Differential isoform usage netween total and nuclear fractions

Final data preparation:

Create the differential sample file. It will have the names of the samples in column 1 and the fraction they belong to in column two.

isoform_data=read.table("../data/leafcutter/all_leaf_200wind.csv")

samples=colnames(isoform_data)

fraction=c()
for (i in samples){
  if(grepl("N", i)){
    fraction=c(fraction, "Nuclear")
  }
  else{
    fraction=c(fraction, "Total")
  }
}


sample_anno=cbind(samples,fraction)

I will write this to the leafcutter directory without the header.

#write.table(sample_anno, file="../data/leafcutter/sample_ano.txt", row.names = FALSE, quote = FALSE, sep=" ", col.names = F)

Leafcutter results

Confirm we only have 2188 genes with APA here.

genes.anno=data.frame(x=rownames(isoform_data)) %>%  separate(col=x, into=c("chr","bin","gene"), sep=":")
n_genes= n_distinct(genes.anno$gene) 
num_gene=genes.anno %>% group_by(gene) %>% select(gene) %>% tally() %>% filter(n>1)

Warning: package 'bindrcpp' was built under R version 3.4.4

dim(num_gene)

[1] 2188    2

We have 3797 unique genes in this file and only 2188 have multiple bins passing the filter.

I ran leafcutter on the cluster with the following command.

Rscript /project2/gilad/briana/leafcutter/scripts/leafcutter_ds.R all_apa_perind.csv.gz sample_ano.txt -o APA

The resutls for significant bins are in the effet size file.

effect_size=read.table("../data/leafcutter/APA_effect_sizes.txt", header=T)
effect_size= effect_size %>%  separate(col=intron, into=c("chr","start","end", "gene"), sep=":")
effect_size= effect_size %>%  separate(col=gene, into=c("clu", "gene", "strand"), sep="_")

counts=read.table("../data/leafcutter/all_leaf_200wind.csv")
genes=rownames(counts)


counts_anno=cbind(genes,counts) 

I need a way to plot the counts for the bins called as significant in leafcutter. To do this I should tidy the counts data and have line and sample coulmns. Then I can create boxplots.

counts_melt =melt(counts_anno, id.vars="genes") %>% mutate(fraction=ifelse(grepl("T", variable), "total", "nuclear")) %>% mutate(line=substr(variable,3,7)) %>% separate(col=genes, into=c("chr","bin", "gene"), sep=":")

I can filter this for specific genes and examples. I am going to first look at the gene with the top effect size. ENSG00000066135.8

counts_melt_ENSG00000066135.8= counts_melt %>% filter(gene=="ENSG00000066135.8") %>% arrange(bin) %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>%  mutate(ratio=value/sum)

Try to plot this.

ggplot(counts_melt_ENSG00000066135.8, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in KDM4A by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) 

Look at one more gene. ENSG00000182578.9

counts_melt_ENSG00000182578.9= counts_melt %>% filter(gene=="ENSG00000182578.9") %>% arrange(bin)  %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>%  mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000182578.9, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + geom_jitter(aes(col=fraction),width=.5) +labs(title="Used polyA sites in CSF1R by fraction", y="Read ratio") 

One more. ENSG00000163632.8

counts_melt_ENSG00000163632.8= counts_melt %>% filter(gene=="ENSG00000163632.8") %>% arrange(bin)  %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>%  mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000163632.8, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in C3orf49 by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) 

Top effect size in the positive direction.
ENSG00000113068.5

counts_melt_ENSG00000113068.5= counts_melt %>% filter(gene=="ENSG00000113068.5") %>% arrange(bin)  %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>%  mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000113068.5, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in ENSG00000113068.5 by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) 

Warning: Removed 10 rows containing non-finite values (stat_boxplot).

Warning: Removed 10 rows containing missing values (geom_point).

ENSG00000138785.10

counts_melt_ENSG00000138785.10= counts_melt %>% filter(gene=="ENSG00000138785.10") %>% arrange(bin)  %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>%  mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000138785.10, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in ENSG00000138785.10 by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) 

Session information

sessionInfo()

R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] bindrcpp_0.2.2  reshape2_1.4.3  edgeR_3.20.9    limma_3.34.9   
[5] dplyr_0.7.5     tidyr_0.7.2     ggplot2_2.2.1   workflowr_1.0.1
[9] rmarkdown_1.8.5

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17      compiler_3.4.2    pillar_1.1.0     
 [4] git2r_0.21.0      plyr_1.8.4        bindr_0.1.1      
 [7] R.methodsS3_1.7.1 R.utils_2.6.0     tools_3.4.2      
[10] digest_0.6.15     lattice_0.20-35   evaluate_0.10.1  
[13] tibble_1.4.2      gtable_0.2.0      pkgconfig_2.0.1  
[16] rlang_0.2.1       yaml_2.1.19       stringr_1.3.1    
[19] knitr_1.18        locfit_1.5-9.1    rprojroot_1.3-2  
[22] grid_3.4.2        tidyselect_0.2.4  glue_1.2.0       
[25] R6_2.2.2          purrr_0.2.5       magrittr_1.5     
[28] whisker_0.3-2     backports_1.1.2   scales_0.5.0     
[31] htmltools_0.3.6   assertthat_0.2.0  colorspace_1.3-2 
[34] labeling_0.3      stringi_1.2.2     lazyeval_0.2.1   
[37] munsell_0.4.3     R.oo_1.22.0