Last updated: 2019-02-27

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Knit directory: threeprimeseq/analysis/

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
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    Modified:   code/Snakefile

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File Version Author Date Message
Rmd c583588 Briana Mittleman 2019-02-27 add res and plots
html dd8d988 Briana Mittleman 2019-02-21 Build site.
Rmd d210987 Briana Mittleman 2019-02-21 add res and plots
html 4ea438e Briana Mittleman 2019-02-18 Build site.
Rmd bcb2f86 Briana Mittleman 2019-02-18 add qtl by per and diff iso 
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0
Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
  • filternamePeaks5percCov_GeneLocAnno.py
  • bothFrac_processed_GeneLocAnno_FC.sh
  • fix_head_fc_procBothFrac_GeneLocAnno.py
  • fc2leafphen_processed_GeneLocAnno.py
  • subset_diffisopheno_processed_GeneLocAnno.py/ run_subset_diffisopheno_processed_GeneLocAnno.sh
  • makeLCSampleList_processed_GeneLocAnno.py
  • run_leafcutter_ds_bychrom_processed_GeneLocAnno.sh

Leafcutter environment: module unload Anaconda3 module load Anaconda3/5.3.0 conda activate leafcutter

awk '{if(NR>1)print}' /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_chr*.txt_effect_sizes.txt > /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_effect_sizes.txt

awk '{if(NR>1)print}' /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_chr*cluster_significance.txt > /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_cluster_significance.txt
diffIso=read.table("../data/diff_iso_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_cluster_significance.txt", header = F,col.names = c("status",   "loglr",    "df",   "p",    "cluster",  "p.adjust"),stringsAsFactors = F,sep="\t") %>% filter(status == "Success")


diffIso$p.adjust=as.numeric(as.character(diffIso$p.adjust))

Make plot

png("../output/plots/DiffIsoQQplot.png")
qqplot(-log10(runif(nrow(diffIso))), -log10(diffIso$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between fractions")
abline(0,1)
dev.off()
quartz_off_screen 
                2 
diffIso_10FDR=diffIso %>% filter(-log10(p.adjust)>1)

diffIso_10FDR_genes=diffIso_10FDR %>% separate(cluster, into = c("chr", "gene"), sep=":") %>% group_by(gene) %>% tally()

nrow(diffIso_10FDR_genes)
[1] 8227

There are 8227 significant genes

effectsize=read.table("../data/diff_iso_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltapsi'))

effectsize$deltapsi=as.numeric(as.character(effectsize$deltapsi))
Warning: NAs introduced by coercion
effectsize$logef=as.numeric(as.character(effectsize$logef))
Warning: NAs introduced by coercion
plot(sort(effectsize$deltapsi),main="Leafcutter delta PSI", ylab="Delta PSI", xlab="Peak Index")

Version Author Date
dd8d988 Briana Mittleman 2019-02-21 
effectsize_dpsi= effectsize %>% filter(abs(deltapsi) > .2) 

effectsize_dpsi_gene= effectsize %>% filter(abs(deltapsi) > .2) %>% separate(intron, into=c("chr", 'start', 'end','gene'), sep=":") %>% group_by(gene) %>% tally()

nrow(effectsize_dpsi)
[1] 2574
nrow(effectsize_dpsi_gene)
[1] 1983
inboth=effectsize_dpsi_gene %>% inner_join(diffIso_10FDR_genes, by="gene")
nrow(inboth)
[1] 1983

There are 1983 genes that are significant at 10 FDR with peaks with delta psi > .2. There are 2574 peaks in this set.

arrange(effectsize_dpsi,deltapsi) %>% head()
                              intron     logef           Nuclear
1 chr1:151134497:151134579:TNFAIP8L2 -1.531127  0.78054161651153
2       chr21:43762910:43762982:TFF2 -1.292723   0.7517177403328
3      chr3:23306502:23306675:UBE2E2 -1.576854 0.689518624324535
4       chr14:67029307:67029417:GPHN -1.178720  0.79525048466399
5         chr6:84007319:84007404:ME1 -1.941535 0.637895884685942
6    chr7:73885912:73885994:GTF2IRD1 -1.094156 0.803004504625396
              Total   deltapsi
1 0.142652878646319 -0.6378887
2 0.185782405086405 -0.5659353
3 0.152772791233433 -0.5367458
4 0.268829380937913 -0.5264211
5 0.115849020504727 -0.5220469
6 0.313645034829832 -0.4893595

How many total genes tested:

diffIsoGene=diffIso %>% separate(cluster, into=c("chrom", "gene"), sep = ":") 

length(unique(diffIsoGene$gene))
[1] 9790

We tested 9790 genes and 8227 are significant at FDR 10%

I can make a plot that separates genes into tested, if passes has fdr 10%, if it has a peak greater than .2 delta psi.

sigandPSIGene=effectsize_dpsi_gene$gene
SiggenesDF=diffIso_10FDR %>% separate(cluster, into=c("chrom", "gene"), sep = ":")  %>% select(gene)
Siggenes = SiggenesDF$gene
LCgeneDF=diffIsoGene %>% select(gene)
LCgene=LCgeneDF$gene
type=c("NotSig", "Sig", "SigHighDPAU")
nGenes=c(1563, 6244,1983)
nGenesProp=c(1563/9790, 6244/9790, 1983/9790)
LCDF=data.frame(cbind(type, nGenes, nGenesProp))
LCDF$nGenesProp=as.numeric(as.character(LCDF$nGenesProp))
labT=paste("Genes =", "1563", sep=" ")
labS=paste("Genes =", "6244", sep=" ")
labD=paste("Genes =", "1983", sep=" ")




LCResplot=ggplot(LCDF, aes(x=" ", y=nGenesProp, fill=type))+ geom_bar(stat="identity") + labs(x="Total Genes = 9790", y="Proportion of Genes", title="Proportion of Genes \nby Differencial PAU Test Result") + annotate("text", x=" ", y= .1, label=labT) + annotate("text", x=" ", y= .5, label=labS) + annotate("text", x=" ", y= .9, label=labD) + scale_fill_brewer(palette="RdYlBu")

LCResplot

Version Author Date
dd8d988 Briana Mittleman 2019-02-21 
ggsave(LCResplot, file="../output/plots/LCResPlot.png",height=8, width=5)

As a boxplot:

LCResplotpie=ggplot(LCDF, aes(x=" ", y=nGenesProp, fill=type))+ geom_bar(stat="identity") + labs(x="Total Genes = 9790", y="Proportion of Genes", title="Proportion of Genes \nby Differencial PAU Test Result")  + scale_fill_brewer(palette="RdYlBu")+ coord_polar("y")

LCResplotpie

Version Author Date
dd8d988 Briana Mittleman 2019-02-21 
ggsave(LCResplotpie, file="../output/plots/LCResBoxPie.png")
Saving 7 x 5 in image

Look at examples:

arrange(effectsize_dpsi,deltapsi) %>% head(n=15)
                                 intron      logef           Nuclear
1    chr1:151134497:151134579:TNFAIP8L2 -1.5311270  0.78054161651153
2          chr21:43762910:43762982:TFF2 -1.2927231   0.7517177403328
3         chr3:23306502:23306675:UBE2E2 -1.5768538 0.689518624324535
4          chr14:67029307:67029417:GPHN -1.1787199  0.79525048466399
5            chr6:84007319:84007404:ME1 -1.9415348 0.637895884685942
6       chr7:73885912:73885994:GTF2IRD1 -1.0941563 0.803004504625396
7           chr10:76217704:76217788:ADK -2.3345121 0.514019809620595
8      chr13:76202828:76202942:LMO7-AS1 -0.9814079  0.72785023020159
9      chr11:10415338:10415423:CAND1.11 -0.9620970 0.664450041884926
10         chr3:52434425:52434511:DNAH1 -0.9754214 0.643269826767032
11 chr11:61518275:61518363:DKFZP434K028 -0.9377435 0.715454168470806
12       chr1:246336771:246336983:SMYD3 -1.3736073 0.493768702856053
13        chr1:52550381:52550450:BTF3L4 -1.5857042 0.542706568051568
14        chr13:99716094:99716178:DOCK9 -1.9875150 0.479993274419107
15        chr1:234519189:234519278:COA6 -1.4539240 0.535470572408034
                Total   deltapsi
1   0.142652878646319 -0.6378887
2   0.185782405086405 -0.5659353
3   0.152772791233433 -0.5367458
4   0.268829380937913 -0.5264211
5   0.115849020504727 -0.5220469
6   0.313645034829832 -0.4893595
7  0.0448511480538154 -0.4691687
8      0.273075759544 -0.4547745
9   0.224261076041106 -0.4401890
10  0.204035877067268 -0.4392339
11  0.278188768939522 -0.4372654
12 0.0588483217543034 -0.4349204
13  0.108088063042586 -0.4346185
14 0.0504035538093602 -0.4295897
15  0.110557031548937 -0.4249135

Stuck on visualization

peak5329- that is the QTL peak for dock7

test=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt")
testN=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt")


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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  workflowr_1.2.0 forcats_0.3.0  
 [5] stringr_1.4.0   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4   haven_1.1.2        lattice_0.20-35   
 [4] colorspace_1.3-2   htmltools_0.3.6    yaml_2.2.0        
 [7] rlang_0.2.2        pillar_1.3.0       glue_1.3.0        
[10] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[13] readxl_1.1.0       bindr_0.1.1        plyr_1.8.4        
[16] munsell_0.5.0      gtable_0.2.0       cellranger_1.1.0  
[19] rvest_0.3.2        evaluate_0.13      labeling_0.3      
[22] knitr_1.20         broom_0.5.0        Rcpp_0.12.19      
[25] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[28] fs_1.2.6           hms_0.4.2          digest_0.6.17     
[31] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[34] cli_1.0.1          tools_3.5.1        magrittr_1.5      
[37] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[40] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[43] assertthat_0.2.0   rmarkdown_1.11     httr_1.3.1        
[46] rstudioapi_0.9.0   R6_2.3.0           nlme_3.1-137      
[49] git2r_0.24.0       compiler_3.5.1