Differential transcript detection

What transcripts can we detect in one strain, but not the others?

First we need to detect transcript differences. To do this we run the detect_transcripts.R script found within code/differential_detection directory.

Are these genes enriched for any GO terms?

Next we run the topGO script:

bash code/differential_detection/run_topgo.sh data/gene_ontology/Pf3D7_go_sept2014.txt data/gene_ontology/Pf3D7_desc_sept2014.txt

Are undetected genes sometimes due to known polymorphic regions?

We can address this by calculating the coverage across each exon and comparing the fraction covered by reads.

for i in $(seq 7); 
  do bedtools coverage -a  -b ../data/bam/3d7.3d7_v3_chr.tp${i}.bam -s -split > ../output/differential_detection/coverages/3d7_tp${i}_cov.tsv;
done

for strain in hb3 it;
  do for i in $(seq 7); 
    do bedtools coverage -a  -b ../data/bam/${strain}.3d7chr.tp${i}.bam -s -split > ../output/differential_detection/coverages/${strain}_tp${i}_cov.tsv;
  done
done

read_cov <- function(file) {

  df <- readr::read_tsv(file,col_names=F) %>%
    dplyr::select(X9,X10,X11,X12,X13) %>%
    dplyr::rename(att=X9,reads=X10,nonzero=X11,len=X12,fraction=X13)

  df$exon <- apply(df, 1, function(x) {
    stringr::str_replace(stringr::str_split(stringr::str_split(x[["att"]],";")[[1]][1],"=")[[1]][2],"exon_","")
  })

  return(dplyr::select(df,-att))
}

files_3d7 <- list.files("../output/differential_detection/coverages/",pattern="3d7",full.names=T)
files_hb3 <- list.files("../output/differential_detection/coverages/",pattern="hb3",full.names=T)
files_it <- list.files("../output/differential_detection/coverages/",pattern="it",full.names=T)

tp1_3d7 <- read_cov(files_3d7[1]) %>% mutate(strain="3D7",tp="T1")
tp2_3d7 <- read_cov(files_3d7[2]) %>% mutate(strain="3D7",tp="T2")
tp3_3d7 <- read_cov(files_3d7[3]) %>% mutate(strain="3D7",tp="T3")
tp4_3d7 <- read_cov(files_3d7[4]) %>% mutate(strain="3D7",tp="T4")
tp5_3d7 <- read_cov(files_3d7[5]) %>% mutate(strain="3D7",tp="T5")
tp6_3d7 <- read_cov(files_3d7[6]) %>% mutate(strain="3D7",tp="T6")
tp7_3d7 <- read_cov(files_3d7[7]) %>% mutate(strain="3D7",tp="T7")
df_3d7 <- rbind(tp1_3d7,tp2_3d7,tp3_3d7,tp4_3d7,tp5_3d7,tp6_3d7,tp7_3d7)
rm(tp1_3d7,tp2_3d7,tp3_3d7,tp4_3d7,tp5_3d7,tp6_3d7,tp7_3d7)

tp1_hb3 <- read_cov(files_hb3[1]) %>% mutate(strain="HB3",tp="T1")
tp2_hb3 <- read_cov(files_hb3[2]) %>% mutate(strain="HB3",tp="T2")
tp3_hb3 <- read_cov(files_hb3[3]) %>% mutate(strain="HB3",tp="T3")
tp4_hb3 <- read_cov(files_hb3[4]) %>% mutate(strain="HB3",tp="T4")
tp5_hb3 <- read_cov(files_hb3[5]) %>% mutate(strain="HB3",tp="T5")
tp6_hb3 <- read_cov(files_hb3[6]) %>% mutate(strain="HB3",tp="T6")
tp7_hb3 <- read_cov(files_hb3[7]) %>% mutate(strain="HB3",tp="T7")
df_hb3 <- rbind(tp1_hb3,tp2_hb3,tp3_hb3,tp4_hb3,tp5_hb3,tp6_hb3,tp7_hb3)
rm(tp1_hb3,tp2_hb3,tp3_hb3,tp4_hb3,tp5_hb3,tp6_hb3,tp7_hb3)

tp1_it <- read_cov(files_it[1]) %>% mutate(strain="IT",tp="T1")
tp2_it <- read_cov(files_it[2]) %>% mutate(strain="IT",tp="T2")
tp3_it <- read_cov(files_it[3]) %>% mutate(strain="IT",tp="T3")
tp4_it <- read_cov(files_it[4]) %>% mutate(strain="IT",tp="T4")
tp5_it <- read_cov(files_it[5]) %>% mutate(strain="IT",tp="T5")
tp6_it <- read_cov(files_it[6]) %>% mutate(strain="IT",tp="T6")
tp7_it <- read_cov(files_it[7]) %>% mutate(strain="IT",tp="T7")
df_it <- rbind(tp1_it,tp2_it,tp3_it,tp4_it,tp5_it,tp6_it,tp7_it)
rm(tp1_it,tp2_it,tp3_it,tp4_it,tp5_it,tp6_it,tp7_it)

coverages <- rbind(df_3d7,df_hb3,df_it)
coverages <- coverages[,c(5,6,7,1,2,3,4)] %>%
  separate(exon, into = c("gene_id","exon"), sep = "-") %>%
  group_by(gene_id,strain,tp) %>%
  summarise(reads=sum(reads),nonzero=sum(nonzero),len=sum(len),fraction=sum(nonzero)/sum(len))

readr::write_tsv(x=coverages,path="../output/differential_detection/coverages/coverages.tsv")

coverages$tp <- factor(coverages$tp, levels = c("T1","T2","T3","T4","T5","T6","T7"))

core_genes <- read_tsv("../data/gene_lists/core_pf3d7_genes.txt",col_names=F)$X1

mean_coverages <- coverages %>%
  group_by(gene_id,strain) %>%
  summarise(fraction=mean(fraction)) %>%
  spread(strain,fraction) %>%
  mutate(diffhb3 = `3D7` - HB3, diffit = `3D7` - IT)

max_coverages <- coverages %>%
  group_by(gene_id,strain) %>%
  summarise(fraction=max(fraction)) %>%
  spread(strain,fraction) %>%
  mutate(diffhb3 = `3D7` - HB3, diffit = `3D7` - IT)

g1 <- ggplot(mean_coverages,aes(x=diffhb3)) +
  geom_histogram(color="grey90") +
  xlab("Difference from HB3")
g2 <- ggplot(mean_coverages,aes(x=diffit)) +
  geom_histogram(color="grey90") +
  xlab("Difference from IT")

p <- plot_grid(g1,g2,nrow=1)
print(p)

g <- ggplot(mean_coverages,aes(x=`3D7`,y=HB3)) + geom_point() + geom_point(data=mean_coverages %>% dplyr::filter(gene_id == "PF3D7_1222600"),color = "#E41A1C") + geom_text(data=mean_coverages %>% dplyr::filter(gene_id == "PF3D7_1222600"),label="PF3D7_1222600", color = "#E41A1C", hjust = 1.1)
print(g)

g <- ggplot(mean_coverages,aes(x=`3D7`,y=HB3)) + geom_point() + geom_point(data=mean_coverages %>% dplyr::filter(gene_id == "PF3D7_0930300"),color = "#E41A1C") + geom_text(data=mean_coverages %>% dplyr::filter(gene_id == "PF3D7_0930300"),label="PF3D7_0930300", color = "#E41A1C", hjust = 1.1)
print(g)

g <- ggplot(mean_coverages,aes(x=`3D7`,y=HB3)) + geom_point() + geom_point(data=mean_coverages %>% dplyr::filter(`3D7`<0.25 & HB3 >0.75),color = "#E41A1C") + geom_text(data=mean_coverages %>% dplyr::filter(`3D7`<0.25 & HB3 >0.75), aes(label=gene_id), color = "#E41A1C", hjust = -0.5, check_overlap = TRUE)
print(g)

g <- ggplot(mean_coverages,aes(x=`3D7`,y=IT)) + geom_point() + geom_point(data=mean_coverages %>% dplyr::filter(`3D7`<0.5 & IT >0.5),color = "#E41A1C") + geom_text(data=mean_coverages %>% dplyr::filter(`3D7`<0.5 & IT >0.5), aes(label=gene_id), color = "#E41A1C", hjust = -0.5, check_overlap = TRUE)
print(g)

g <- ggplot(mean_coverages,aes(x=IT,y=HB3)) + geom_point() + geom_point(data=mean_coverages %>% dplyr::filter(IT<0.25 & HB3 >0.75),color = "#E41A1C") + geom_text(data=mean_coverages %>% dplyr::filter(IT<0.25 & HB3 >0.75), aes(label=gene_id), color = "#E41A1C", hjust = -0.5, check_overlap = TRUE)
print(g)

Results

Some genes may barely get over the threshold of 5 TPMs, which was chosen arbitrarily. We are not interested in those results. We want to see what transcripts are clearly abundant in one strain, while clearly absent or at the very least severely down-regulated in another strain. The question then becomes whether this is due to poor coverage across the gene (highly polymorphic) or actual low transcript copy number. MSP1 is an example of a highly polymorphic gene in HB3 versus 3D7. It is not covered very well across the gene and thus has been marked as “undetected.” MSP2 is an example of a gene that is detected, but is also highly polymorphic.

Looking through this CGH data will help as well: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2901668/

readr::read_tsv("../output/differential_detection/off_hb3_not_3d7.tsv") %>%
  DT::datatable(rownames = F)

readr::read_tsv("../output/differential_detection/off_3d7_not_hb3.tsv") %>% 
  DT::datatable(rownames = F)

readr::read_tsv("../output/differential_detection/off_it_not_3d7.tsv") %>% 
DT::datatable(rownames = F)

readr::read_tsv("../output/differential_detection/off_3d7_not_it.tsv") %>% 
  DT::datatable(rownames = F)

readr::read_tsv("../output/differential_detection/off_it_not_hb3.tsv") %>%
  DT::datatable(rownames = F)

readr::read_tsv("../output/differential_detection/off_hb3_not_it.tsv") %>%
  DT::datatable(rownames = F)

Session information

sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Gentoo/Linux

Matrix products: default
BLAS: /usr/local/lib64/R/lib/libRblas.so
LAPACK: /usr/local/lib64/R/lib/libRlapack.so

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] bindrcpp_0.2.2                       
 [2] BSgenome.Pfalciparum.PlasmoDB.v24_1.0
 [3] BSgenome_1.48.0                      
 [4] rtracklayer_1.40.6                   
 [5] Biostrings_2.48.0                    
 [6] XVector_0.20.0                       
 [7] GenomicRanges_1.32.7                 
 [8] GenomeInfoDb_1.16.0                  
 [9] org.Pf.plasmo.db_3.6.0               
[10] AnnotationDbi_1.42.1                 
[11] IRanges_2.14.12                      
[12] S4Vectors_0.18.3                     
[13] Biobase_2.40.0                       
[14] BiocGenerics_0.26.0                  
[15] scales_1.0.0                         
[16] cowplot_0.9.3                        
[17] magrittr_1.5                         
[18] forcats_0.3.0                        
[19] stringr_1.3.1                        
[20] dplyr_0.7.6                          
[21] purrr_0.2.5                          
[22] readr_1.1.1                          
[23] tidyr_0.8.1                          
[24] tibble_1.4.2                         
[25] ggplot2_3.0.0                        
[26] tidyverse_1.2.1                      

loaded via a namespace (and not attached):
 [1] nlme_3.1-137                bitops_1.0-6               
 [3] matrixStats_0.54.0          lubridate_1.7.4            
 [5] bit64_0.9-7                 httr_1.3.1                 
 [7] rprojroot_1.3-2             tools_3.5.0                
 [9] backports_1.1.2             DT_0.4                     
[11] R6_2.3.0                    DBI_1.0.0                  
[13] lazyeval_0.2.1              colorspace_1.3-2           
[15] withr_2.1.2                 tidyselect_0.2.4           
[17] bit_1.1-14                  compiler_3.5.0             
[19] git2r_0.23.0                cli_1.0.1                  
[21] rvest_0.3.2                 xml2_1.2.0                 
[23] DelayedArray_0.6.6          labeling_0.3               
[25] digest_0.6.17               Rsamtools_1.32.3           
[27] rmarkdown_1.10              R.utils_2.7.0              
[29] pkgconfig_2.0.2             htmltools_0.3.6            
[31] htmlwidgets_1.3             rlang_0.2.2                
[33] readxl_1.1.0                rstudioapi_0.8             
[35] RSQLite_2.1.1               shiny_1.1.0                
[37] bindr_0.1.1                 jsonlite_1.5               
[39] crosstalk_1.0.0             BiocParallel_1.14.2        
[41] R.oo_1.22.0                 RCurl_1.95-4.11            
[43] GenomeInfoDbData_1.1.0      Matrix_1.2-14              
[45] Rcpp_0.12.19                munsell_0.5.0              
[47] R.methodsS3_1.7.1           stringi_1.2.4              
[49] whisker_0.3-2               yaml_2.2.0                 
[51] SummarizedExperiment_1.10.1 zlibbioc_1.26.0            
[53] plyr_1.8.4                  grid_3.5.0                 
[55] blob_1.1.1                  promises_1.0.1             
[57] crayon_1.3.4                lattice_0.20-35            
[59] haven_1.1.2                 hms_0.4.2                  
[61] knitr_1.20                  pillar_1.3.0               
[63] XML_3.98-1.16               glue_1.3.0                 
[65] evaluate_0.11               modelr_0.1.2               
[67] httpuv_1.4.5                cellranger_1.1.0           
[69] gtable_0.2.0                assertthat_0.2.0           
[71] mime_0.5                    xtable_1.8-3               
[73] broom_0.5.0                 later_0.7.5                
[75] GenomicAlignments_1.16.0    memoise_1.1.0              
[77] workflowr_1.1.1