Last updated: 2020-05-20

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.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/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
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    Modified:   analysis/signalsites_doublefilter.Rmd
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library(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()
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggpubr':

    get_legend
The following object is masked from 'package:ggplot2':

    ggsave

Packages/functions for this:

vegan: diversity, can calculate shannon or simpson

I will probably do this in python because I can go gene by gene easier:

scipy stats example

This is good because I will be able to change the base and see how it effects the measurements

https://kite.com/python/docs/scipy.stats.entropy

default base is e

from scipy.stats import entropy
import numpy as np
from math import log, e
entropy([1/2, 1/2], base=2)
  
#shannon 
Shannon2 = -np.sum(pA*np.log2(pA))

I most likely want to use a uniform prior. for this. I could get more complicated in the future by weighting differences by utr and intron. this would help find “more surpising” results.

simpson- squares the probability

from ecopy import diversity 

diversity(x, medod="simpson")


#x- side x species matrix, sites are rows, columns are species - ie column counts, row == pas
library(vegan)
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-3
data(BCI)
dim(BCI)
[1]  50 225
H <- diversity(BCI)
length(H)
[1] 50
diversity(c(.5,.5,.5))
[1] 1.098612
diversity(c(.25,.75,.25))
[1] 0.9502705
#more peak= lower 


diversity(c(.5,.5,.5), "simpson")
[1] 0.6666667
diversity(c(.25,.75,.25),"simpson")
[1] 0.56
#more peak= lower


diversity(c(.5,.5,.5), "inv")
[1] 3
diversity(c(.25,.75,.25),"inv")
[1] 2.272727

Seem like it is most simple to use the mean usages for this.
##Shannon

First test:

use entropy in python with different bases. -base 2 is the classic shannon and it uses the - when probabilities are given (ie uniform prior)

the python code will work with my meta file for now and take species as an input.

\(H=-\sum^{s}_{i=1}p_{i}log_{2}p_{i}\)

\(H=-\sum^{s}_{i=1}p_{i}lnp_{i}\)

mkdir ../data/InfoContent

python InfoContentShannon.py Human
python InfoContentShannon.py Chimp

Results:

HumanResInfo= read.table("../data/InfoContent/Human_InfoContent.txt", header = T,stringsAsFactors = F) %>% rename(Human_Base2=base2, Human_basee= basee)
ChimpResInfo= read.table("../data/InfoContent/Chimp_InfoContent.txt", header = T,stringsAsFactors = F) %>% rename(Chimp_Base2=base2, Chimp_basee= basee)

BothResInfo= HumanResInfo %>% inner_join(ChimpResInfo, by=c("gene", "numPAS")) %>% filter(numPAS > 1)

First plot the distributions:

BothResInfo_2= BothResInfo %>% select(gene, contains("Base2")) %>% gather("species", "base2", -gene)

ggplot(BothResInfo_2, aes(x=base2, fill=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Set1")+ labs(title="Shannon Information Content")
Warning: Removed 1 rows containing non-finite values (stat_density).

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wilcox.test(BothResInfo$Human_Base2, BothResInfo$Chimp_Base2, alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  BothResInfo$Human_Base2 and BothResInfo$Chimp_Base2
W = 39254000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0

Human shift higher, ie less density:

BothResInfo_e= BothResInfo %>% select(gene, contains("basee")) %>% gather("species", "basee", -gene)

ggplot(BothResInfo_e, aes(x=basee, fill=species)) + geom_density(alpha=.3)
Warning: Removed 1 rows containing non-finite values (stat_density).

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I want to look at this by dominance:

ggplot(BothResInfo_2,aes(x=base2, fill=species)) + geom_histogram() + facet_grid(~species)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Warning: Removed 1 rows containing non-finite values (stat_bin).

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Plot human vs chimp:

ggplot(BothResInfo,aes(x=Human_Base2,y= Chimp_Base2 )) + geom_point() + geom_abline(slope=1, intercept = 0) + stat_cor(col="blue") + geom_density_2d(col="blue")
Warning: Removed 1 rows containing non-finite values (stat_cor).
Warning: Removed 1 rows containing non-finite values (stat_density2d).
Warning: Removed 1 rows containing missing values (geom_point).

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ggplot(BothResInfo,aes(x=Human_Base2,y= Chimp_Base2 ,col=numPAS)) + geom_point(alpha=.4) + geom_abline(slope=1, intercept = 0) +labs(title="Shannon Index Colored by number of PAS")
Warning: Removed 1 rows containing missing values (geom_point).

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Does number explain:

summary(lm(BothResInfo$Human_Base2 ~BothResInfo$numPAS))

Call:
lm(formula = BothResInfo$Human_Base2 ~ BothResInfo$numPAS)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.88694 -0.20022  0.06073  0.23527  0.53990 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.209838   0.008559   24.52   <2e-16 ***
BothResInfo$numPAS 0.314404   0.001527  205.93   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3166 on 8448 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.8339,    Adjusted R-squared:  0.8339 
F-statistic: 4.241e+04 on 1 and 8448 DF,  p-value: < 2.2e-16
summary(lm(BothResInfo$Chimp_Base2 ~BothResInfo$numPAS ))

Call:
lm(formula = BothResInfo$Chimp_Base2 ~ BothResInfo$numPAS)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.76846 -0.25131  0.06184  0.27657  0.67339 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.108685   0.010226   10.63   <2e-16 ***
BothResInfo$numPAS 0.307373   0.001824  168.49   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3784 on 8449 degrees of freedom
Multiple R-squared:  0.7706,    Adjusted R-squared:  0.7706 
F-statistic: 2.839e+04 on 1 and 8449 DF,  p-value: < 2.2e-16

So this is working but the number of PAS explains most of the variation. Maybe I can normalize this out and look at residuals:

BothResInfoRes= BothResInfo %>% mutate(HumanNorm=residuals(BothResInfo$Human_Base2~BothResInfo$numPAS),ChimpNorm=residuals(BothResInfo$Chimp_Base2~BothResInfo$numPAS))

pull in dominance:

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_10=BothRes %>% filter(Chimp_DiffDom >=0.1 | Human_DiffDom>=0.1) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=10) 
BothRes_20=BothRes %>% filter(Chimp_DiffDom >=0.2 | Human_DiffDom>=0.2) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=20)
BothRes_30=BothRes %>% filter(Chimp_DiffDom >=0.3 | Human_DiffDom>=0.3) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=30)
BothRes_40=BothRes %>% filter(Chimp_DiffDom >=0.4 | Human_DiffDom>=0.4) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=40)
BothRes_50=BothRes %>% filter(Chimp_DiffDom >=0.5 | Human_DiffDom>=0.5) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=50)
BothRes_60=BothRes %>% filter(Chimp_DiffDom >=0.6 | Human_DiffDom>=0.6) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=60)
BothRes_70=BothRes %>% filter(Chimp_DiffDom >=0.7 | Human_DiffDom>=0.7) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=70)
BothRes_80=BothRes %>% filter(Chimp_DiffDom >=0.8 | Human_DiffDom>=0.8) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=80)
BothRes_90=BothRes %>% filter(Chimp_DiffDom >=0.9 | Human_DiffDom>=0.9) %>% mutate(Set= ifelse(Human_PAS==Chimp_PAS,"Same", "Different"),cut=90)

BothResAll=BothRes_10 %>% bind_rows(BothRes_20) %>% bind_rows(BothRes_30) %>% bind_rows(BothRes_40) %>% bind_rows(BothRes_50) %>% bind_rows(BothRes_60) %>% bind_rows(BothRes_70) %>% bind_rows(BothRes_80) %>% bind_rows(BothRes_90)

I want dominance in 1 or both at .4.

BothRes_40_each= BothRes_40 %>% mutate(Dom=ifelse(Human_DiffDom>=.4, ifelse(Chimp_DiffDom >=.4, "Both", "Human"), "Chimp"))

BothRes_40_each %>% group_by(Dom) %>% summarise(n())
# A tibble: 3 x 2
  Dom   `n()`
  <chr> <int>
1 Both   1565
2 Chimp   906
3 Human   257
BothRes_40_each %>% group_by(Set,Dom) %>% summarise(n())
# A tibble: 6 x 3
# Groups:   Set [2]
  Set       Dom   `n()`
  <chr>     <chr> <int>
1 Different Both     22
2 Different Chimp   114
3 Different Human    46
4 Same      Both   1543
5 Same      Chimp   792
6 Same      Human   211
BothRes_40_eachsm= BothRes_40_each %>% select(gene, Set, Dom)


BothResInfoDom= BothResInfo %>% full_join(BothRes_40_eachsm, by="gene", fill="None") %>%  mutate(Set= replace_na(Set, "None"),Dom= replace_na(Dom, "None"))


ggplot(BothResInfoDom,aes(x=Human_Base2,y= Chimp_Base2, col=Dom )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + scale_color_brewer(palette = "Set2") + labs(x="Human Information", y="Chimp Information", title="Shannon Information Index colored by whether gene has a dominant PAS")
Warning: Removed 1 rows containing missing values (geom_point).

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ggplot(BothResInfoDom,aes(x=Human_Base2,y= Chimp_Base2, col=Set )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + scale_color_brewer(palette = "Set2") +geom_density2d()+ labs(x="Human Information", y="Chimp Information", title="Shannon Information Index colored by Dominance Structure ")
Warning: Removed 1 rows containing non-finite values (stat_density2d).

Warning: Removed 1 rows containing missing values (geom_point).

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BothResInfoDom$numPAS=as.factor(BothResInfoDom$numPAS)
ggplot(BothResInfoDom,aes(x=Human_Base2,y= Chimp_Base2, col=numPAS )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + labs(x="Human Information", y="Chimp Information", title="Shannon Information Index colored by number of PAS") + facet_grid(~Dom)
Warning: Removed 1 rows containing missing values (geom_point).

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#+ scale_color_brewer(palette = "Spectral")

Dominance and number of PAS:

BothResInfoDom$numPAS=as.numeric(as.character(BothResInfoDom$numPAS))
ggplot(BothResInfoDom,aes(x=Dom, y=numPAS)) +geom_boxplot() +stat_compare_means() +  labs(x="Dominance Structure",y="Number of PAS", title="Number of PAS differ by dominance structure")

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ggplot(BothResInfoDom,aes(x=Set, y=numPAS)) +geom_boxplot() +stat_compare_means() +  labs(x="Dominance Structure",y="Number of PAS", title="Number of PAS differ by dominance structure")

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Ratio problem!!!!

but the confounder is biological- number of PAS.

Simpson

Try the simpson index.

skit-bio: http://scikit-bio.org/docs/0.1.3/math.diversity.alpha.html

\(D=\sum_{i=1}^{R}p_{i}^{2}\)

and

\(D=1-\sum_{i=1}^{R}p_{i}^{2}\)

python infoContentSimpson.py Human
python infoContentSimpson.py Chimp
SimpHuman=read.table("../data/InfoContent/Human_SimpsonInfoContent.txt", header = T, stringsAsFactors = F) %>% rename(simpson_Human=simpson) %>% mutate(simpOpp_Human=1-simpson_Human)
SimpChimp=read.table("../data/InfoContent/Chimp_SimpsonInfoContent.txt", header = T, stringsAsFactors = F)%>% rename(simpson_Chimp=simpson)%>% mutate(simpOpp_Chimp=1-simpson_Chimp)

BothSimp= SimpHuman %>% inner_join(SimpChimp, by=c("gene", "numPAS")) %>% filter(numPAS > 1)

Gather and plot:

BothSimp_g= BothSimp %>% select(-contains("Opp")) %>% gather("species", "Simpson", -gene, -numPAS)
ggplot(BothSimp_g, aes(x=Simpson, fill=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Set1")+labs(title="Simpson Index")

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BothOppSimp_g= BothSimp %>% select(-contains("simpson")) %>% gather("species", "SimpsonOpp", -gene, -numPAS)

ggplot(BothOppSimp_g, aes(x=SimpsonOpp, fill=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Set1")+labs(title="Simpson Index (1-opp)")

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wilcox.test(BothSimp$simpOpp_Human, BothSimp$simpOpp_Chimp, alternative = "greater")

    Wilcoxon rank sum test with continuity correction

data:  BothSimp$simpOpp_Human and BothSimp$simpOpp_Chimp
W = 40925000, p-value < 2.2e-16
alternative hypothesis: true location shift is greater than 0

Histogram:

ggplot(BothSimp_g,aes(x=Simpson, fill=species)) + geom_histogram() + facet_grid(~species)+scale_fill_brewer(palette = "Set1")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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ggplot(BothOppSimp_g, aes(x=SimpsonOpp, fill=species)) + geom_histogram() + facet_grid(~species)+scale_fill_brewer(palette = "Set1")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

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here, higher index is lower diversity= more dominance (opposite of shannon)

the opposite one is 1- sum. this is more dominance at lower values like shannon. I will go with this so the sign is the same.

BothInfoTypes=BothSimp %>% inner_join(BothResInfoRes, by=c("gene", "numPAS"))

BothInfoTypes_h=BothInfoTypes %>% select(gene,numPAS, simpOpp_Human, Human_Base2) %>% mutate(species="Human") %>% rename(Simpson= simpOpp_Human, Shannon=Human_Base2)
BothInfoTypes_c=BothInfoTypes %>% select(gene,numPAS, simpOpp_Chimp, Chimp_Base2) %>% mutate(species="Chimp")%>% rename(Simpson= simpOpp_Chimp, Shannon=Chimp_Base2)

BothInfoTypes_both=BothInfoTypes_h %>% bind_rows(BothInfoTypes_c)

ggplot(BothInfoTypes_both,aes(x=Simpson, y=Shannon, by=species, col=species)) +geom_point(alpha=.4) +geom_density2d(col="black") +  stat_cor(label.x=0) + geom_smooth(col="black",method = "lm") + facet_grid(~species) + labs(title="Correlation between Indicies") +theme(legend.position = "none")+scale_color_brewer(palette = "Set1")
Warning: Removed 1 rows containing non-finite values (stat_density2d).
Warning: Removed 1 rows containing non-finite values (stat_cor).
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing missing values (geom_point).

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There is more variation at the low end here.

Compare human and chimp simpson by PAS number:

ggplot(BothInfoTypes,aes(x=simpOpp_Human,y= simpOpp_Chimp)) + geom_point(alpha=.4) + geom_abline(slope=1, intercept = 0)+labs(title="Simpson Index") + stat_cor(col="blue")+ geom_density_2d(col="blue")

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ggplot(BothInfoTypes,aes(x=simpOpp_Human,y= simpOpp_Chimp ,col=numPAS)) + geom_point(alpha=.4) + geom_abline(slope=1, intercept = 0)+labs(title="Simpson Index Colored by number of PAS")

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summary(lm(BothInfoTypes$simpOpp_Human ~BothResInfo$numPAS))

Call:
lm(formula = BothInfoTypes$simpOpp_Human ~ BothResInfo$numPAS)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.94165 -0.08859  0.00961  0.10528  0.43248 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.4483250  0.0046537   96.34   <2e-16 ***
BothResInfo$numPAS 0.0595955  0.0008302   71.78   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1722 on 8449 degrees of freedom
Multiple R-squared:  0.3788,    Adjusted R-squared:  0.3788 
F-statistic:  5153 on 1 and 8449 DF,  p-value: < 2.2e-16
cor.test(BothInfoTypes$simpOpp_Human,BothResInfo$numPAS)

    Pearson's product-moment correlation

data:  BothInfoTypes$simpOpp_Human and BothResInfo$numPAS
t = 71.784, df = 8449, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6020812 0.6285731
sample estimates:
     cor 
0.615501 
summary(lm(BothInfoTypes$simpOpp_Chimp ~BothResInfo$numPAS ))

Call:
lm(formula = BothInfoTypes$simpOpp_Chimp ~ BothResInfo$numPAS)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.79002 -0.10111  0.01491  0.11286  0.50115 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.3685223  0.0049596   74.31   <2e-16 ***
BothResInfo$numPAS 0.0651630  0.0008848   73.65   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1835 on 8449 degrees of freedom
Multiple R-squared:  0.391, Adjusted R-squared:  0.3909 
F-statistic:  5424 on 1 and 8449 DF,  p-value: < 2.2e-16
cor.test(BothInfoTypes$simpOpp_Chimp,BothResInfo$numPAS)

    Pearson's product-moment correlation

data:  BothInfoTypes$simpOpp_Chimp and BothResInfo$numPAS
t = 73.649, df = 8449, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6121253 0.6380995
sample estimates:
      cor 
0.6252856 

Number of PAS is less correlated with this index.

Add in the dominanace structure to compare to simpson:

BothResBothInfoDom= BothInfoTypes %>% full_join(BothRes_40_eachsm, by="gene", fill="None") %>%  mutate(Set= replace_na(Set, "None"),Dom= replace_na(Dom, "None"))


ggplot(BothResBothInfoDom,aes(x=simpOpp_Human,y= simpOpp_Chimp, col=Dom )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + scale_color_brewer(palette = "Set2") + labs(x="Human Simpson", y="Chimp Simpson", title="Simpson Information Index colored by whether gene has a dominant PAS")

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ggplot(BothResBothInfoDom,aes(x=simpOpp_Human,y= simpOpp_Chimp, col=Set )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + scale_color_brewer(palette = "Set2") +geom_density2d()+ labs(x="Human Simpson", y="Chimp Simpson", title="Simpson Information Index colored by Dominance Structure ")

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7725e4d brimittleman 2020-04-27
BothResBothInfoDom$numPAS=as.factor(BothResBothInfoDom$numPAS)
ggplot(BothResBothInfoDom,aes(x=simpOpp_Human,y= simpOpp_Chimp, col=numPAS )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + labs(x="Human Simpson", y="Chimp Simpson", title="Simpson Information Index colored by number of PAS ") + facet_grid(~Dom)

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27
7725e4d brimittleman 2020-04-27
#+ scale_color_brewer(palette = "Spectral")

Shannon Equitability

Equitability. Shannon diversity divided by the logarithm of number of taxa. This measures the evenness with which individuals are divided among the taxa present.

Shannon’s equitability (EH) measures the evenness of a community and can be easily calculated by diving the value of H with H_max, which equals to lnS(S=number of species encountered). Its value ranges between 0 and 1, with being complete evenness. (0-1)

\(E_{h}=H/log2(NumPAS)\)

BothResBothInfoDomEH=BothResBothInfoDom %>% mutate(human_EH=Human_Base2/log2(as.numeric(as.character(numPAS))), chimp_EH=Chimp_Base2/log2(as.numeric(as.character(numPAS))))


BothEH= BothResBothInfoDomEH %>% select(gene, numPAS, human_EH,chimp_EH) %>% gather("species", "ShannonEH", -gene, -numPAS)

ggplot(BothEH, aes(x=ShannonEH, fill=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Set1")+labs(title="Shannon equitability", x="Shannon equitability")
Warning: Removed 1 rows containing non-finite values (stat_density).

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27
wilcox.test(BothResBothInfoDomEH$human_EH, BothResBothInfoDomEH$chimp_EH)

    Wilcoxon rank sum test with continuity correction

data:  BothResBothInfoDomEH$human_EH and BothResBothInfoDomEH$chimp_EH
W = 42395000, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0
BothResBothInfoDomEH$numPAS=as.numeric(as.character(BothResBothInfoDomEH$numPAS))
ggplot(BothResBothInfoDomEH,aes(x=human_EH,y= chimp_EH )) + geom_point(alpha=.4) + geom_abline(slope=1, intercept = 0) +labs(title="Shannon equitability") + stat_cor(col="blue")+ geom_density_2d(col="blue")
Warning: Removed 1 rows containing non-finite values (stat_cor).
Warning: Removed 1 rows containing non-finite values (stat_density2d).
Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27
BothResBothInfoDomEH$numPAS=as.numeric(as.character(BothResBothInfoDomEH$numPAS))
ggplot(BothResBothInfoDomEH,aes(x=human_EH,y= chimp_EH ,col=numPAS)) + geom_point(alpha=.4) + geom_abline(slope=1, intercept = 0) +labs(title="Shannon equitability Colored by number of PAS")
Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27
summary(lm(BothResBothInfoDomEH$human_EH ~BothResBothInfoDomEH$numPAS))

Call:
lm(formula = BothResBothInfoDomEH$human_EH ~ BothResBothInfoDomEH$numPAS)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.90216 -0.07951  0.02225  0.10510  0.32953 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                 0.5905281  0.0044589  132.44   <2e-16 ***
BothResBothInfoDomEH$numPAS 0.0399720  0.0007954   50.25   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.165 on 8448 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.2301,    Adjusted R-squared:   0.23 
F-statistic:  2525 on 1 and 8448 DF,  p-value: < 2.2e-16
summary(lm(BothResBothInfoDomEH$chimp_EH ~BothResBothInfoDomEH$numPAS ))

Call:
lm(formula = BothResBothInfoDomEH$chimp_EH ~ BothResBothInfoDomEH$numPAS)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.82198 -0.10698  0.02453  0.12846  0.40625 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                 0.5058251  0.0053208   95.06   <2e-16 ***
BothResBothInfoDomEH$numPAS 0.0439612  0.0009492   46.31   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1969 on 8449 degrees of freedom
Multiple R-squared:  0.2025,    Adjusted R-squared:  0.2024 
F-statistic:  2145 on 1 and 8449 DF,  p-value: < 2.2e-16

This normalizes the number of PAS.

Correlation between values:

BothInfoTypes_eh_h=BothResBothInfoDomEH %>% select(gene,numPAS, simpOpp_Human, human_EH) %>% mutate(species="Human") %>% rename(Simpson= simpOpp_Human, ShannonEH=human_EH)
BothInfoTypes_eh_c=BothResBothInfoDomEH %>% select(gene,numPAS, simpOpp_Chimp, chimp_EH) %>% mutate(species="Chimp")%>% rename(Simpson= simpOpp_Chimp, ShannonEH=chimp_EH)

BothInfoTypes_bothEH=BothInfoTypes_eh_h %>% bind_rows(BothInfoTypes_eh_c)

ggplot(BothInfoTypes_bothEH,aes(x=Simpson, y=ShannonEH, by=species, col=species)) +geom_point(alpha=.4) +geom_density2d(col="black") +  stat_cor(label.x=0) + geom_smooth(col="black",method = "lm") + facet_grid(~species) + labs(title="Correlation between Indicies") +theme(legend.position = "none")+scale_color_brewer(palette = "Set1")
Warning: Removed 1 rows containing non-finite values (stat_density2d).
Warning: Removed 1 rows containing non-finite values (stat_cor).
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27

Look at it with dominance:

ggplot(BothResBothInfoDomEH,aes(x=human_EH,y= chimp_EH, col=Dom )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + scale_color_brewer(palette = "Set2") + labs(x="Human equitability", y="Chimp equitability", title="Shannon equitability colored by whether gene has a dominant PAS")
Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27
ggplot(BothResBothInfoDomEH,aes(x=human_EH,y= chimp_EH, col=Set )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + scale_color_brewer(palette = "Set2") +geom_density2d()+ labs(x="Human equitability", y="Chimp equitability", title="Shannon equitability colored by Dominance Structure")
Warning: Removed 1 rows containing non-finite values (stat_density2d).

Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27
BothResBothInfoDomEH$numPAS=as.factor(BothResBothInfoDomEH$numPAS)
ggplot(BothResBothInfoDomEH,aes(x=human_EH,y= chimp_EH, col=numPAS )) + geom_point(alpha=.3) + geom_abline(slope=1, intercept = 0) + labs(x="Human equitability", y="Chimp equitability", title="Shannon Equitability colored by number of PAS ") + facet_grid(~Dom)
Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12
81dcd9f brimittleman 2020-04-27

plot simpson h/c colors:

simpsonind=ggplot(BothOppSimp_g, aes(x=SimpsonOpp, fill=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2",labels=c("Chimp", "Human"))+labs(title="Simpson Index to measure isoform diversity", x="Simpson Index")+ theme_classic2()

simpsonind

Version Author Date
cb0a024 brimittleman 2020-04-30

Plot only 1 color to demonstrate:

ggplot(BothOppSimp_g, aes(x=SimpsonOpp )) + geom_density(fill="grey") +labs(title="Simpson Index", x="Simpson Index")+ theme_classic2()

Version Author Date
10590ea brimittleman 2020-05-07

Plot number of PAS and info content to use:

ggplot(BothInfoTypes,aes(x=simpOpp_Human,y= simpOpp_Chimp ,col=numPAS)) + geom_point(alpha=.4) + geom_abline(slope=1, intercept = 0)+labs(title="Simpson Index Colored by number of PAS", x="Human", y="Chimp") + theme_classic()

Version Author Date
33d6feb brimittleman 2020-05-07

Plot shannon with HC colors

ggplot(BothResInfo_2, aes(x=base2, fill=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2",labels=c("Chimp", "Human"))+labs(title="Shannon Index to measure isoform diversity", x="Shannon Index")+ theme_classic2()
Warning: Removed 1 rows containing non-finite values (stat_density).

Version Author Date
6a947a5 brimittleman 2020-05-12
shannonPlot=ggplot(BothResInfo_2, aes(x=base2, fill=species)) + geom_density(alpha=.5) + scale_fill_brewer(palette = "Dark2",labels=c("Chimp", "Human"))+labs(title="Shannon Index to measure isoform diversity", x="Shannon Index")+ theme_classic2()
shannonPlot
Warning: Removed 1 rows containing non-finite values (stat_density).

Version Author Date
6a947a5 brimittleman 2020-05-12

pdf of figures

ggplot(BothInfoTypes,aes(x=Human_Base2,y= Chimp_Base2 ,col=numPAS)) + geom_point(alpha=.4) + geom_abline(slope=1, intercept = 0)+labs(title="Shannon Index Colored by number of PAS", x="Human", y="Chimp") + theme_classic()
Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12

Plot number of PAS by index:

ggplot(BothInfoTypes,aes(x=numPAS,y= Chimp_Base2 )) + geom_point(alpha=.4) + stat_cor()+ theme_classic()

Version Author Date
6a947a5 brimittleman 2020-05-12
ggplot(BothInfoTypes,aes(x=numPAS,y= Human_Base2 )) + geom_point(alpha=.4) + stat_cor()+ theme_classic()
Warning: Removed 1 rows containing non-finite values (stat_cor).
Warning: Removed 1 rows containing missing values (geom_point).

BothInfoTypesShanG= BothInfoTypes %>% select(gene, numPAS,Human_Base2,Chimp_Base2 )  %>% rename(Human=Human_Base2, Chimp=Chimp_Base2) %>% gather("Species", "value", -gene, -numPAS) 

shanoNum=ggplot(BothInfoTypesShanG,aes(x=numPAS,y= value ,col=Species)) + geom_point(alpha=.4) + stat_cor(col="black",label.y.npc="bottom")+ theme_classic() + facet_grid(~Species) + scale_color_brewer(palette = "Dark2") + labs(y="Shannon Information Content", title="Shannon Information Content and PAS number", x= "number of PAS in gene") 

shanoNum
Warning: Removed 1 rows containing non-finite values (stat_cor).
Warning: Removed 1 rows containing missing values (geom_point).

Version Author Date
6a947a5 brimittleman 2020-05-12
BothInfoTypesSimpG= BothInfoTypes %>% select(gene, numPAS,simpOpp_Human,simpOpp_Chimp )  %>% rename(Human=simpOpp_Human, Chimp=simpOpp_Chimp) %>% gather("Species", "value", -gene, -numPAS) 

simpnum=ggplot(BothInfoTypesSimpG,aes(x=numPAS,y= value ,col=Species)) + geom_point(alpha=.4) + stat_cor(col="black",label.y.npc="bottom")+ theme_classic() + facet_grid(~Species) + scale_color_brewer(palette = "Dark2")+ labs(y="Simpson Diversity", title="Simpson Diversity and PAS number", x= "number of PAS in gene") 

simpnum

Version Author Date
6a947a5 brimittleman 2020-05-12
plot_grid(shanoNum,simpnum, nrow=2)
Warning: Removed 1 rows containing non-finite values (stat_cor).
Warning: Removed 1 rows containing missing values (geom_point).


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] vegan_2.5-3     lattice_0.20-38 permute_0.9-4   cowplot_0.9.4  
 [5] workflowr_1.6.0 ggpubr_0.2      magrittr_1.5    forcats_0.3.0  
 [9] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
[13] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6       lubridate_1.7.4    assertthat_0.2.0  
 [4] rprojroot_1.3-2    digest_0.6.18      utf8_1.1.4        
 [7] R6_2.3.0           cellranger_1.1.0   plyr_1.8.4        
[10] backports_1.1.2    evaluate_0.12      httr_1.3.1        
[13] pillar_1.3.1       rlang_0.4.0        lazyeval_0.2.1    
[16] readxl_1.1.0       rstudioapi_0.10    whisker_0.3-2     
[19] Matrix_1.2-15      reticulate_1.10    rmarkdown_1.10    
[22] labeling_0.3       munsell_0.5.0      broom_0.5.1       
[25] compiler_3.5.1     httpuv_1.4.5       modelr_0.1.2      
[28] pkgconfig_2.0.2    mgcv_1.8-25        htmltools_0.3.6   
[31] tidyselect_0.2.5   fansi_0.4.0        crayon_1.3.4      
[34] withr_2.1.2        later_0.7.5        MASS_7.3-51.1     
[37] grid_3.5.1         nlme_3.1-137       jsonlite_1.6      
[40] gtable_0.2.0       git2r_0.26.1       scales_1.0.0      
[43] cli_1.1.0          stringi_1.2.4      reshape2_1.4.3    
[46] fs_1.3.1           promises_1.0.1     xml2_1.2.0        
[49] generics_0.0.2     RColorBrewer_1.1-2 tools_3.5.1       
[52] glue_1.3.0         hms_0.4.2          parallel_3.5.1    
[55] yaml_2.2.0         colorspace_1.3-2   cluster_2.0.7-1   
[58] rvest_0.3.2        knitr_1.20         haven_1.1.2