Can signal site explain differential usage

Last updated: 2020-05-08

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DICNotDEDP.Rmd
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/SetsdAPADIC.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	8c3efc8	brimittleman	2020-05-08	fix example plots and add dIC examples
html	5bdd8c7	brimittleman	2020-05-05	Build site.
Rmd	6850c9d	brimittleman	2020-05-05	fix enrichemnt
html	8f72fb7	brimittleman	2020-04-10	Build site.
Rmd	db347d0	brimittleman	2020-04-10	remove 184999
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Rmd	fbcbbd2	brimittleman	2020-04-07	add non con analysis
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Rmd	92f06e4	brimittleman	2020-04-06	update anno
html	332cbca	brimittleman	2020-03-19	Build site.
Rmd	4e2a869	brimittleman	2020-03-19	add SS in opp
html	ac2b058	brimittleman	2020-01-26	Build site.
Rmd	6658151	brimittleman	2020-01-26	add density plots
html	838da9b	brimittleman	2020-01-23	Build site.
Rmd	a018c33	brimittleman	2020-01-23	add diff usgage and ss

In this analysis, I will ask if presence or absense of a signal site in either species can explain the diffentially used PAS. I can do overlaps and look at correlations.

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(RColorBrewer)
library(ggpubr)

Loading required package: ggplot2

Loading required package: magrittr

library(tidyverse)

── Attaching packages ───────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0

── Conflicts ──────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()

topSS=c("AATAAA", "ATTAAA")
MetaPAS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSS.txt", header = T, stringsAsFactors = F) %>% mutate(ChimpTopSS=ifelse(ChimpPAS %in% topSS, "Yes", "No"),HumanTopSS=ifelse(HumanPAS %in% topSS, "Yes", "No") )

Pull in the differentiall used PAS:

DiffUsed=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F) %>% rename("Human_NormUse"=Human, "Chimp_NormUse"=Chimp)

Join this by chr, start,end, gene

DiffUsed_anno=DiffUsed %>% inner_join(MetaPAS,by=c("chr","start", "end","gene"))

Ask how many of the effect size either way are those with SS in one species

upreg in human
upred in chimp

DiffUsed_anno_humanup=DiffUsed_anno %>% filter(deltaPAU>0)
nrow(DiffUsed_anno_humanup)

[1] 1030

nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))

[1] 32

DiffUsed_anno_chimpup=DiffUsed_anno %>% filter(deltaPAU<0)
nrow(DiffUsed_anno_chimpup)

[1] 1312

nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))

[1] 32

Significance:

humanOnlyPattern=MetaPAS %>%  filter(HumanTopSS=="Yes", ChimpTopSS=="No")

chimpOnlyPattern=MetaPAS %>%  filter(HumanTopSS=="No", ChimpTopSS=="Yes")

nrow(humanOnlyPattern)+nrow(chimpOnlyPattern)

[1] 720

Of the 44432 we see that 720 have the pattern of interest. We choose 1312 of them and 32 come out. Look to see if it is more than expected by change

Human up reg: phyper(success in sample, sucesss in possible, failure possible, sample size)

up with pattern up reg general no up used all with pattern

#DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No")
x= nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
m= nrow(DiffUsed_anno_humanup)
n=nrow(MetaPAS)-m
k=nrow(humanOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)

[1] 1.366468e-10

(x/k)/(m/N)

[1] 3.823855

[1] 32

This means that of those that have a top signal site only in human there is an enrichment for dAPA PAS.

Try oppostie dir.

x= nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
m= nrow(DiffUsed_anno_humanup)
n=nrow(MetaPAS)-m
k=nrow(chimpOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.0420955

(x/k)/(m/N)

[1] 1.682257

[1] 14

So this is 32 vs 14.

Do this for chimp.

x= nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
m= nrow(DiffUsed_anno_chimpup)
n=nrow(MetaPAS)-m
k=nrow(chimpOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)

[1] 3.906968e-08

(x/k)/(m/N)

[1] 3.018683

Strong enrichment here too:

x= nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
m= nrow(DiffUsed_anno_chimpup)
n=nrow(MetaPAS)-m
k=nrow(humanOnlyPattern)
N=nrow(MetaPAS)


#pval
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.9943171

(x/k)/(m/N)

[1] 0.3752449

[1] 4

No enrichment for this pattern.

This direction has 32 vs 4.

This can be something I use. This suggests that if there are differences in signal site they are likely to lead to differential usage in the expected direction.

Next question: is the pattern enriched in PAS called as dAPA: Are dAPA more likely than expected to have this pattern?

I can use a chisq test of independence.

DiffUsed_annotest= DiffUsed_anno %>% mutate(UpReg=ifelse(deltaPAU< 0, "Chimp", "Human"), Pattern=ifelse(ChimpTopSS=="Yes", ifelse(HumanTopSS=="Yes", "none", "Chimp"), ifelse(HumanTopSS=="Yes", "Human", "none")))  %>% select(PAS, UpReg,Pattern ) %>% filter(Pattern!="none")

DiffUsed_annotest %>% group_by(UpReg, Pattern) %>% summarise(nType=n())

# A tibble: 4 x 3
# Groups:   UpReg [2]
  UpReg Pattern nType
  <chr> <chr>   <int>
1 Chimp Chimp      32
2 Chimp Human       4
3 Human Chimp      14
4 Human Human      32

toTest=DiffUsed_annotest %>% group_by(UpReg, Pattern) %>% summarise(nType=n()) %>% spread(Pattern, nType) %>% column_to_rownames("UpReg")

chisq.test(toTest)


    Pearson's Chi-squared test with Yates' continuity correction

data:  toTest
X-squared = 25.695, df = 1, p-value = 3.998e-07

This is significant

DiffUsed_annotest %>% group_by(UpReg, Pattern) %>% summarise(nType=n()) %>% spread(Pattern, nType)

# A tibble: 2 x 3
# Groups:   UpReg [2]
  UpReg Chimp Human
  <chr> <int> <int>
1 Chimp    32     4
2 Human    14    32

Are these the strongest differences?

DiffUsed_annoPatternAssign= DiffUsed_anno %>% mutate(UpReg=ifelse(deltaPAU< 0, "Chimp", "Human"), Pattern=ifelse(ChimpTopSS=="Yes", ifelse(HumanTopSS=="Yes", "none", "Chimp"), ifelse(HumanTopSS=="Yes", "Human", "none")), ExpectedPattern=ifelse(Pattern!="none", "Yes", "No"))

DiffUsed_annoPatternAssign$ExpectedPattern= as.factor(DiffUsed_annoPatternAssign$ExpectedPattern)
ggplot(DiffUsed_annoPatternAssign, aes(x=ExpectedPattern, y=abs(deltaPAU), fill=ExpectedPattern))+ geom_boxplot()+ geom_jitter(alpha=.1)+stat_compare_means() + scale_fill_brewer(palette = "Set1") + labs(title="delta PAU by signal site in expected direction") + theme(legend.position = "none")

Version	Author	Date
5bdd8c7	brimittleman	2020-05-05

Plot dPAU by presence and absense:

ggplot(DiffUsed_anno_humanup,aes(y=abs(deltaPAU), x=HumanTopSS))+ geom_boxplot()+ stat_compare_means() + labs(title="Human upregualted PAS by presence of Signal")

Version	Author	Date
5bdd8c7	brimittleman	2020-05-05

ggplot(DiffUsed_anno_humanup,aes(x=abs(deltaPAU), by=HumanTopSS, fill=HumanTopSS))+ geom_density(alpha=.5)+  labs(title="Human upregualted PAS by presence of Signal") + scale_fill_discrete(name="Human Signal Site Detected")

Version	Author	Date
5bdd8c7	brimittleman	2020-05-05

ggplot(DiffUsed_anno_chimpup,aes(x=ChimpTopSS, y=abs(deltaPAU))) + geom_boxplot() + stat_compare_means() + labs(title="Chimp upregualted PAS by presence of Signal")

Version	Author	Date
5bdd8c7	brimittleman	2020-05-05

ggplot(DiffUsed_anno_chimpup,aes(x=abs(deltaPAU), by=ChimpTopSS, fill=ChimpTopSS))+ geom_density(alpha=.5)+  labs(title="Chimp upregualted PAS by presence of Signal") + scale_fill_discrete(name="Chimp Signal Site Detected") + annotate("text",label="Wilcoxon, p=0.035",x=.8,y=7.5)

Version	Author	Date
5bdd8c7	brimittleman	2020-05-05

It does not look like presence of a signal within the upregulated matters.

I choose these is the original SS analysis. I used the chooseSignalSite.py it was a hierarchical model

MetaAllSS=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSS.txt",stringsAsFactors = F,header = T)
MetaAllSSsmall=MetaAllSS %>% select(chr, start,end,PAS)

This has every PAS with a signal site. I will add information about if the PAS is differentially used.

DiffUsed_small= DiffUsed %>% select(chr,start,end, gene, deltaPAU, p.adjust) %>% inner_join(MetaAllSSsmall,by=c("chr",'start','end'))

DiffUsed_small_human= DiffUsed_small %>% filter(deltaPAU>0)
DiffUsed_small_chimp= DiffUsed_small %>% filter(deltaPAU<0)

MetaAllSS_diffUsage=MetaAllSS %>% mutate(DiffUsed=ifelse(PAS %in% DiffUsed_small$PAS, "yes","no"), HumanUp=ifelse(PAS %in% DiffUsed_small_human$PAS, "yes","no"), ChimpUp=ifelse(PAS %in% DiffUsed_small_chimp$PAS, "yes","no"))

Look at any vs none.

x= nrow(MetaAllSS_diffUsage %>% filter(HumanUp=="yes",HumanPAS!="None", ChimpPAS=="None"))
m=nrow(MetaAllSS_diffUsage %>% filter(HumanUp=="yes"))
n=nrow(MetaAllSS_diffUsage %>% filter(HumanUp!="yes"))
k= nrow(MetaAllSS_diffUsage %>% filter(HumanPAS!="None", ChimpPAS=="None"))

N=nrow(MetaAllSS_diffUsage)

x

[1] 8

#pval
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.4952338

(x/k)/(m/N)

[1] 1.048945

Chimp:

x= nrow(MetaAllSS_diffUsage %>% filter(ChimpUp=="yes",HumanPAS=="None", ChimpPAS!="None"))
m=nrow(MetaAllSS_diffUsage %>% filter(ChimpUp=="yes"))
n=nrow(MetaAllSS_diffUsage %>% filter(ChimpUp!="yes"))
k= nrow(MetaAllSS_diffUsage %>% filter(HumanPAS=="None", ChimpPAS!="None"))

N=nrow(MetaAllSS_diffUsage)


#pval
phyper(x-1,m,n,k,lower.tail=F)

[1] 0.00935056

(x/k)/(m/N)

[1] 1.858492

[1] 18

Examples:

DiffUsed_annoPatternAssign %>% filter(ExpectedPattern=="Yes") %>% filter(gene=="IRF5")

   chr     start       end gene     logef Chimp_NormUse Human_NormUse
1 chr7 128949203 128949403 IRF5 -1.714254     0.7929789     0.1984335
    deltaPAU     p.adjust         PAS ChimpPAS HumanPAS disc  loc Chimp
1 -0.5945454 1.378802e-07 human332118   AATAAA     None Both utr3 0.745
  Human strandFix ChimpTopSS HumanTopSS UpReg Pattern ExpectedPattern
1  0.24         +        Yes         No Chimp   Chimp             Yes

IRF5 is interesting because it is an apaQTL and associated with SLE

DiffUsed_annoPatternAssign %>% filter(ExpectedPattern=="Yes")

     chr     start       end         gene      logef Chimp_NormUse
1  chr10  59791977  59792177        CCDC6 -2.3609400  3.005115e-01
2  chr10  59792119  59792319        CCDC6 -2.3609400  2.690596e-01
3  chr10  94333212  94333412        NOC3L  2.5737582  1.395679e-02
4  chr10  56357324  56357528        ZWINT  2.9867507  4.384621e-02
5  chr11   7990329   7990528        EIF3F  1.6600320  9.685237e-03
6  chr11  17075090  17075285        RPS13  3.6510661  3.311405e-02
7  chr11 120330451 120330651      TMEM136  2.8684225  9.307083e-03
8  chr12  39550440  39550640        ABCD2 -1.5653061  3.577579e-01
9  chr12  39550557  39550757        ABCD2 -1.5653081  3.530681e-01
10 chr12  53039159  53039359        EIF4B  1.6016557  3.879968e-02
11 chr12 101744014 101744214       GNPTAB -2.9284962  2.053203e-01
12 chr13  40901962  40902162      TPTE2P5  9.1183902  3.623728e-06
13 chr14  64934809  64935009       CHURC1 -1.8499672  4.217093e-01
14 chr14 102348271 102348471         CINP -2.4645587  7.821473e-01
15 chr14  50000053  50000253    LINC01588 -2.6905727  7.602752e-01
16 chr14  23298290  23298490      PPP1R3E -1.6196699  3.536809e-01
17 chr16   8860271   8860471 LOC100130283 -2.4140099  9.421233e-01
18 chr16    108973    109173        NPRL3  2.4926529  7.582583e-03
19 chr16  47629989  47630187         PHKB  2.4095846  2.438840e-02
20 chr16  84178370  84178570        TAF1C -0.9335106  8.117466e-01
21 chr17  80416138  80416342        ENDOV -3.2326827  2.168166e-01
22 chr17  45219191  45219391    LOC339192  1.2652021  1.002842e-01
23 chr17  45219299  45219499    LOC339192  1.7679577  5.942880e-02
24 chr17  10672464  10672664         SCO1  1.6035833  4.692105e-02
25 chr17  76564801  76565001       SNHG16 -4.0679028  2.560004e-01
26 chr17  42341112  42341312        STAT3  1.3694848  1.660558e-02
27 chr17   9568370   9568570         STX8  0.5530410  4.014333e-01
28 chr17  15976552  15976752       ZSWIM7  1.1002929  2.426421e-01
29 chr18  31670759  31670966      B4GALT6  2.5762130  2.508008e-02
30 chr18   6034045   6034245      L3MBTL4  1.6959583  2.656275e-02
31 chr18   6034146   6034346      L3MBTL4  3.2729452  4.949802e-03
32 chr18  11884037  11884237        MPPE1  5.3563331  1.650931e-01
33 chr18  48928450  48928650        SMAD7  1.0429344  1.679741e-01
34 chr19  10653339  10653539     ILF3-AS1 -0.7889717  4.240225e-01
35 chr19  11322053  11322251        RAB3D -2.3110910  9.974328e-01
36 chr19  14709065  14709265       ZNF333  7.3295004  1.375701e-04
37  chr1  97573839  97574039         DPYD  0.6882693  2.794376e-01
38  chr1  41554236  41554436       HIVEP3  0.5290050  3.796336e-01
39  chr1  58659457  58659657        MYSM1 -0.7991334  7.855372e-01
40  chr1   7916424   7916624      TNFRSF9 -1.6248354  3.151728e-01
41  chr1 184073162 184073362       TSEN15 -3.2680965  3.351936e-01
42 chr21  39397521  39397721          WRB -1.5379273  5.270167e-01
43 chr22  36226606  36226806        APOL2  2.2037870  1.549809e-01
44 chr22  50583690  50583890     CHKB-AS1 -5.7499635  9.076967e-01
45 chr22  41525810  41526010       POLR3H  4.1856954  3.864910e-03
46 chr22  43987490  43987690       SAMM50  1.1487742  5.352321e-02
47 chr22  43166605  43166805       TTLL12  2.2170081  5.578125e-01
48  chr2  61868486  61868686         CCT4  1.1603600  3.504263e-01
49  chr2  36596154  36596354         FEZ2 -1.9605042  2.616566e-01
50  chr2  86137954  86138154        PTCD3 -1.7417538  8.005474e-01
51  chr3  52295069  52295269       GLYCTK -1.3090994  4.640044e-01
52  chr3  44748734  44748934     KIAA1143  0.9663980  6.127824e-01
53  chr3  42224942  42225142        TRAK1  2.8810608  1.046848e-02
54  chr4   2965043   2965243         GRK4 -2.9629678  5.762209e-01
55  chr4 184262890 184263090 LOC105377582  3.4983843  3.613872e-03
56  chr4   6621293   6621496       MAN2B2 -3.3828360  2.921627e-01
57  chr4   6622218   6622418       MAN2B2  3.3828360  7.078373e-01
58  chr4 177362571 177362770        NEIL3 -2.6347855  3.592382e-01
59  chr4 112276066 112276266         TIFA -2.4424029  3.988241e-01
60  chr4 112277069 112277269         TIFA  3.3341566  1.073268e-02
61  chr5 116764662 116764862 LOC105379133  2.5059431  1.560275e-02
62  chr5  50837096  50837296        PARP8 -1.9621658  2.925939e-01
63  chr5 115207208 115207408       PGGT1B -3.7300743  5.716163e-01
64  chr7  94525342  94525542        CASD1 -1.5620435  2.928990e-01
65  chr7 150743396 150743596       GIMAP5 -1.5555936  9.626590e-01
66  chr7 128949203 128949403         IRF5 -1.7142543  7.929789e-01
67  chr7 128501913 128502113      METTL2B 86.7470224  1.238577e-46
68  chr7 154940707 154940907   PAXIP1-AS2  1.1509956  1.268497e-01
69  chr7  26319693  26319893        SNX10  1.3561739  8.649664e-02
70  chr7  64666552  64666752       ZNF107  1.2930001  2.675643e-01
71  chr8  56218433  56218633       CHCHD7  1.2823808  5.763660e-02
72  chr8  38266967  38267167        PLPP5  1.8956237  2.942507e-02
73  chr8  66430537  66430737         RRS1  4.2698789  1.223870e-01
74  chr8 123434424 123434624       WDYHV1  1.0204941  1.629903e-02
75  chr9  38398476  38398676      ALDH1B1  2.9760684  2.192423e-02
76  chr9  32455290  32455490        DDX58 -2.8803484  2.904042e-01
77  chr9  37780439  37780639       EXOSC3  3.7169339  7.118665e-03
78  chr9 131260069 131260269       FAM78A  3.3760639  5.895334e-03
79  chr9   7013620   7013820        KDM4C -0.5494084  5.974038e-01
80  chr9  96942896  96943096      MFSD14C -3.2640911  7.922608e-01
81  chr9  68758484  68758684      PIP5K1B  1.8303006  1.429333e-01
82  chr9  69254568  69254768         TJP2 -2.0233322  5.517031e-01
   Human_NormUse   deltaPAU     p.adjust         PAS ChimpPAS HumanPAS
1    0.029688752 -0.2708228 2.931533e-14  human42127   AATAAA   GATAAA
2    0.026581493 -0.2424782 2.931533e-14  human42128   AATAAA   GATAAA
3    0.221833069  0.2078763 3.129175e-10  human47245   TATAAA   AATAAA
4    0.599243032  0.5553968 7.819144e-11  chimp37493   CATAAA   AATAAA
5    0.253829117  0.2441439 1.515433e-31  chimp48255   AAAAAG   AATAAA
6    0.481907112  0.4487931 1.796871e-18  chimp49415   ATTAAA   AATAGA
7    0.226966170  0.2176591 1.183898e-05  human68497   AATACA   AATAAA
8    0.049583712 -0.3081742 1.292217e-18  human75673   AATAAA   TATAAA
9    0.048933627 -0.3041345 1.292217e-18  human75674   AATAAA   TATAAA
10   0.247439019  0.2086393 4.135123e-21  human78417   AAAAAG   AATAAA
11   0.003461142 -0.2018592 1.519610e-08  chimp76729   ATTAAA   AAAAAG
12   0.234475753  0.2344721 1.142842e-16  human93560   AATAAA   AAAAAA
13   0.061588273 -0.3601210 1.403654e-07 human106913   AATAAA     None
14   0.458020725 -0.3241266 1.970367e-08 human113710   AATAAA   AGTAAA
15   0.185824945 -0.5744502 6.873670e-13 human103761   AATAAA   AAAAAG
16   0.077052211 -0.2766287 1.027702e-06 human101471   ATTAAA   AGTAAA
17   0.399504724 -0.5426185 4.138201e-12 human128258   AATAAA     None
18   0.410163454  0.4025809 6.897499e-15 human127082   AATAAA   AAAAAA
19   0.249759119  0.2253707 2.460731e-08 chimp120785   AATAAA   AAAAAG
20   0.399962374 -0.4117842 3.078559e-04 human137266   AATAAA   AATACA
21   0.009984516 -0.2068321 3.415796e-08 chimp138281   ATTAAA   AAAAAG
22   0.336828362  0.2365442 2.426360e-14 human146220   AAAAAA   AATAAA
23   0.330002478  0.2705737 2.426360e-14 human146221   AATATA   AATAAA
24   0.353004999  0.3060839 1.164238e-08 human140767   AATATA   AATAAA
25   0.001984361 -0.2540160 1.743510e-11 human151744   ATTAAA     None
26   0.226572298  0.2099667 7.991078e-17 human145411   AATAAA   AATACA
27   0.612640317  0.2112070 1.767399e-06 human140708   AATAAA   AAAAAG
28   0.743136928  0.5004949 2.668329e-05 human141183   AATAAA   AAAAAG
29   0.361507752  0.3364277 3.878083e-13 chimp141788     None   AATAAA
30   0.228500404  0.2019377 3.719513e-08 human154026   AAAAAG   ATTAAA
31   0.206100201  0.2011504 3.719513e-08 human154027     None   ATTAAA
32   0.371362637  0.2062696 1.536772e-15 human154969   AATATA   AATAAA
33   0.407530026  0.2395559 6.910573e-06 human158441   AAAAAG   ATTAAA
34   0.097168573 -0.3268540 5.685956e-05 chimp148004   AATAAA   AAAAAA
35   0.792522578 -0.2049103 2.475007e-06 chimp148176   ATTAAA   AATATA
36   0.321398908  0.3212613 1.021124e-05 human164763   TATAAA   ATTAAA
37   0.497489922  0.2180524 3.670463e-02  human15760   ATTAAA   AGTAAA
38   0.638048984  0.2584154 8.451722e-03   human7817   AATAAA   AAAAAG
39   0.529364094 -0.2561731 4.001118e-07  human11130   TATAAA   AATAAA
40   0.091360493 -0.2238123 2.834969e-11    human946   AATAAA     None
41   0.018404425 -0.3167892 1.016000e-21  human27322   ATTAAA   AAAAAG
42   0.316993022 -0.2100237 6.879604e-06 human216650   ATTAAA     None
43   0.502876942  0.3478961 7.881703e-17 human220876   AATAGA   AATAAA
44   0.067932560 -0.8397641 6.803444e-16 human223984   AATAAA     None
45   0.420718345  0.4168534 1.981796e-08 human222310   CATAAA   AATAAA
46   0.318694839  0.2651716 1.611561e-07 chimp170220   AAAAAA   AATAAA
47   0.960391666  0.4025791 4.602264e-09 human222781   AGTAAA   AATAAA
48   0.653165873  0.3027396 1.422515e-19 human180692   ACTAAA   ATTAAA
49   0.042588818 -0.2190677 6.128312e-14 chimp175522   AATAAA     None
50   0.385666603 -0.4148808 2.756441e-23 human184440   AATAAA   GATAAA
51   0.155458822 -0.3085456 1.565425e-04 human232119   ATTAAA   CATAAA
52   0.870864041  0.2580816 7.302724e-06 human230236   AGTAAA   AATAAA
53   0.257218512  0.2467500 1.323332e-09 human229740   AATACA   AATAAA
54   0.020425664 -0.5557953 1.510347e-08 chimp225097   AATAAA   TATAAA
55   0.228630169  0.2250163 7.523453e-13 human269898   ACTAAA   AATAAA
56   0.000475544 -0.2916871 5.335902e-07 chimp226080   AATAAA     None
57   0.999524456  0.2916871 5.335902e-07 human252668   AATATA   ATTAAA
58   0.060563650 -0.2986745 6.677528e-14 chimp241347   AATAAA   GATAAA
59   0.034929825 -0.3638943 3.411424e-26 human263460   ATTAAA   GATAAA
60   0.303284936  0.2925523 3.411424e-26 human263464   AATAGA   AATAAA
61   0.216748486  0.2011457 8.043422e-10 human284149   AGTAAA   AATAAA
62   0.038153214 -0.2544407 1.883815e-10 human274281   AATACA   AATAAA
63   0.024719978 -0.5468963 2.800936e-28 human283660   AATAAA   AAAAAG
64   0.073882166 -0.2190168 6.119732e-08 human327152     None   ATTAAA
65   0.716818338 -0.2458406 3.921844e-04 human335952   AATAAA   AATAGA
66   0.198433494 -0.5945454 1.378802e-07 human332118   AATAAA     None
67   0.236258829  0.2362588 3.417617e-08 human331963   AATAAA   AAAAAA
68   0.336660738  0.2098110 2.568654e-03 human336415   ATTAAA     None
69   0.289868160  0.2033715 1.096447e-02 human319251   AATAAA   AATACA
70   0.564699240  0.2971350 4.571642e-09 human322931   ATTAAA     None
71   0.262164559  0.2045280 1.174527e-07 human342580   AAAAAG   AATAAA
72   0.320799631  0.2913746 9.257145e-08 human340756   AGTAAA   AATAAA
73   0.953092700  0.8307057 1.396969e-19 human343900     None   AATAAA
74   0.233009003  0.2167100 1.874475e-10 human350495   AATAAA   AATACA
75   0.592498971  0.5705747 3.235054e-12 human358107   AAAAAA   AATAAA
76   0.015903914 -0.2745003 7.639730e-14 human356373   ATTAAA   TATAAA
77   0.291803043  0.2846844 9.180813e-11 human357933   AAAAAA   AATAAA
78   0.318067048  0.3121717 1.181769e-07 human367259     None   AATAAA
79   0.330891102 -0.2665127 2.488868e-04 human354937   ATTAAA   AAAAAA
80   0.148333465 -0.6439273 2.937875e-12 human361600   TATAAA   AATAAA
81   0.692787511  0.5498542 4.702463e-10 chimp320159   AAAAAG   AATAAA
82   0.117819464 -0.4338837 2.405803e-13 human358855   AATAAA   AAAAAG
    disc    loc        Chimp Human strandFix ChimpTopSS HumanTopSS UpReg
1   Both   utr3 0.2116666667 0.006         -        Yes         No Chimp
2   Both   utr3 0.1800000000 0.002         -        Yes         No Chimp
3   Both   utr3 0.0141666667 0.136         -         No        Yes Human
4  Chimp   utr3 0.0508333333 0.402         -         No        Yes Human
5  Chimp intron 0.0058333333 0.168         +         No        Yes Human
6  Chimp    cds 0.0450000000 0.480         -        Yes         No Human
7  Human   utr3 0.0041666667 0.250         +         No        Yes Human
8  Human   utr3 0.2425000000 0.016         -        Yes         No Chimp
9   Both   utr3 0.2425000000 0.014         -        Yes         No Chimp
10 Human    cds 0.0275000000 0.182         +         No        Yes Human
11 Chimp    end 0.1191666667 0.000         -        Yes         No Chimp
12 Human intron 0.0008333333 0.120         -        Yes         No Human
13  Both   utr3 0.3875000000 0.070         +        Yes         No Chimp
14  Both   utr3 0.7600000000 0.440         -        Yes         No Chimp
15  Both   utr3 0.7191666667 0.176         -        Yes         No Chimp
16  Both   utr3 0.4025000000 0.062         -        Yes         No Chimp
17  Both    end 0.9408333333 0.392         +        Yes         No Chimp
18 Human intron 0.0033333333 0.326         -        Yes         No Human
19 Chimp intron 0.0000000000 0.050         +        Yes         No Human
20  Both   utr3 0.8191666667 0.392         -        Yes         No Chimp
21 Chimp intron 0.1816666667 0.008         +        Yes         No Chimp
22 Human intron 0.0683333333 0.298         -         No        Yes Human
23  Both intron 0.0366666667 0.290         -         No        Yes Human
24  Both   utr3 0.0233333333 0.268         -         No        Yes Human
25  Both   utr5 0.2008333333 0.002         +        Yes         No Chimp
26  Both intron 0.0058333333 0.076         -        Yes         No Human
27  Both   utr3 0.1841666667 0.286         -        Yes         No Human
28  Both   utr3 0.1416666667 0.656         -        Yes         No Human
29 Chimp intron 0.0083333333 0.162         -         No        Yes Human
30 Human intron 0.0033333333 0.056         -         No        Yes Human
31 Human intron 0.0000000000 0.050         -         No        Yes Human
32 Human   utr3 0.1383333333 0.296         -         No        Yes Human
33 Human intron 0.0941666667 0.320         -         No        Yes Human
34 Chimp   utr3 0.3900000000 0.012         -        Yes         No Chimp
35 Chimp   utr3 0.8716666667 0.710         -        Yes         No Chimp
36 Human intron 0.0000000000 0.310         +         No        Yes Human
37  Both    cds 0.0525000000 0.068         -        Yes         No Human
38  Both    end 0.0400000000 0.072         -        Yes         No Human
39  Both   utr3 0.4400000000 0.212         -         No        Yes Chimp
40  Both   utr3 0.2275000000 0.084         -        Yes         No Chimp
41  Both   utr3 0.4100000000 0.014         +        Yes         No Chimp
42  Both   utr3 0.5166666667 0.298         +        Yes         No Chimp
43 Human   utr3 0.1325000000 0.424         -         No        Yes Human
44  Both   utr5 0.9300000000 0.050         +        Yes         No Chimp
45 Human   utr3 0.0033333333 0.338         -         No        Yes Human
46 Chimp intron 0.0283333333 0.282         +         No        Yes Human
47  Both   utr3 0.5400000000 0.958         -         No        Yes Human
48  Both   utr3 0.3333333333 0.548         -         No        Yes Human
49 Chimp intron 0.1175000000 0.028         -        Yes         No Chimp
50  Both   utr3 0.6925000000 0.280         +        Yes         No Chimp
51  Both   utr3 0.3833333333 0.152         +        Yes         No Chimp
52  Both   utr3 0.4941666667 0.738         -         No        Yes Human
53  Both   utr3 0.0016666667 0.116         +         No        Yes Human
54 Chimp intron 0.5425000000 0.002         +        Yes         No Chimp
55 Human intron 0.0008333333 0.130         -         No        Yes Human
56 Chimp   utr3 0.2741666667 0.000         +        Yes         No Chimp
57  Both   utr3 0.5491666667 0.940         +         No        Yes Human
58 Chimp   utr3 0.3208333333 0.048         +        Yes         No Chimp
59  Both   utr3 0.2841666667 0.018         -        Yes         No Chimp
60 Human   utr3 0.0050000000 0.184         -         No        Yes Human
61 Human intron 0.0200000000 0.162         +         No        Yes Human
62 Human    end 0.0558333333 0.006         +         No        Yes Chimp
63  Both   utr3 0.5016666667 0.024         -        Yes         No Chimp
64  Both intron 0.1725000000 0.042         +         No        Yes Chimp
65  Both   utr3 0.9316666667 0.694         +        Yes         No Chimp
66  Both   utr3 0.7450000000 0.240         +        Yes         No Chimp
67 Human   utr3 0.0000000000 0.210         +        Yes         No Human
68  Both intron 0.1008333333 0.288         +        Yes         No Human
69  Both intron 0.0341666667 0.164         +        Yes         No Human
70  Both intron 0.1458333333 0.336         +        Yes         No Human
71  Both   utr3 0.0450000000 0.248         +         No        Yes Human
72  Both   utr3 0.0233333333 0.274         -         No        Yes Human
73  Both   utr3 0.0925000000 0.926         +         No        Yes Human
74 Human intron 0.0083333333 0.256         +        Yes         No Human
75  Both   utr3 0.0116666667 0.558         +         No        Yes Human
76  Both   utr3 0.2850000000 0.026         -        Yes         No Chimp
77  Both   utr3 0.0066666667 0.248         -         No        Yes Human
78 Human   utr3 0.0116666667 0.292         -         No        Yes Human
79  Both    cds 0.0625000000 0.032         +        Yes         No Chimp
80  Both   utr3 0.6750000000 0.158         -         No        Yes Chimp
81 Chimp intron 0.0208333333 0.060         +         No        Yes Human
82  Both   utr3 0.4800000000 0.076         +        Yes         No Chimp
   Pattern ExpectedPattern
1    Chimp             Yes
2    Chimp             Yes
3    Human             Yes
4    Human             Yes
5    Human             Yes
6    Chimp             Yes
7    Human             Yes
8    Chimp             Yes
9    Chimp             Yes
10   Human             Yes
11   Chimp             Yes
12   Chimp             Yes
13   Chimp             Yes
14   Chimp             Yes
15   Chimp             Yes
16   Chimp             Yes
17   Chimp             Yes
18   Chimp             Yes
19   Chimp             Yes
20   Chimp             Yes
21   Chimp             Yes
22   Human             Yes
23   Human             Yes
24   Human             Yes
25   Chimp             Yes
26   Chimp             Yes
27   Chimp             Yes
28   Chimp             Yes
29   Human             Yes
30   Human             Yes
31   Human             Yes
32   Human             Yes
33   Human             Yes
34   Chimp             Yes
35   Chimp             Yes
36   Human             Yes
37   Chimp             Yes
38   Chimp             Yes
39   Human             Yes
40   Chimp             Yes
41   Chimp             Yes
42   Chimp             Yes
43   Human             Yes
44   Chimp             Yes
45   Human             Yes
46   Human             Yes
47   Human             Yes
48   Human             Yes
49   Chimp             Yes
50   Chimp             Yes
51   Chimp             Yes
52   Human             Yes
53   Human             Yes
54   Chimp             Yes
55   Human             Yes
56   Chimp             Yes
57   Human             Yes
58   Chimp             Yes
59   Chimp             Yes
60   Human             Yes
61   Human             Yes
62   Human             Yes
63   Chimp             Yes
64   Human             Yes
65   Chimp             Yes
66   Chimp             Yes
67   Chimp             Yes
68   Chimp             Yes
69   Chimp             Yes
70   Chimp             Yes
71   Human             Yes
72   Human             Yes
73   Human             Yes
74   Chimp             Yes
75   Human             Yes
76   Chimp             Yes
77   Human             Yes
78   Human             Yes
79   Chimp             Yes
80   Human             Yes
81   Human             Yes
82   Chimp             Yes

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     
 [4] purrr_0.3.2        readr_1.3.1        tidyr_0.8.3       
 [7] tibble_2.1.1       tidyverse_1.2.1    ggpubr_0.2        
[10] magrittr_1.5       ggplot2_3.1.1      RColorBrewer_1.1-2
[13] 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    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.4.6     promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.1.0        tools_3.5.1      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

Can signal site explain differential usage

Briana Mittleman

1/23/2020