Last updated: 2019-03-15
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 5d6ac93 | Briana Mittleman | 2019-03-15 | add decay analysis |
I want to ask if more nuclear specific transcripts compared to total is associated with RNA decay.
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0 ✔ purrr 0.3.1
✔ tibble 2.0.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.4.0
✔ readr 1.3.1 ✔ forcats 0.4.0
Warning: package 'tibble' was built under R version 3.5.2
Warning: package 'tidyr' was built under R version 3.5.2
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── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
decay=read.table(file = "../data/RNAdecay/tr_decay_table_norm.txt", header=T, stringsAsFactors = F) %>% select(gene_id,contains("RNAdecay"))
Change gene names:
geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F)
decay_geneNames=decay %>% inner_join(geneNames, by="gene_id") %>% select(GeneName, contains("RNAdecay"))
decay_geneNames_long=melt(decay_geneNames,id.vars = "GeneName", value.name = "RNA_Decay", variable.name = "Decay_Ind") %>% separate(Decay_Ind, into=c("type", "ind"), sep="_") %>% mutate(Individual=paste("X" , ind, sep="")) %>% select(GeneName, Individual, RNA_Decay)
For each gene I need to get nuclear counts/nuclear + counts
I want to use the filtered 5% peak counts.
/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.5perc.fc
/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc
Make a dictionary from the individuals in the first line. I want them to have NA##### format
makepheno4decayComparison.py
nucCounts="/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.5perc.fc"
totCounts="/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc"
#top key is individual
OutPutdic={}
#problem keeping ind connected to column
Try in R
Nuclear first:
NucAPA=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.5perc.fc", stringsAsFactors = F, header = T) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chrom", "start", "end", "strand", "GeneName"), sep=":") %>% select(-chrom, -start, -end, -strand)
NucApaMelt=melt(NucAPA, id.vars =c( "peak", "GeneName"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_") %>% select(peak, GeneName, Individual, count)
NucAPA_bygene= NucApaMelt %>% group_by(GeneName,Individual) %>% summarise(NuclearSum=sum(count))
Total first:
TotAPA=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", stringsAsFactors = F, header = T) %>% select(-Chr, -Start, -End, -Strand, -Length) %>% separate(Geneid, into=c("peak", "chrom", "start", "end", "strand", "GeneName"), sep=":") %>% select(-chrom, -start, -end, -strand)
TotApaMelt=melt(TotAPA, id.vars =c( "peak", "GeneName"), value.name="count", variable.name="Ind") %>% separate(Ind, into=c('Individual', 'fraction') ,sep="_") %>% select(peak, GeneName, Individual, count)
TotAPA_bygene=TotApaMelt %>% group_by(GeneName,Individual) %>% summarise(TotalSum=sum(count))
Sum these together:
Apa_all=TotAPA_bygene %>% inner_join(NucAPA_bygene, by=c("GeneName", "Individual")) %>% filter(NuclearSum>0 |TotalSum>0 ) %>% mutate(APAvalue=NuclearSum/(NuclearSum+TotalSum)) %>% select(GeneName, Individual, APAvalue)
APAandDecay=decay_geneNames_long %>% inner_join(Apa_all, by=c('GeneName', 'Individual'))
ngenes=APAandDecay %>% select(GeneName) %>% unique() %>% nrow()
ngenes
[1] 7888
plot it:
summary(lm(data=APAandDecay, APAvalue~RNA_Decay))
Call:
lm(formula = APAvalue ~ RNA_Decay, data = APAandDecay)
Residuals:
Min 1Q Median 3Q Max
-0.46459 -0.15044 -0.01135 0.13392 0.58497
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4373568 0.0003228 1354.83 <2e-16 ***
RNA_Decay -0.0257699 0.0019255 -13.38 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2017 on 398991 degrees of freedom
Multiple R-squared: 0.0004487, Adjusted R-squared: 0.0004462
F-statistic: 179.1 on 1 and 398991 DF, p-value: < 2.2e-16
APAdecalAllindplot=ggplot(APAandDecay, aes(y=APAvalue, x=RNA_Decay)) + geom_point(aes(col=Individual)) +geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_smooth(method="lm") + annotate("text", label="Estimated Slope= -.026", y=1, x=-1) + labs(title="Relationship between RNA decay \nand APA fraction counts", x=" mRNA decay rate/h", y= "Nuclear/(Nuclear + Total)")
APAdecalAllindplot
ggsave(APAdecalAllindplot, file="../output/plots/APAandRNADecay_allInd.png", height = 7, width=15)
1 individual:
APAandDecay_18498= APAandDecay %>% filter(Individual=="X18498")
APAdecay_18498=ggplot(APAandDecay_18498, aes(y=APAvalue, x=RNA_Decay)) + geom_point() +geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + annotate("text", label="Estimated Slope= -.133", y=0, x=-.8) + geom_smooth(method="lm")+ labs(title="Relationship between RNA decay \nand APA fraction counts", x=" mRNA decay rate/h", y= "Nuclear/(Nuclear + Total)")
APAdecay_18498
ggsave(APAdecay_18498, file="../output/plots/APAandRNADecay_18498.png")
Saving 7 x 5 in image
summary(lm(data=APAandDecay_18498, APAvalue~RNA_Decay))
Call:
lm(formula = APAvalue ~ RNA_Decay, data = APAandDecay_18498)
Residuals:
Min 1Q Median 3Q Max
-0.63123 -0.17159 0.00659 0.17479 0.47142
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.581252 0.002667 217.933 < 2e-16 ***
RNA_Decay -0.133867 0.016938 -7.903 3.09e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2324 on 7766 degrees of freedom
Multiple R-squared: 0.007979, Adjusted R-squared: 0.007851
F-statistic: 62.46 on 1 and 7766 DF, p-value: 3.094e-15
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] reshape2_1.4.3 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.0.1
[5] purrr_0.3.1 readr_1.3.1 tidyr_0.8.3 tibble_2.0.1
[9] ggplot2_3.1.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 plyr_1.8.4 pillar_1.3.1
[5] compiler_3.5.1 git2r_0.24.0 workflowr_1.2.0 tools_3.5.1
[9] digest_0.6.18 lubridate_1.7.4 jsonlite_1.6 evaluate_0.13
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-38 pkgconfig_2.0.2
[17] rlang_0.3.1 cli_1.0.1 rstudioapi_0.9.0 yaml_2.2.0
[21] haven_2.1.0 xfun_0.5 withr_2.1.2 xml2_1.2.0
[25] httr_1.4.0 knitr_1.21 hms_0.4.2 generics_0.0.2
[29] fs_1.2.6 rprojroot_1.3-2 grid_3.5.1 tidyselect_0.2.5
[33] glue_1.3.0 R6_2.4.0 readxl_1.3.0 rmarkdown_1.11
[37] modelr_0.1.4 magrittr_1.5 whisker_0.3-2 MASS_7.3-51.1
[41] backports_1.1.3 scales_1.0.0 htmltools_0.3.6 rvest_0.3.2
[45] assertthat_0.2.0 colorspace_1.4-0 labeling_0.3 stringi_1.3.1
[49] lazyeval_0.2.1 munsell_0.5.0 broom_0.5.1 crayon_1.3.4