Class 17: AWS Analysis

Aadhya Tripathi (PID: A17878439)

• Downstream Analysis of Kallisto Output
• Principal Component Analysis

Downstream Analysis of Kallisto Output

library(tximport)

# setup the folder and file-names to read
folders <- dir(pattern="SRR21568*")
samples <- sub("_quant", "", folders)
files <- file.path( folders, "abundance.h5" )
names(files) <- samples

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

Number of transcripts per sample:

colSums(txi.kallisto$counts)

SRR2156848 SRR2156849 SRR2156850 SRR2156851 
   2563611    2600800    2372309    2111474 

Number of transcripts detected in at least one sample:

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

[1] 94561

Filter out annotated transcripts with no reads:

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

Filter out transcripts with no change over the samples:

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

Principal Component Analysis

Run PCA, scaled based on transcipt levels:

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 of first 2 principal components:

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

Q. Use ggplot to make a similar figure of PC1 vs PC2 and a separate figure PC1 vs PC3 and PC2 vs PC3

library(ggplot2)
library(ggrepel)

mycols <- c("blue","blue","red","red")

ggplot(pca$x) +
  aes(PC1, PC2, label=rownames(pca$x)) +
  geom_point( col=mycols ) +
  geom_text_repel( col=mycols ) +
  theme_bw() +
  labs(title = "PC1 vs PC2")

ggplot(pca$x) +
  aes(PC1, PC3, label=rownames(pca$x)) +
  geom_point( col=mycols ) +
  geom_text_repel( col=mycols ) +
  theme_bw() +
  labs(title = "PC1 vs PC3")

ggplot(pca$x) +
  aes(PC2, PC3, label=rownames(pca$x)) +
  geom_point( col=mycols ) +
  geom_text_repel( col=mycols ) +
  theme_bw() +
  labs(title = "PC2 vs PC3")