Last updated: 2020-04-21
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Knit directory: Comparative_APA/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0f53338 | brimittleman | 2020-04-21 | add de and diff dom mech |
library(workflowr)This is workflowr version 1.6.0
Run ?workflowr for help getting startedlibrary(tidyverse)── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0 ── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()To better understand how the difference in dominance could relate to DE, I want to look at the location of the PAS. If they are both UTR is it is a different expected mechanism for intronic and UTR.
I can then incorporate the direction of effect for expression.
Again start with 0.4 and then expand.
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
MetaCol=colnames(PASMeta)
HumanRes=read.table("../data/DomDefGreaterX/Human_AllGenes_DiffTop.txt", col.names = c("Human_PAS", "gene","Human_DiffDom"),stringsAsFactors = F)
ChimpRes=read.table("../data/DomDefGreaterX/Chimp_AllGenes_DiffTop.txt", col.names = c("Chimp_PAS", "gene","Chimp_DiffDom"),stringsAsFactors = F)
BothRes=HumanRes %>% inner_join(ChimpRes,by="gene")
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)Seperate the different and spread:
BothRes_40_diff= BothRes_40 %>%
filter(Set=="Different") %>%
select(gene, Human_PAS, Chimp_PAS) %>%
gather(Species, PAS, -gene) %>%
inner_join(PASMeta,by=c("gene","PAS")) %>%
select(gene, Species, loc) %>%
spread(Species, loc)Filter these with the DE set:
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
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) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(gene,DE)
DE_yes= DE %>% filter(DE=="Yes")BothRes_40_diff_de= BothRes_40_diff %>% inner_join(DE, by="gene") %>% filter(DE=="Yes") %>% mutate(type=paste(Chimp_PAS, Human_PAS, sep=":"))
BothRes_40_diff_de %>% group_by(type) %>% summarise(ntype=n()) # A tibble: 13 x 2
type ntype
<chr> <int>
1 cds:cds 1
2 cds:intron 1
3 cds:utr3 7
4 end:utr3 2
5 intron:cds 1
6 intron:intron 1
7 intron:utr3 1
8 utr3:cds 10
9 utr3:end 3
10 utr3:intron 19
11 utr3:utr3 28
12 utr3:utr5 1
13 utr5:utr3 1Filter for interactions with more than 1
BothRes_40_diff_de %>% group_by(type) %>% summarise(ntype=n()) %>% filter(ntype>1)# A tibble: 6 x 2
type ntype
<chr> <int>
1 cds:utr3 7
2 end:utr3 2
3 utr3:cds 10
4 utr3:end 3
5 utr3:intron 19
6 utr3:utr3 28This shows a similar relationship where most of these are chimp utr3 and the change is in the human location.
Add in the DE effect.
DEeffect= 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) %>% select(gene, logFC) %>% mutate(directionDE=ifelse(logFC>=1, "Chimp", "Human"))
BothRes_40_diff_deDE= BothRes_40_diff_de %>% inner_join(DEeffect, by="gene")
BothRes_40_diff_deDE %>% group_by(type,directionDE) %>% summarise(ntype=n())# A tibble: 18 x 3
# Groups: type [13]
type directionDE ntype
<chr> <chr> <int>
1 cds:cds Human 1
2 cds:intron Human 1
3 cds:utr3 Chimp 2
4 cds:utr3 Human 5
5 end:utr3 Chimp 1
6 end:utr3 Human 1
7 intron:cds Chimp 1
8 intron:intron Human 1
9 intron:utr3 Human 1
10 utr3:cds Chimp 3
11 utr3:cds Human 7
12 utr3:end Human 3
13 utr3:intron Chimp 3
14 utr3:intron Human 16
15 utr3:utr3 Chimp 8
16 utr3:utr3 Human 20
17 utr3:utr5 Human 1
18 utr5:utr3 Chimp 120 up in human, 8 up in chimp for 3’ UTR-3’ UTR relationships
interesting relationship with chimp utr3, human intron, 16 up in human and 3 up in chimp…
This is interesting as well:
cds:utr3 Chimp 2
cds:utr3 Human 5
Filter the 3’ UTR matched:
BothRes_40_diff_deDEutr3= BothRes_40_diff_deDE %>% filter(type=="utr3:utr3")
nrow(BothRes_40_diff_deDEutr3)[1] 28Add in distal and proximal information for each of these:
I
BothRes_40_diffDistProx= BothRes_40 %>%
filter(Set=="Different") %>%
select(gene, Human_PAS, Chimp_PAS) %>%
gather(Species, PAS, -gene) %>%
inner_join(PASMeta,by=c("gene","PAS")) %>%
group_by(gene) %>%
arrange(start) %>%
mutate(id = 1:n())
BothRes_40_diffDistProx_pos= BothRes_40_diffDistProx %>% filter(strandFix=="+") %>% mutate(Isoform=ifelse(id==1, "Short", "Long")) %>% select(gene, PAS, Species, Isoform)
BothRes_40_diffDistProx_neg= BothRes_40_diffDistProx %>% filter(strandFix=="-") %>% mutate(Isoform=ifelse(id==1, "Long", "Short"))%>% select(gene, PAS, Species, Isoform)
BothRes_40_diffDistProx_bothIso= BothRes_40_diffDistProx_pos %>% bind_rows(BothRes_40_diffDistProx_neg) %>% select(-PAS) %>% spread(Species,Isoform) %>% rename(Chimp_iso=Chimp_PAS, Human_iso =Human_PAS)BothRes_40_diff_deDEutr3_length= BothRes_40_diff_deDEutr3 %>% inner_join(BothRes_40_diffDistProx_bothIso, by="gene")
table=BothRes_40_diff_deDEutr3_length %>% mutate(ChimpHumanLen=paste(Chimp_iso, Human_iso, sep=":")) %>% select(directionDE, ChimpHumanLen) %>% group_by(directionDE, ChimpHumanLen) %>% summarise(n=n())
table# A tibble: 4 x 3
# Groups: directionDE [2]
directionDE ChimpHumanLen n
<chr> <chr> <int>
1 Chimp Long:Short 4
2 Chimp Short:Long 4
3 Human Long:Short 7
4 Human Short:Long 13tabler= table %>% spread(ChimpHumanLen, n) %>% column_to_rownames("directionDE")
chisq.test(tabler)Warning in chisq.test(tabler): Chi-squared approximation may be incorrect
Pearson's Chi-squared test with Yates' continuity correction
data: tabler
X-squared = 0.093583, df = 1, p-value = 0.7597#write out for otheranalysis
write.table(BothRes_40_diff_deDEutr3_length, "../data/DomDefGreaterX/DE_Diffdom4_UTRboth.txt", col.names = T, row.names = F, quote = F)Not significant.
Look at examples:
deanddiff= BothRes_40 %>%
filter(Set=="Different") %>%
inner_join(DE_yes, by="gene") %>%
arrange(gene)
deanddiff Human_PAS gene Human_DiffDom Chimp_PAS Chimp_DiffDom Set
1 human138426 ABR 0.220 chimp125487 0.457500000 Different
2 human172141 ADI1 0.538 chimp171173 0.304166667 Different
3 human170162 AKT1S1 0.626 human170157 0.085833333 Different
4 human358107 ALDH1B1 0.354 human358106 0.600000000 Different
5 human357031 ARHGEF39 0.118 human357038 0.451666667 Different
6 human365538 ARPC5L 0.690 human365528 0.037500000 Different
7 human150577 ARSG 0.186 human150550 0.670833333 Different
8 human117227 BLOC1S6 0.012 human117234 0.535000000 Different
9 human52495 C10orf143 0.242 human52492 0.515833333 Different
10 human265324 C4orf33 0.050 human265332 0.583333333 Different
11 human286317 C5orf15 0.096 human286308 0.417500000 Different
12 human350411 C8orf76 0.524 chimp312071 0.199166667 Different
13 human231310 CCDC51 0.006 human231308 0.590000000 Different
14 human262152 CISD2 0.010 human262148 0.507500000 Different
15 human21027 CKS1B 0.846 human21024 0.805000000 Different
16 human1434 CLSTN1 0.166 human1445 0.441666667 Different
17 human352780 COMMD5 0.116 human352779 0.519166667 Different
18 human68160 DDX6 0.734 human68171 0.515833333 Different
19 human303921 EEF1A1 0.150 human303920 0.915000000 Different
20 human345717 FABP5 0.564 chimp308202 0.926666667 Different
21 human60068 FTH1 0.732 human60067 0.645833333 Different
22 human263322 GAR1 0.046 human263323 0.600833333 Different
23 chimp208497 GNAI2 0.164 human231846 0.412500000 Different
24 human309614 HDDC2 0.096 human309607 0.439166667 Different
25 human167451 HKR1 0.514 human167446 0.050833333 Different
26 human215583 IFNGR2 0.828 chimp163919 0.802500000 Different
27 human332120 IRF5 0.240 human332118 0.637500000 Different
28 human129900 ITPRIPL2 0.118 human129904 0.473333333 Different
29 human303849 KHDC1 0.130 human303827 0.498333333 Different
30 human150491 KPNA2 0.338 chimp136628 0.448333333 Different
31 human217562 LSS 0.548 chimp165747 0.620000000 Different
32 human40961 MARCH8 0.028 chimp36695 0.760000000 Different
33 human123967 MORF4L1 0.098 human123962 0.431666667 Different
34 human137796 MVD 0.642 human137793 0.452500000 Different
35 human331398 NDUFA5 0.640 human331402 0.479166667 Different
36 human36686 NUDT5 0.006 human36685 0.495833333 Different
37 human217145 PDXK 0.298 human217139 0.424166667 Different
38 human35795 PFKFB3 0.180 human35810 0.440000000 Different
39 human48357 PGAM1 0.566 human48359 0.960000000 Different
40 human324283 POM121 0.044 human324315 0.407500000 Different
41 human43366 PPA1 0.202 human43351 0.666666667 Different
42 human16820 PSMA5 0.082 human16815 0.524166667 Different
43 human315881 PSMB1 0.026 chimp281346 0.975833333 Different
44 human112093 PSMC1 0.902 chimp102313 0.042500000 Different
45 human316846 RAC1 0.572 human316843 0.021666667 Different
46 human161808 RNF126 0.642 human161809 0.020833333 Different
47 human229486 RPL14 0.118 human229488 0.426666667 Different
48 human650 RPL22 0.330 human651 0.429166667 Different
49 human142691 RPL23A 0.438 human142693 0.696666667 Different
50 human225615 RPL32 0.730 human225614 0.571666667 Different
51 human344785 RPL7 0.946 human344788 0.007500000 Different
52 human269831 RWDD4 0.420 human269825 0.082500000 Different
53 human22819 SDHC 0.012 human22809 0.754166667 Different
54 human366500 SLC27A4 0.594 human366498 0.515833333 Different
55 human142532 SLC46A1 0.040 human142539 0.557500000 Different
56 human155788 SNRPD1 0.694 chimp141120 0.007500000 Different
57 human102488 SNX6 0.014 human102501 0.635000000 Different
58 human222105 ST13 0.298 human222090 0.437500000 Different
59 human52635 STK32C 0.866 human52625 0.067500000 Different
60 human137265 TAF1C 0.190 human137266 0.641666667 Different
61 human70925 TAPBPL 0.222 human70922 0.650833333 Different
62 human277896 TBCA 0.118 human277890 0.426666667 Different
63 human153035 TBCD 0.016 human153045 0.816666667 Different
64 human89242 TMED2 0.034 human89240 0.427500000 Different
65 human344970 TMEM70 0.622 human344972 0.123333333 Different
66 human94752 TPT1 0.562 human94756 0.004166667 Different
67 human221197 TRIOBP 0.284 human221196 0.401666667 Different
68 human141421 TRPV2 0.758 human141413 0.207500000 Different
69 human151667 UBALD2 0.436 human151668 0.348333333 Different
70 human321907 UBE2D4 0.020 human321917 0.512500000 Different
71 human340358 UBXN8 0.072 human340355 0.801666667 Different
72 human135011 UTP4 0.472 chimp122712 0.711666667 Different
73 human172529 YWHAQ 0.072 human172530 0.458333333 Different
74 human166806 ZNF302 0.026 human166815 0.456666667 Different
75 human335508 ZNF425 0.476 human335505 0.050000000 Different
76 human141183 ZSWIM7 0.418 human141185 0.625833333 Different
cut DE
1 40 Yes
2 40 Yes
3 40 Yes
4 40 Yes
5 40 Yes
6 40 Yes
7 40 Yes
8 40 Yes
9 40 Yes
10 40 Yes
11 40 Yes
12 40 Yes
13 40 Yes
14 40 Yes
15 40 Yes
16 40 Yes
17 40 Yes
18 40 Yes
19 40 Yes
20 40 Yes
21 40 Yes
22 40 Yes
23 40 Yes
24 40 Yes
25 40 Yes
26 40 Yes
27 40 Yes
28 40 Yes
29 40 Yes
30 40 Yes
31 40 Yes
32 40 Yes
33 40 Yes
34 40 Yes
35 40 Yes
36 40 Yes
37 40 Yes
38 40 Yes
39 40 Yes
40 40 Yes
41 40 Yes
42 40 Yes
43 40 Yes
44 40 Yes
45 40 Yes
46 40 Yes
47 40 Yes
48 40 Yes
49 40 Yes
50 40 Yes
51 40 Yes
52 40 Yes
53 40 Yes
54 40 Yes
55 40 Yes
56 40 Yes
57 40 Yes
58 40 Yes
59 40 Yes
60 40 Yes
61 40 Yes
62 40 Yes
63 40 Yes
64 40 Yes
65 40 Yes
66 40 Yes
67 40 Yes
68 40 Yes
69 40 Yes
70 40 Yes
71 40 Yes
72 40 Yes
73 40 Yes
74 40 Yes
75 40 Yes
76 40 YesUse the Rscript PlotNuclearUsagebySpecies_DF.R -g DFFB
mkdir ../data/DiffDomandDE_exampledeanddiff_genes= deanddiff %>% select(gene)
write.table(deanddiff_genes, "../data/DiffDomandDE_example/genesfor4examples.txt", col.names = F,row.names = F, quote=F)#PlotNuclearUsagebySpecies_DF_DEout.R
sbatch NuclearPlotsDEandDiffDom_4.sh
#problem with ../data/files4viz_nuclear_DF/NuclearPASUsage.txtAre any of these genes in the directional selection set?
1.directional human 2.directional in chimp 3. undetermined 4. no mean difference 5.relaxed in human 6.related in chimp
KhanData=read.csv("../data/Khan_prot/Khan_TableS4.csv",stringsAsFactors = F) %>% select(gene.symbol,contains("model") ) %>% rename("gene"=gene.symbol, "Protein"=model.num.protein, "RNA"=model.num.rna) %>% filter(gene %in% BothRes_40_diffDistProx_bothIso$gene)
KhanData %>% filter(RNA %in% c(1,2,5,6)) %>% group_by(RNA) %>% summarise(n=n())# A tibble: 2 x 2
RNA n
<int> <int>
1 1 4
2 2 14KhanData %>% filter(RNA %in% c(1,2,5,6)) gene Protein RNA
1 TMED2 4 2
2 PSMC1 4 2
3 DDX6 3 2
4 HDDC2 3 2
5 GNAI2 4 1
6 RPL22 4 2
7 RAB14 4 1
8 TUBGCP3 3 2
9 IRF3 3 2
10 FLNB 3 2
11 RAC1 4 2
12 TPM3 3 2
13 IFIT5 4 1
14 NUDT5 4 1
15 TMEM70 4 2
16 ADI1 3 2
17 MORF4L1 4 2
18 SEC22B 2 2
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38 colorspace_1.3-2
[5] generics_0.0.2 htmltools_0.3.6 yaml_2.2.0 utf8_1.1.4
[9] rlang_0.4.0 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 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[21] evaluate_0.12 knitr_1.20 httpuv_1.4.5 fansi_0.4.0
[25] broom_0.5.1 Rcpp_1.0.2 promises_1.0.1 scales_1.0.0
[29] backports_1.1.2 jsonlite_1.6 fs_1.3.1 hms_0.4.2
[33] digest_0.6.18 stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[37] cli_1.1.0 tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[41] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[45] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[53] compiler_3.5.1