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Extend_data_10.Rmd
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Extend_data_10.Rmd
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---
title: "SalmHelm_UMIcounts"
author: "yejg"
date: "2017/12/26"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,message = FALSE,warning = FALSE,tidy = TRUE,fig.width=12, fig.height=10,highlight = TRUE)
```
### Library necessary packages
```{r,message=FALSE,message=FALSE}
library(NMF)
library(rsvd)
library(Rtsne)
library(ggplot2)
library(cowplot)
library(sva)
library(igraph)
library(cccd)
library(KernSmooth)
library(beeswarm)
library(stringr)
library(reshape2)
library(formatR)
library(destiny)
source('Fxns.R')
```
### Load data
```{r}
SalmHelm_UMIs<-load_data("./Extend_data/GSE92332_SalmHelm_UMIcounts.txt.gz")
SalmHelm_UMIs<-SalmHelm_UMIs[which(unlist(apply(SalmHelm_UMIs,1,sum))>0),]
SalmHelm_tpm<-data.frame(log2(1+tpm(SalmHelm_UMIs)))
```
### Select variables
```{r}
v = get.variable.genes(SalmHelm_UMIs, min.cv2 = 100)
var.genes = as.character(rownames(v)[v$p.adj<0.05]) # select genes
```
### Extract message from sample
```{r}
SalmHelm.all.cells<-colnames(SalmHelm_UMIs)
SalmHelm.all.genes<-rownames(SalmHelm_UMIs)
SalmHelm.condition<-unlist(lapply(SalmHelm.all.cells,function(x)return(str_split(x,'_')[[1]][3]))) # cell by conditon
SalmHelm.cell.groups<-unlist(lapply(SalmHelm.all.cells,function(x)return(str_split(x,'_')[[1]][4]))) # cell groups
SalmHelm.batches<-unlist(lapply(SalmHelm.all.cells,function(x)return(str_split(x,'_')[[1]][1]))) # batches
```
### Batch effect
```{r}
# check batch effect
table(SalmHelm.batches)
batch_mean_tpm = group.means(counts = SalmHelm_tpm, groups = SalmHelm.batches)
x = batch_mean_tpm[, 1]
y = batch_mean_tpm[,2]
expr.cor = round(cor(x,y),2)
smoothScatter(x, y, nrpoints=Inf, pch=16, cex=0.25, main=sprintf("Before batch correction, correlation between \ntwo illustrative batches is %s", expr.cor), xlab="All genes Batch 2, mean log2(TPM+1)", ylab="All genes Batch 1, mean log2(TPM+1)")
# remove the batch effect
SalmHelm_tpm_norm = batch.normalise.comBat(counts = as.matrix(SalmHelm_tpm), batch.groups = SalmHelm.batches)
batch_mean_tpm_norm = group.means(counts =SalmHelm_tpm_norm, groups = SalmHelm.batches)
x = batch_mean_tpm_norm[, 1]
y = batch_mean_tpm_norm[,2]
expr.cor = round(cor(x,y),2)
smoothScatter(x, y, nrpoints=Inf, pch=16, cex=0.25, main=sprintf("After batch correction, correlation between \ntwo illustrative batches is %s", expr.cor), xlab="All genes Batch 2, mean log2(TPM+1)", ylab="All genes Batch 1, mean log2(TPM+1)")
```
### Dimensionality reduction:PCA,tSNE
```{r}
SalmHelm.tsne.rot<-PCA_TSNE.scores(data.tpm = SalmHelm_tpm_norm,data.umis = SalmHelm_UMIs,var_genes = var.genes,
data_name = './Extend_data/SalmHelm',sig.pcs = TRUE,is.var.genes = TRUE)
SalmHelm.tsne.rot<-data.frame(SalmHelm.tsne.rot)
colnames(SalmHelm.tsne.rot)<-c('tSNE_1','tSNE_2')
ggplot(data =SalmHelm.tsne.rot,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(colour=SalmHelm.cell.groups))+
scale_color_manual(values=brewer16)+scale_fill_discrete()+theme(legend.title=element_blank())+ggtitle('SalmHelm')
ggplot(data =SalmHelm.tsne.rot,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(colour=SalmHelm.condition))+
scale_color_manual(values=brewer16)+scale_fill_discrete()+theme(legend.title=element_blank())+ggtitle('SalmHelm')
```
### Figure a
```{r}
genes<-c('Cd74','Gpx2','Hsph1')
All.Facet.tsne<-data.frame()
for(gene in genes){
All.Facet.tsne<-rbind(All.Facet.tsne,
Facet_wrap_fun(gene=gene,tpm.data = SalmHelm_tpm_norm,tsne.data = SalmHelm.tsne.rot,condition = unique(SalmHelm.condition),all.condition = SalmHelm.condition))
}
```
```{r}
ggplot(data=All.Facet.tsne[All.Facet.tsne$Gene%in%genes[1],],aes(x=tSNE_1,y=tSNE_2,colour=Gene.Mp))+geom_point()+ggtitle(genes[1])+
facet_wrap(~ Condition, nrow = 5,ncol = 2)+
theme(legend.title = element_text(size=10,color='blue',face='bold'),legend.position = 'right')+
scale_color_gradient2(low='lightblue',mid='green',high='red',name='Log2\nTPM+1')
ggplot(data=All.Facet.tsne[All.Facet.tsne$Gene%in%genes[2],],aes(x=tSNE_1,y=tSNE_2,colour=Gene.Mp))+geom_point()+ggtitle(genes[2])+
facet_wrap(~ Condition, nrow = 5,ncol = 2)+
theme(legend.title = element_text(size=10,color='blue',face='bold'),legend.position = 'right')+
scale_color_gradient2(low='lightblue',mid='green',high='red',name='Log2\nTPM+1')
ggplot(data=All.Facet.tsne[All.Facet.tsne$Gene%in%genes[3],],aes(x=tSNE_1,y=tSNE_2,colour=Gene.Mp))+geom_point()+ggtitle(genes[3])+
facet_wrap(~ Condition, nrow = 5,ncol = 2)+
theme(legend.title = element_text(size=10,color='blue',face='bold'),legend.position = 'right')+
scale_color_gradient2(low='lightblue',mid='green',high='red',name='Log2\nTPM+1')
```
### Figure b
```{r}
SalmHelm.Tuft.tpm<-SalmHelm_tpm_norm[,SalmHelm.cell.groups%in%'Tuft']
SalmHelm.Tuft.tsne<-SalmHelm.tsne.rot[SalmHelm.cell.groups%in%'Tuft',]
SalmHelm.Tuft.pca<-read.table('./Extend_data/SalmHelm_pca_scores.txt')[SalmHelm.cell.groups%in%'Tuft',]
### Find the k to make 3 cluster
### k-nearest method
# Find_K<-function(K,pca.data,n=3){
# dm<-as.matrix(dist(pca.data))
# for(k in K){
# knn<-build_knn_graph(dm,k=k)
# clustering<-cluster_graph(knn)$partition
# if(length(unique(clustering))==n){
# cat(sprintf('Find the K:%d\n',k))
# return(k)
# break
# }
# }
# }
#
# K<-seq(2,200,by = 2)
# k<-Find_K(K = K,pca.data = SalmHelm.Tuft.pca[,1:15])
dm<-as.matrix(dist(SalmHelm.Tuft.pca[,1:15]))
# build nearest neighbor graph
knn = build_knn_graph(dm, k =74)
Tuft.clustering = cluster_graph(knn)$partition
Tuft_1_marker_genes<-as.character(read.table('./Extend_data/Tuft_1_marker_genes.txt')$V1)
Tuft_2_marker_genes<-as.character(read.table('./Extend_data/Tuft_2_marker_genes.txt')$V1)
Tuft.clustering<-paste('Tuft-',Tuft.clustering,sep='')
Tuft.clustering<-ifelse(Tuft.clustering=='Tuft-1','Tuft-1',ifelse(Tuft.clustering=='Tuft-2','Tuft-2','Progenitor'))
score_1<-unlist(apply(SalmHelm.Tuft.tpm[Tuft_1_marker_genes,],2,mean,na.rm=TRUE))
score_2<-unlist(apply(SalmHelm.Tuft.tpm[Tuft_2_marker_genes,],2,mean,na.rm=TRUE))
Dklk1<-as.numeric(SalmHelm.Tuft.tpm['Dclk1',])
Violin.data1<-cbind(data.frame(Value=score_1),data.frame(Group=Tuft.clustering))
Violin.data2<-cbind(data.frame(Value=score_2),data.frame(Group=Tuft.clustering))
Dklk1.data<-cbind(data.frame(Value=Dklk1),data.frame(Group=Tuft.clustering))
```
### Figure b.1
```{r}
ggplot(data=Violin.data1,aes(x=Group,y=Value,colour=Group))+
geom_violin(aes(fill=Group))+geom_boxplot(width=0.1,fill="white")+
geom_jitter(shape=16, position=position_jitter(0.2),color='black')+
theme(legend.title = element_blank())+ggtitle(label = 'Tuft-1 score')+
xlab(NULL)+ylab('Expression Level\nLog2(TPM+1)')
#stat_summary(fun.data=mean_sdl,geom="pointrange", color="red")
```
### Figure b.2
```{r}
ggplot(data=Violin.data2,aes(x=Group,y=Value,colour=Group))+
geom_violin(aes(fill=Group))+geom_boxplot(width=0.1,fill="white")+
geom_jitter(shape=16, position=position_jitter(0.2),color='black')+
theme(legend.title = element_blank())+ggtitle(label = 'Tuft-2 score')+
xlab(NULL)+ylab('Expression Level\nLog2(TPM+1)')
```
### Figure b.3
```{r}
ggplot(data=Dklk1.data,aes(x=Group,y=Value,colour=Group))+
geom_violin(aes(fill=Group))+geom_boxplot(width=0.1,fill="white")+
geom_jitter(shape=16, position=position_jitter(0.2),color='black')+
theme(legend.title = element_blank())+ggtitle(label = 'Dckl1')+
xlab(NULL)+ylab('Expression Level\nLog2(TPM+1)')
```
### Figure c
```{r}
## Figure c.1
ggplot(SalmHelm.Tuft.tsne, aes(x=tSNE_1, y=tSNE_2))+geom_point(aes(color=score_1))+
theme(legend.title = element_text(size=8,color='blue',face='bold'),
legend.position = 'right') +ggtitle('Tuft-1 Score')+scale_color_gradient2(low='lightblue',mid='green',high='red',name='Log2\nTPM+1')
## Figure c.2
ggplot(SalmHelm.Tuft.tsne, aes(x=tSNE_1, y=tSNE_2))+geom_point(aes(color=score_2))+
theme(legend.title = element_text(size=8,color='blue',face='bold'),
legend.position = 'right') +ggtitle('Tuft-2 Score')+scale_color_gradient2(low='lightblue',mid='green',high='red',name='Log2\nTPM+1')
## Figure c.3
ggplot(data =SalmHelm.Tuft.tsne,aes(x=tSNE_1,y=tSNE_2))+geom_point(aes(colour=Tuft.clustering))+
scale_color_manual(values=brewer16)+scale_fill_discrete()+theme(legend.title=element_blank())+ggtitle('Tuft')
```
### Figure d
Induction of antiparasitic genes by goblet cells after helminth infection
```{r}
SalmHelm.Goblet.tpm<-SalmHelm_tpm_norm[,SalmHelm.cell.groups%in%'Goblet']
SalmHelm.Goblet.condiion<-SalmHelm.condition[SalmHelm.cell.groups%in%'Goblet']
SalmHelm.Goblet.condition.tpm<-SalmHelm.Goblet.tpm[,SalmHelm.Goblet.condiion%in%c('Control',"Hpoly.Day3","Hpoly.Day10")]
SalmHelm.Goblet.condiion3<-SalmHelm.Goblet.condiion[SalmHelm.Goblet.condiion%in%c('Control',"Hpoly.Day3","Hpoly.Day10")]
Retnlb<-as.numeric(SalmHelm.Goblet.condition.tpm['Retnlb',])
Wars<-as.numeric(SalmHelm.Goblet.condition.tpm['Wars',])
Pnliprp2<-as.numeric(SalmHelm.Goblet.condition.tpm['Pnliprp2',])
Violin.Retnlb<-cbind(data.frame(Value=Retnlb),data.frame(Group=SalmHelm.Goblet.condiion3))
Violin.Wars<-cbind(data.frame(Value=Wars),data.frame(Group=SalmHelm.Goblet.condiion3))
Violin.Pnliprp2<-cbind(data.frame(Value=Pnliprp2),data.frame(Group=SalmHelm.Goblet.condiion3))
```
```{r}
## Figure d.1
ggplot(data=Violin.Retnlb,aes(x=Group,y=Value,colour=Group))+
geom_violin(aes(fill=Group))+geom_boxplot(width=0.1,fill="white")+
geom_jitter(shape=16, position=position_jitter(0.2),color='black')+
theme(legend.title = element_blank())+ggtitle(label = 'Retnlb')+
xlab(NULL)+ylab('Expression Level\nLog2(TPM+1)')
## Figure d.2
ggplot(data=Violin.Wars,aes(x=Group,y=Value,colour=Group))+
geom_violin(aes(fill=Group))+geom_boxplot(width=0.1,fill="white")+
geom_jitter(shape=16, position=position_jitter(0.2),color='black')+
theme(legend.title = element_blank())+ggtitle(label = 'Wars')+
xlab(NULL)+ylab('Expression Level\nLog2(TPM+1)')
## Figure d.3
ggplot(data=Violin.Pnliprp2,aes(x=Group,y=Value,colour=Group))+
geom_violin(aes(fill=Group))+geom_boxplot(width=0.1,fill="white")+
geom_jitter(shape=16, position=position_jitter(0.2),color='black')+
theme(legend.title = element_blank())+ggtitle(label = 'Pnliprp2')+
xlab(NULL)+ylab('Expression Level\nLog2(TPM+1)')
```