.character) %>% as.data.frame()
rownames(labels)=SampleInfo$label_rep
pheatmap(sampleDistMatrix,
clustering_distance_rows = distance,
clustering_distance_cols = distance,
clustering_method = "ward.D2",
scale ="row",
border_color = NA,
annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
main = "D62 Distances normalized log2 counts")
# PCA
gpca <- glmpca(t(cpm.sel.trans), L = 2)
gpca.dat <- gpca$factors
gpca.dat$CellLine <- SampleInfo$CellLine
gpca.dat$gRNA <- SampleInfo$gRNA
gpca.dat$KO<- SampleInfo$KO
gpca.dat$DIFF <- SampleInfo$DIFF
gpca.dat$RAPA<- SampleInfo$RAPA
gpca.dat$Growth_cond = paste(SampleInfo$DIFF, SampleInfo$RAPA, sep="_")
rownames(gpca.dat) = SampleInfo$labels
mds = as.data.frame(SampleInfo) %>% cbind(cmdscale(distance))
mds$Growth_cond = paste(SampleInfo$DIFF, SampleInfo$RAPA, sep="_")
save(mds, gpca.dat, file=paste0(home, "/analysis/MDSplots/D62_mdsplots.RData"))
ggplot(gpca.dat, aes(x = dim1, y = dim2, color = gRNA,
shape = Growth_cond)) +
geom_point(size = 2) + ggtitle("PCA with log2 counts D62")
ggplot(mds, aes(x = `1`, y = `2`, color = gRNA, shape = Growth_cond)) +
geom_point(size = 2) + ggtitle("MDS with log2 counts D62")
save(ddsMat, file=paste0(output,"/D62_dds_matrix.RData"))
sessionInfo()
R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8
[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
[5] LC_TIME=German_Germany.utf8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gprofiler2_0.2.1 dendextend_1.16.0
[3] glmpca_0.2.0 RCurl_1.98-1.8
[5] knitr_1.40 DESeq2_1.36.0
[7] SummarizedExperiment_1.26.1 Biobase_2.56.0
[9] MatrixGenerics_1.8.1 matrixStats_0.62.0
[11] GenomicRanges_1.48.0 GenomeInfoDb_1.32.4
[13] IRanges_2.30.1 S4Vectors_0.34.0
[15] BiocGenerics_0.42.0 pheatmap_1.0.12
[17] RColorBrewer_1.1-3 compareGroups_4.5.1
[19] forcats_0.5.2 stringr_1.4.1
[21] dplyr_1.0.10 purrr_0.3.4
[23] readr_2.1.2 tidyr_1.2.1
[25] tibble_3.1.8 ggplot2_3.3.6
[27] tidyverse_1.3.2 kableExtra_1.3.4
[29] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.4.1 uuid_1.1-0 backports_1.4.1
[4] systemfonts_1.0.4 lazyeval_0.2.2 splines_4.2.0
[7] BiocParallel_1.30.3 digest_0.6.29 htmltools_0.5.3
[10] viridis_0.6.2 fansi_1.0.3 magrittr_2.0.3
[13] Rsolnp_1.16 memoise_2.0.1 googlesheets4_1.0.1
[16] tzdb_0.3.0 Biostrings_2.64.1 annotate_1.74.0
[19] modelr_0.1.9 officer_0.4.4 svglite_2.1.0
[22] colorspace_2.0-3 blob_1.2.3 rvest_1.0.3
[25] haven_2.5.1 xfun_0.33 callr_3.7.2
[28] crayon_1.5.1 jsonlite_1.8.0 genefilter_1.78.0
[31] survival_3.4-0 glue_1.6.2 gtable_0.3.1
[34] gargle_1.2.1 zlibbioc_1.42.0 XVector_0.36.0
[37] webshot_0.5.3 DelayedArray_0.22.0 scales_1.2.1
[40] DBI_1.1.3 Rcpp_1.0.9 viridisLite_0.4.1
[43] xtable_1.8-4 bit_4.0.4 truncnorm_1.0-8
[46] htmlwidgets_1.5.4 httr_1.4.4 ellipsis_0.3.2
[49] mice_3.14.0 farver_2.1.1 pkgconfig_2.0.3
[52] XML_3.99-0.10 nnet_7.3-17 sass_0.4.2
[55] dbplyr_2.2.1 locfit_1.5-9.6 utf8_1.2.2
[58] labeling_0.4.2 tidyselect_1.1.2 rlang_1.0.6
[61] later_1.3.0 AnnotationDbi_1.58.0 munsell_0.5.0
[64] cellranger_1.1.0 tools_4.2.0 cachem_1.0.6
[67] cli_3.4.1 generics_0.1.3 RSQLite_2.2.17
[70] broom_1.0.1 evaluate_0.16 fastmap_1.1.0
[73] yaml_2.3.5 processx_3.7.0 bit64_4.0.5
[76] fs_1.5.2 zip_2.2.1 KEGGREST_1.36.3
[79] whisker_0.4 xml2_1.3.3 compiler_4.2.0
[82] rstudioapi_0.14 plotly_4.10.0 png_0.1-7
[85] reprex_2.0.2 geneplotter_1.74.0 bslib_0.4.0
[88] stringi_1.7.8 HardyWeinberg_1.7.5 highr_0.9
[91] ps_1.7.1 gdtools_0.2.4 lattice_0.20-45
[94] Matrix_1.5-1 vctrs_0.4.1 pillar_1.8.1
[97] lifecycle_1.0.2 BiocManager_1.30.18 jquerylib_0.1.4
[100] data.table_1.14.2 bitops_1.0-7 flextable_0.8.1
[103] httpuv_1.6.6 R6_2.5.1 promises_1.2.0.1
[106] gridExtra_2.3 writexl_1.4.0 codetools_0.2-18
[109] MASS_7.3-58.1 assertthat_0.2.1 chron_2.3-57
[112] rprojroot_2.0.3 withr_2.5.0 GenomeInfoDbData_1.2.8
[115] parallel_4.2.0 hms_1.1.2 grid_4.2.0
[118] rmarkdown_2.16 googledrive_2.0.0 git2r_0.30.1
[121] getPass_0.2-2 lubridate_1.8.0 base64enc_0.1-3