Last updated: 2020-04-10
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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Rmd | 86aabf2 | brimittleman | 2020-01-28 | add expression overlap with APA exploration |
I will complete the same analysis I did in the other explore APA and expression analysis but I will subset the overlapping gene. For these I know that there are differences in both phenotypes.
Genes with overlap:
GenesUse=read.table("../data/DiffIso_Nuclear_DF/GeneswithDEanddAPA.txt", header = T, stringsAsFactors = F)
In this analysis I will do as much as I can do compare APA and expression. I will look at the genes we have data for both.
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ readr 1.3.1 ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library("gplots")
Attaching package: 'gplots'
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library("scales")
Attaching package: 'scales'
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library("RColorBrewer")
library(ggpubr)
Loading required package: magrittr
Attaching package: 'magrittr'
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I will start with a full correlation matrix using the counts for the top used PAS (mean human and chimp).
TopUsedPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header=T,stringsAsFactors = F) %>% mutate(MeanUsage=(Chimp+Human)/2) %>% group_by(gene) %>% arrange(desc(MeanUsage)) %>% slice(1) %>% ungroup() %>% dplyr::select(PAS, chr, start, end) %>% rename("Chr"= chr,"Start"= start, "End"= end)
Filter these in the counts file:
#Both:human74:chr1:944201:944401:+:NOC2L_utr3
HumanCounts=read.table("../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc",stringsAsFactors = F,header = T) %>% inner_join(TopUsedPAS,by=c("Chr", "Start", "End")) %>% separate(Geneid, into=c("d", "pas", "chr","start","end","strand", "geneID"), sep=":") %>% separate(geneID,into=c("gene","loc"),sep="_") %>% dplyr::select(PAS, gene, contains("_N"))
ChimpCounts=read.table("../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc",stringsAsFactors = F,header = T) %>% separate(Geneid, into=c("d", "PAS", "chr","start","end","strand", "geneID"), sep=":") %>% separate(geneID,into=c("gene","loc"),sep="_") %>% inner_join(TopUsedPAS,by=c("PAS")) %>% dplyr::select(PAS, gene, contains("_N"))
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [50254,
50255, 50256, 95621].
AllCounts=HumanCounts %>% inner_join(ChimpCounts, by=c("gene", "PAS")) %>% dplyr::select(-PAS)
colnames(AllCounts)=paste("APA" ,colnames(AllCounts), sep="_")
AllCounts=AllCounts %>% rename("gene"= APA_gene) %>% filter(gene %in% GenesUse$gene)
Expression counts:
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
HumanRNA=read.table("../Human/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)
ChimpRNA=read.table("../Chimp/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)
RNACounts=HumanRNA %>% inner_join(ChimpRNA,by="Geneid") %>% rename("Gene_stable_ID"=Geneid) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"= Gene.name) %>% group_by(gene) %>% slice(1) %>% ungroup()
colnames(RNACounts)=paste("RNA" ,colnames(RNACounts), sep="_")
RNACounts=RNACounts %>% rename("gene"= RNA_gene) %>% filter(gene %in% GenesUse$gene)
Add all together:
BothPhenoCount= RNACounts %>% inner_join(AllCounts, by="gene")%>% column_to_rownames(var="gene")
Names=colnames(BothPhenoCount)
pal <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"), brewer.pal(12, "Set3"))
colors <- colorRampPalette(c(brewer.pal(9, "Blues")[1],brewer.pal(9, "Blues")[9]))(100)
Species=c(rep("Human",5), rep("Chimp",6),rep("Human",5),rep("Chimp",6))
Meta=as.data.frame(cbind(Names,Species )) %>% mutate(Pheno=ifelse(grepl("RNA",Names),"RNA", "APA" ))
Warning in cbind(Names, Species): number of rows of result is not a
multiple of vector length (arg 2)
cors <- cor(BothPhenoCount, method="spearman", use="pairwise.complete.obs")
heatmap.2( cors, scale="none", col = colors, margins = c(12, 12), trace='none',ColSideColors=pal[as.integer(as.factor(Meta$Species))], RowSideColors=pal[as.integer(as.factor(Meta$Pheno))+9], cexCol = 0.2 + 1/log10(15), cexRow = 0.2 + 1/log10(15))
Pvalues:
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name)%>% filter(gene %in% GenesUse$gene)
APA=read.table(c("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt"),header = T,stringsAsFactors = F)
GenesWithapa=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F) %>% filter(gene %in% GenesUse$gene)
DEandAPA= DE %>% inner_join(APA, by="gene") %>% mutate(HasDPAU=ifelse(gene %in% GenesWithapa$gene, "Yes","No"))
Correlation between effect sizes:
ggplot(DEandAPA,aes(x=logFC, y=logef)) + geom_point() + labs(x="DE log Effect size", y="APA effect size",title="Relationship between effect sizes") + geom_density2d()
ggplot(DEandAPA,aes(x=abs(logFC), y=abs(logef))) + geom_point()+ labs(x="abs(DE log Effect size)", y="abs(APA effect size)",title="Relationship between absolute values of effect sizes") + geom_smooth(method="lm") +annotate("text", label="Beta= 0.28 , R2= 0.03",y=40,x=5)
summary(lm(abs(DEandAPA$logef)~abs(DEandAPA$logFC)))
Call:
lm(formula = abs(DEandAPA$logef) ~ abs(DEandAPA$logFC))
Residuals:
Min 1Q Median 3Q Max
-2.141 -0.728 -0.355 0.206 85.459
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.84969 0.05699 14.910 < 2e-16 ***
abs(DEandAPA$logFC) 0.24810 0.03621 6.853 8.64e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.082 on 3264 degrees of freedom
Multiple R-squared: 0.01418, Adjusted R-squared: 0.01388
F-statistic: 46.96 on 1 and 3264 DF, p-value: 8.638e-12
Subset the PAS in the top used:
TopUsedPASlow= TopUsedPAS %>% rename("chr"=Chr, "start"=Start, "end"=End)
DEandAPA_filt= DEandAPA %>% semi_join(TopUsedPASlow,by=c("chr","start","end"))
Correlation between effect sizes:
ggplot(DEandAPA_filt,aes(x=logFC, y=logef)) + geom_point() + labs(x="DE log Effect size", y="APA effect size", title="Relationship between effect sizes \n most used PAS")
ggplot(DEandAPA_filt,aes(x=abs(logFC), y=abs(logef))) + geom_point( )+ labs(x="abs(DE log Effect size)", y="abs(APA effect size)",title="Relationship between absolute values of effect sizes \n most used PAS") + geom_smooth(method="lm") +annotate("text", label="Beta= 0.3 , R2= 0.04",y=20,x=5)
summary(lm(abs(DEandAPA_filt$logef)~abs(DEandAPA_filt$logFC)))
Call:
lm(formula = abs(DEandAPA_filt$logef) ~ abs(DEandAPA_filt$logFC))
Residuals:
Min 1Q Median 3Q Max
-2.2025 -0.7283 -0.3141 0.1841 16.8990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.90926 0.09678 9.395 < 2e-16 ***
abs(DEandAPA_filt$logFC) 0.34544 0.06231 5.544 4.41e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.555 on 608 degrees of freedom
Multiple R-squared: 0.04812, Adjusted R-squared: 0.04656
F-statistic: 30.74 on 1 and 608 DF, p-value: 4.405e-08
Number of significant PAS and de
DEandAPA_sig= DEandAPA %>% filter(SigPAU2 == "Yes") %>% group_by(gene) %>% summarise(NsigPAS=n())
DEandAPA_sigWithDE= DE %>% inner_join(DEandAPA_sig,by="gene" )
summary(lm(data=DEandAPA_sigWithDE, abs(logFC)~NsigPAS))
Call:
lm(formula = abs(logFC) ~ NsigPAS, data = DEandAPA_sigWithDE)
Residuals:
Min 1Q Median 3Q Max
-1.3016 -0.6169 -0.3045 0.2329 5.7502
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.85417 0.10191 8.382 3.62e-16 ***
NsigPAS 0.23005 0.06607 3.482 0.000534 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.002 on 608 degrees of freedom
Multiple R-squared: 0.01955, Adjusted R-squared: 0.01794
F-statistic: 12.12 on 1 and 608 DF, p-value: 0.0005339
ggplot(DEandAPA_sigWithDE, aes(x=NsigPAS, y=abs(logFC))) +geom_point() + geom_smooth(method = "lm") + annotate("text",label="beta=.2, r2=0.018, pvalue=0.0006",x=4, y=6) + labs(x="Number of differentially used PAS 20%",title="Relationship between DE effect size and dAPA PAS")
cor( DEandAPA_sigWithDE$NsigPAS, DEandAPA_sigWithDE$logFC, method = "spearman")
[1] -0.04383949
cor(DEandAPA_sigWithDE$B, DEandAPA_sigWithDE$NsigPAS, method = "spearman")
[1] 0.1248363
Maybe use the variance in effect sizes for dAPA:
APA_var= APA %>% group_by(gene) %>% summarise(VarLogef=var(logef), VarDeltaPAU=var(deltaPAU))
APA_varDE= APA_var %>% inner_join(DE, by="gene")
cor(APA_varDE$VarDeltaPAU, APA_varDE$B, method = "spearman")
[1] 0.09857041
cor(APA_varDE$VarDeltaPAU, APA_varDE$logFC, method = "spearman")
[1] -0.02724444
cor(APA_varDE$VarLogef, APA_varDE$B, method = "spearman")
[1] 0.2396989
cor(APA_varDE$VarLogef, APA_varDE$logFC, method = "spearman")
[1] -0.01888453
This does not help other than for the varlogeffect size for APA and the DE beta value.
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggpubr_0.2 magrittr_1.5 RColorBrewer_1.1-2
[4] scales_1.0.0 gplots_3.0.1 forcats_0.3.0
[7] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[10] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[13] ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] gtools_3.8.1 tidyselect_0.2.5 haven_1.1.2
[4] lattice_0.20-38 colorspace_1.3-2 generics_0.0.2
[7] htmltools_0.3.6 yaml_2.2.0 rlang_0.4.0
[10] later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] workflowr_1.6.0 cellranger_1.1.0 rvest_0.3.2
[22] caTools_1.17.1.1 evaluate_0.12 labeling_0.3
[25] knitr_1.20 httpuv_1.4.5 broom_0.5.1
[28] Rcpp_1.0.2 KernSmooth_2.23-15 promises_1.0.1
[31] backports_1.1.2 gdata_2.18.0 jsonlite_1.6
[34] fs_1.3.1 hms_0.4.2 digest_0.6.18
[37] stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[40] bitops_1.0-6 cli_1.1.0 tools_3.5.1
[43] lazyeval_0.2.1 crayon_1.3.4 whisker_0.3-2
[46] pkgconfig_2.0.2 MASS_7.3-51.1 xml2_1.2.0
[49] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[52] httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[55] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1