Class 17

Karolina Navarro (PID:A191906745)

Downstream Analysis

library(tximport)

# setup the folder and filenames to read
folders <- c(
  "SRR2156848_quant",
  "SRR2156849_quant",
  "SRR2156850_quant",
  "SRR2156851_quant"
)


samples <- sub("_quant", "", folders)
files <- file.path(folders, "abundance.h5")
names(files) <- samples

library(rhdf5)

txi.kallisto <- tximport(files, type = "kallisto", txOut = TRUE)

1 2 3 4 

head(txi.kallisto$counts)

                SRR2156848 SRR2156849 SRR2156850 SRR2156851
ENST00000539570          0          0    0.00000          0
ENST00000576455          0          0    2.62037          0
ENST00000510508          0          0    0.00000          0
ENST00000474471          0          1    1.00000          0
ENST00000381700          0          0    0.00000          0
ENST00000445946          0          0    0.00000          0

colSums(txi.kallisto$counts)

SRR2156848 SRR2156849 SRR2156850 SRR2156851 
   2563611    2600800    2372309    2111474 

sum(rowSums(txi.kallisto$counts)>0)

[1] 94561

to.keep <- rowSums(txi.kallisto$counts) > 0
kset.nonzero <- txi.kallisto$counts[to.keep,]

keep2 <- apply(kset.nonzero,1,sd)>0
x <- kset.nonzero[keep2,]

Principal Component Analysis

pca <- prcomp(t(x), scale=TRUE)

summary(pca)

Importance of components:
                            PC1      PC2      PC3   PC4
Standard deviation     183.6379 177.3605 171.3020 1e+00
Proportion of Variance   0.3568   0.3328   0.3104 1e-05
Cumulative Proportion    0.3568   0.6895   1.0000 1e+00

plot(pca$x[,1], pca$x[,2],
     col=c("blue","blue","red","red"),
     xlab="PC1", ylab="PC2", pch=16)

plot(pca$x[,1], pca$x[,3],
     col=c("blue","blue","red","red"),
     xlab="PC1", ylab="PC3", pch=16)

plot(pca$x[,2], pca$x[,3],
     col=c("blue","blue","red","red"),
     xlab="PC2", ylab="PC3", pch=16)

library(ggplot2)

library(ggrepel)

# Make metadata object for the samples
colData <- data.frame(condition = factor(rep(c("control", "treatment"), each = 2)))
rownames(colData) <- colnames(txi.kallisto$counts)

# Make the data.frame for ggplot 
y <- as.data.frame(pca$x)
y$Condition <- as.factor(colData$condition)

ggplot(y) +
  aes(PC1, PC2, col=Condition) +
  geom_point() +
  geom_text_repel(label=rownames(y)) +
  theme_bw()