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functions.R
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## color panels
mycolor2_blue_yellow <- c("#0072B5FF","#FFDC91FF")
mycolor2_green_blue <- c("#20854EFF","#7876B1FF")
mycolor2_blue_red <- c("#6F99ADFF","#BC3C29FF")
mycolor2_red_blue <- c("#BC3C29FF","#6F99ADFF")
mycolor2_yellow_green <- c("#E18727FF","#20854EFF")
mycolor2_pfs<-c("#EE4C97FF","#0072B5FF")
mycolor3 <- c("#7876B1FF","#6F99ADFF","#FFDC91FF")
mycolor3_red_blue_yellow <- c("#BC3C29FF","#0072B5FF","#E18727FF")
mycolor3_green_red_yellow<- c("#20854EFF","#BC3C29FF","#E18727FF")
mycolor6<- c("#BC3C29FF","#0072B5FF","#E18727FF","#20854EFF","#7876B1FF","#6F99ADFF")
mycolor7<- c("#BC3C29FF","#0072B5FF","#E18727FF","#20854EFF","#7876B1FF","#6F99ADFF","#FFDC91FF")
mycolor8<- c("#BC3C29FF","#0072B5FF","#E18727FF","#20854EFF","#7876B1FF","#6F99ADFF","#FFDC91FF","#EE4C97FF")
#install.packages("ggmosaic")
#install.packages("tidyverse")
#install.packages("GGally")
#install.packages("ggthemes")
#install.packages("gplots")
#install.packages("VennDiagram")
#install.packages("circlize")
#install.packages("viridis")
#install.packages("wesanderson")
#install.packages("ggalluvial")
#install.packages(c("Hmisc","vegan","mediation"))
#install.packages("rlang",version="1.0.3")
#install.packages("glue",version="1.3.2")
#install.packages("tibble",version="3.0.0")
#install.packages("dplyr")
#install.packages("showtext")
#install.packages("Cairo")
#install.packages("scatterpie")
#install.packages("NbClust")
#install.packages("fpc")
#install.packages("tsne")
#install.packages("meta")
#if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install(version = "3.12")
### library(BiocManager)
### BiocManager::install("microbiome")
## libraty packages
library(tidyverse)
library(ggpubr)
library(GGally)
library(ggmosaic)
library(cowplot)
library(ggsci)
library(ggthemes)
library(gplots)
library(VennDiagram)
library(circlize)
library(viridis)
library(wesanderson)
library(ggalluvial)
library(meta)
library(reshape2)
library(Hmisc)
library(vegan)
library(microbiome)
library(mediation)
library(metafor)
## Convert SV name
changeSVname<-function(SVrawid){
testname <- SVrawid
species_name <- as.character(taxonomy$organism[match(str_replace_all(testname, "\\..*",""), taxonomy$X)])
region <- str_replace_all(testname, ".*\\:","")
region_list <- str_split(region,";")
region_suf <- NULL
i<-1
for (i in c(1:length(region_list))){
if(length(region_list[[i]]) <= 2){
region_suf[i] <- paste(":",region[i],sep = "")
}else{
region_suf[i] <- paste(":",region_list[[i]][1]," and ",length(region_list[[i]])-1," segments", sep = "")
}
i <- i+1
}
paste(species_name,region_suf,sep = "")
}
## Calculate SV size
calcSVSize <- function(SVrawid){
testname <- SVrawid
region <- str_replace_all(testname, ".*\\:","")
region_list <- str_split(region,";")
sv_size <- NULL
for (i in c(1:length(region_list))) {
frag_df <- str_split(region_list[[i]], "_") %>% unlist %>% as.numeric %>% matrix(byrow = T, ncol = 2) %>% as.data.frame
sv_size[i] <- sum(as.numeric(frag_df$V2)-as.numeric(frag_df$V1))
}
sv_size
}
shared_sv_dis_canberra<-function(inDf){
#inDf<-all_sv
inList <- split(as.matrix(inDf), seq(nrow(inDf)))
shared_n_func<-function(inVec,Vec_i){
#inVec<-inList[[3]]
Vec_i<-Vec_i
insvdf<-data.frame(inVec,Vec_i) %>% na.omit
sv_dis<- vegdist(t(insvdf), method = "canberra")
#length(na.omit(Vec_i+inVec))
return(sv_dis)
}
marker_n_mat<-matrix(NA, nrow = nrow(inDf), ncol = nrow(inDf))
for (i in 1:nrow(inDf)) { #nrow(inDf)
#i<-2
Vec_i<-inList[[i]]
shared_n_i<-sapply(inList, shared_n_func,Vec_i = Vec_i)
marker_n_mat[i,]<-shared_n_i
}
rownames(marker_n_mat)<-rownames(inDf)
colnames(marker_n_mat)<-rownames(inDf)
return(marker_n_mat)
}
shared_sv_dis_jaccard<-function(inDf){
#inDf<-all_sv
inList <- split(as.matrix(inDf), seq(nrow(inDf)))
shared_n_func<-function(inVec,Vec_i){
#inVec<-inList[[3]]
Vec_i<-Vec_i
insvdf<-data.frame(inVec,Vec_i) %>% na.omit
sv_dis<- vegdist(t(insvdf), method = "jaccard")
#length(na.omit(Vec_i+inVec))
return(sv_dis)
}
marker_n_mat<-matrix(NA, nrow = nrow(inDf), ncol = nrow(inDf))
for (i in 1:nrow(inDf)) { #nrow(inDf)
#i<-2
Vec_i<-inList[[i]]
shared_n_i<-sapply(inList, shared_n_func,Vec_i = Vec_i)
marker_n_mat[i,]<-shared_n_i
}
rownames(marker_n_mat)<-rownames(inDf)
colnames(marker_n_mat)<-rownames(inDf)
return(marker_n_mat)
}
## Calculate SE
se <- function(x) {x<-na.omit(x);sqrt(var(x)/length(x))}
## Summary statistics
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=TRUE,conf.interval=.95, .drop=TRUE) {
require(plyr)
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
nonZero_N = sum(xx[[col]]!=0,na.rm = na.rm),
nonZero_rate = sum(xx[[col]]!=0,na.rm = na.rm)/length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm),
mindata = min(xx[[col]], na.rm=na.rm),
maxdata = max(xx[[col]], na.rm=na.rm),
quant1_ = quantile(xx[[col]], na.rm=na.rm)[2] ,
quant2_ = quantile(xx[[col]], na.rm=na.rm)[4] ,
quantmid = quantile(xx[[col]], na.rm=na.rm)[3] ,
mea = mean (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
datac <- plyr::rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
datac$xd <- datac$mea-datac$se
datac$xu<- datac$mea+datac$se
return(datac)
}
## Remove samples with NA in distance matrix
dist_rmna<-function(inDist){
while(sort(colSums(is.na(inDist)),decreasing = T)[1] > 0){
rmid<-names(sort(colSums(is.na(inDist)),decreasing = T)[1])
inDist<-inDist[-match(rmid, rownames(inDist)),-match(rmid, colnames(inDist))]
}
return(inDist)
}
## pie chart
my_pie<-function(in1,item,mycol = c("#F24D4D","#4D7EAE")){
require(tibble)
require(showtext)
require(Cairo)
require(ggsci)
require(tibble)
require(scales)
require(ggrepel)
require(forcats)
require(scatterpie)
showtext_auto()
in1.tibble <- as_tibble(subset(in1, in1$items%in%item))
in1.tibble$categories <- fct_reorder(in1.tibble$categories, in1.tibble$value)
in1.tibble <- in1.tibble[order(in1.tibble$value, decreasing = TRUE), ]
piepercent <- round(100*in1.tibble$value/sum(in1.tibble$value), 2)
my_labels <- tibble(x.breaks = seq(1.1, 1.1, length.out = length(piepercent)),
y.breaks = cumsum(in1.tibble$value) - in1.tibble$value/2,
labels = paste(in1.tibble$categories, "\n",in1.tibble$value,", ",piepercent, "%", sep = ""),
categories = in1.tibble$categories)
pdfName <- paste(item, ".pie.pdf", sep = "")
ggplot(in1.tibble, aes(x = 1, y = value, fill = categories)) +
ggtitle(paste(item)) +
geom_bar(stat="identity", color='white') +
coord_polar(theta='y') +
theme(legend.position = "None",
axis.ticks=element_blank(), # the axis ticks
axis.title=element_blank(), # the axis labels
axis.text.y=element_blank(), # the 0.75, 1.00, 1.25 labels.
axis.text.x = element_blank(),
panel.background = element_blank(),
panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5)) +
# scale_fill_brewer(palette = "Set3", direction = -1)+
scale_fill_manual(values=mycol) + # set fill color manually
geom_text_repel(data = my_labels,
aes(x = x.breaks, y = y.breaks, label = labels),
size = 7, # label text size
show.legend = FALSE,
inherit.aes = FALSE,
arrow = arrow(length=unit(0.01, "npc")),
force = 1,colour = "white",
segment.color = 'grey50' ## grey50
)
}
## pie chart
my_pie_nolabel<-function(in1,item,mycol = c("#F24D4D","#4D7EAE")){
require(tibble)
require(showtext)
require(Cairo)
require(ggsci)
require(tibble)
require(scales)
require(ggrepel)
require(forcats)
require(scatterpie)
showtext_auto()
in1.tibble <- as_tibble(subset(in1, in1$items%in%item))
in1.tibble$categories <- fct_reorder(in1.tibble$categories, in1.tibble$value)
in1.tibble <- in1.tibble[order(in1.tibble$value, decreasing = TRUE), ]
piepercent <- round(100*in1.tibble$value/sum(in1.tibble$value), 2)
my_labels <- tibble(x.breaks = seq(1.1, 1.1, length.out = length(piepercent)),
y.breaks = cumsum(in1.tibble$value) - in1.tibble$value/2,
labels = paste(in1.tibble$categories, "\n",in1.tibble$value,", ",piepercent, "%", sep = ""),
categories = in1.tibble$categories)
pdfName <- paste(item, ".pie.pdf", sep = "")
ggplot(in1.tibble, aes(x = 1, y = value, fill = categories)) +
ggtitle(paste(item)) +
geom_bar(stat="identity", color='white') +
coord_polar(theta='y') +
theme(legend.position = "None",
axis.ticks=element_blank(), # the axis ticks
axis.title=element_blank(), # the axis labels
axis.text.y=element_blank(), # the 0.75, 1.00, 1.25 labels.
axis.text.x = element_blank(),
panel.background = element_blank(),
panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5)) +
# scale_fill_brewer(palette = "Set3", direction = -1)+
scale_fill_manual(values=mycol) #+ # set fill color manually
#geom_text_repel(data = my_labels,
# aes(x = x.breaks, y = y.breaks, label = labels),
# size = 16, # label text size
# show.legend = FALSE,
# inherit.aes = FALSE,
# arrow = arrow(length=unit(0.01, "npc")),
# force = 1,colour = "black",
# segment.color = 'grey50' ## grey50
#)
}
## Batch adonis
my_adonis_terms<-function(inDist, inDf, covDf=NULL, covar=NULL){
require(vegan)
## test
#inDist <- distList[[i]]
#inDf <- inDf
#covDf <- covDf
#covar <- inCovar
## test
covDf<- covDf[,covar]
inDist_rmna <- dist_rmna(inDist)
covDf_rmna <- na.omit(covDf)
my_adonis<-function(inMat, inVec, covdf = NULL){
## test
#inMat<-inDist_rmna
#inVec<-inDf[,1]
#covdf<-covDf_rmna
## test
inMat_covdf_inter <- intersect(rownames(inMat),rownames(covdf))
inMat_covdf_inVec_inter <- intersect(rownames(inDf)[!is.na(inVec)], inMat_covdf_inter)
inMat_rmna <- inMat[match(inMat_covdf_inVec_inter,rownames(inMat)),
match(inMat_covdf_inVec_inter,colnames(inMat))]
inVec_rmna <- inVec[match(inMat_covdf_inVec_inter, rownames(inDf))]
covdf_rmna <- covdf[match(inMat_covdf_inVec_inter, rownames(covdf)),]
in_cov <- data.frame(inVec_rmna,covdf_rmna)
sample_size<-nrow(in_cov)
if(length(table(inVec_rmna))<2){
return(list(NA,NA, sample_size))
}
else{
adonis_res <- adonis(as.dist(inMat_rmna)~.,data = in_cov)
return(list(adonis_res$aov.tab[1,5],adonis_res$aov.tab[1,6], sample_size))
}
}
adonis_res_batch <- apply(inDf, 2, my_adonis,inMat = inDist_rmna, covdf = covDf_rmna)
adonis_res_batch.unlist <- matrix(unlist(adonis_res_batch), ncol = 3, byrow = T)
colnames(adonis_res_batch.unlist)<-c("R2", "P", "N")
return(adonis_res_batch.unlist)
}
my_adonis_terms_noadjAbun<-function(distList, inDf, covDf, covar,info){
#distList<-PRJEB22863_msv_dist_std
#inDf<-covar_PRJEB22863
#covDf<-PRJEB22863_covar
#covar<-covar2
#info<-France_info
res_table<-NULL
for (i in 1:length(distList)) { #
#i<-8
cat(paste(i,"\n"))
n<-names(distList)[i] %>%
str_replace_all("msv_","") %>%
match(., info$organism)
inCovar<-covar
#inCovar<-c(covar, info$organism[n])
if(is.na(distList[[i]])==F){
if (nrow(distList[[i]])>10 & ncol(distList[[i]])>10){
adonis_res<-my_adonis_terms(distList[[i]], inDf, covDf = covDf, covar = inCovar)
adonis_res_df<-data.frame(Species = rep(info$Short_name[n], dim(adonis_res)[1]),
Prog = colnames(inDf),
as.data.frame(adonis_res))
res_table<-rbind(res_table, adonis_res_df)
}
else {
adonis_res_df<-data.frame(Species = rep(info$Short_name[n], ncol(inDf)),
Prog = colnames(inDf),
R2=NA,P=NA,N=NA)
res_table<-rbind(res_table, adonis_res_df)
}
}
else {
adonis_res_df<-data.frame(Species = rep(info$Short_name[n], ncol(inDf)),
Prog = colnames(inDf),
R2=NA,P=NA,N=NA)
res_table<-rbind(res_table, adonis_res_df)
}
}
res_table$Species<-as.character(res_table$Species)
res_table$Prog<-as.character(res_table$Prog)
res_table$fdr<-p.adjust(res_table$P,method = 'fdr')
res_table$bonferroni<-p.adjust(res_table$P,method = 'bonferroni')
# R2
r2_mat<-matrix(data = res_table$R2,
nrow = length(unique(res_table$Species)),
ncol = length(unique(res_table$Prog)),
byrow = T)
rownames(r2_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(r2_mat)<-colnames(inDf)
# P
p_mat<-matrix(data = res_table$P,
nrow = length(unique(res_table$Species)),
ncol = length(unique(res_table$Prog)),
byrow = T)
rownames(p_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(p_mat)<-colnames(inDf)
# fdr
fdr_mat<-matrix(data = res_table$fdr,
nrow = length(unique(res_table$Species)),
ncol = length(unique(res_table$Prog)),
byrow = T)
rownames(fdr_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(fdr_mat)<-colnames(inDf)
return(list(table = res_table,
r2 = r2_mat,
P = p_mat,
FDR = fdr_mat))
}
my_adonis_terms_adjAbun<-function(distList, inDf, covDf, covar,info){
#distList<-PRJEB22863_msv_dist_std
#inDf<-covar_PRJEB22863
#covDf<-PRJEB22863_covar
#covar<-covar2
#info<-info
res_table<-NULL
for (i in 1:length(distList)) { #
#i<-8
cat(paste(i,"\n"))
n<-names(distList)[i] %>%
str_replace_all("msv_","") %>%
match(., info$organism)
inCovar<-c(covar, info$organism[n])
if(is.na(distList[[i]])==F){
if (nrow(distList[[i]])>10 & ncol(distList[[i]])>10){
adonis_res<-my_adonis_terms(distList[[i]], inDf, covDf = covDf, covar = inCovar)
adonis_res_df<-data.frame(Species = rep(info$Short_name[n], dim(adonis_res)[1]),
Prog = colnames(inDf),
as.data.frame(adonis_res))
res_table<-rbind(res_table, adonis_res_df)
}
else {
adonis_res_df<-data.frame(Species = rep(info$Short_name[n], ncol(inDf)),
Prog = colnames(inDf),
R2=NA,P=NA,N=NA)
res_table<-rbind(res_table, adonis_res_df)
}
}
else {
adonis_res_df<-data.frame(Species = rep(info$Short_name[n], ncol(inDf)),
Prog = colnames(inDf),
R2=NA,P=NA,N=NA)
res_table<-rbind(res_table, adonis_res_df)
}
}
res_table$Species<-as.character(res_table$Species)
res_table$Prog<-as.character(res_table$Prog)
res_table$fdr<-p.adjust(res_table$P,method = 'fdr')
res_table$bonferroni<-p.adjust(res_table$P,method = 'bonferroni')
# R2
r2_mat<-matrix(data = res_table$R2,
nrow = length(unique(res_table$Species)),
ncol = length(unique(res_table$Prog)),
byrow = T)
rownames(r2_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(r2_mat)<-colnames(inDf)
# P
p_mat<-matrix(data = res_table$P,
nrow = length(unique(res_table$Species)),
ncol = length(unique(res_table$Prog)),
byrow = T)
rownames(p_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(p_mat)<-colnames(inDf)
# fdr
fdr_mat<-matrix(data = res_table$fdr,
nrow = length(unique(res_table$Species)),
ncol = length(unique(res_table$Prog)),
byrow = T)
rownames(fdr_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(fdr_mat)<-colnames(inDf)
return(list(table = res_table,
r2 = r2_mat,
P = p_mat,
FDR = fdr_mat))
}
my_meta_p <- function(inVec, study_name, n_col, p_col) {
require(metap)
#require(metafor)
#inVec<-cbind_adonis_res.table[20,]
#study_name<-c("LLD", "300OB")
#n_col<-c(10,15)
#_col<-c(9,14)
study_n <- inVec[n_col] %>% as.numeric
study_p <- inVec[p_col] %>% as.numeric
inDf <- data.frame(study_name, study_n, study_p)
inDf<-subset(inDf,is.na(study_p)==F,drop=T)
study_n<-na.omit(study_n)
study_p<-na.omit(study_p)
Wi=sqrt(study_n)
#Convert p-values to Z-scores
Zi=qnorm(1-(study_p/2))
# Meta-zscore
Z=(sum(Zi*Wi)/sqrt(sum(Wi^2)))
# Convert Z-score to p-value
MetaP=2*pnorm(-abs(Z))
#Cochran Q-test (test heterogeneity of your meta-analysis)
WeightedZ= sum(sqrt(study_n)*Zi)
totalSample=sum(study_n)
#Calculate expected Z
expected_Z= sqrt(study_n)*WeightedZ/totalSample
het_Sum= sum((Zi-expected_Z)*(Zi-expected_Z))
#Get p-value of heterogeneity test!
my_pvalue_co=pchisq(het_Sum, lower.tail = F, df=length(study_p)-1)
return(
list(
Meta.p = MetaP,
Meta.hetero = het_Sum,
Meta.hetero.p = my_pvalue_co
)
)
}
## Batch meta-analysis
my_batch_meta_p <- function(inDf,study_name,n_col,p_col,row_var_col = 1,col_var_col = 2) {
#inDf<-cbind_adonis_res.table[c(1:10),]
#study_name<-c("LLD", "300OB")
#n_col<-c(10,15)
#p_col<-c(9,14)
#row_var_col<-1
#col_var_col<-2
batch_res <- apply(
inDf,
1,
my_meta_p,
study_name = study_name,
n_col = n_col,
p_col = p_col
)
batch_res.unlist <- matrix(unlist(batch_res), ncol = 3, byrow = T)
colnames(batch_res.unlist) <-
c('Meta.p', "Meta.hetero", "Meta.hetero.p")
batch_res_edge <- cbind(inDf, batch_res.unlist)
rowName <- unique(inDf[, row_var_col])
colName <- unique(inDf[, col_var_col])
N_row <- length(rowName)
N_col <- length(colName)
batch_res.p <- matrix(batch_res.unlist[, 1], ncol = N_col, byrow = F)
colnames(batch_res.p) <- colName
rownames(batch_res.p) <- rowName
batch_res.p[is.na(batch_res.p)] <- 0
# add p adjust
batch_res_edge <- data.frame(
as.data.frame(batch_res_edge),
Meta.fdr.p = p.adjust(batch_res_edge$Meta.p, method = "fdr"),
Meta.bonferroni.p = p.adjust(batch_res_edge$Meta.p, method = "bonferroni")
)
# fdr matrix
batch_res.fdr <- matrix(
data = batch_res_edge$Meta.fdr.p,
nrow = N_row,
ncol = N_col,
byrow = T
)
colnames(batch_res.fdr) <- colName
rownames(batch_res.fdr) <- rowName
batch_res.fdr[is.na(batch_res.fdr)] <- 1
# bonferroni matrix
batch_res.bon <- matrix(
data = batch_res_edge$Meta.bonferroni.p,
nrow = N_row,
ncol = N_col,
byrow = T
)
colnames(batch_res.bon) <- colName
rownames(batch_res.bon) <- rowName
batch_res.bon[is.na(batch_res.bon)] <- 1
return(
list(
table = batch_res_edge,
p = batch_res.p,
fdr = batch_res.fdr,
bonferroni = batch_res.bon
)
)
}
## Batch meta-analysis
lr_batch_meta_p <- function(inDf,study_name,n_col,p_col,row_var_col,prog) {
#inDf<-cbind_dsv_res.table
#study_name<-c("PRJEB22863","PRJNA397906","PRJNA751792","PRJNA762360","PRJNA770295","PRJEB43119")
#n_col<-seq(3,13,by=2)
#p_col<-seq(2,13,by=2)
#row_var_col<-1
#prog<-"response"
batch_res <- apply(
inDf,
1,
my_meta_p,
study_name = study_name,
n_col = n_col,
p_col = p_col
)
batch_res.unlist <- matrix(unlist(batch_res), ncol = 3, byrow = T)
colnames(batch_res.unlist) <-
c('Meta.p', "Meta.hetero", "Meta.hetero.p")
batch_res_edge <- cbind(inDf, batch_res.unlist)
rowName <- unique(inDf[, row_var_col])
N_row <- length(rowName)
N_col <- 1
batch_res.p <- matrix(batch_res.unlist[, 1], ncol = N_col, byrow = F)
colnames(batch_res.p) <- prog
rownames(batch_res.p) <- rowName
batch_res.p[is.na(batch_res.p)] <- 0
# add p adjust
batch_res_edge <- data.frame(
as.data.frame(batch_res_edge),
Meta.fdr.p = p.adjust(batch_res_edge$Meta.p, method = "fdr"),
Meta.bonferroni.p = p.adjust(batch_res_edge$Meta.p, method = "bonferroni")
)
# fdr matrix
batch_res.fdr <- matrix(
data = batch_res_edge$Meta.fdr.p,
nrow = N_row,
ncol = N_col,
byrow = T
)
colnames(batch_res.fdr) <- prog
rownames(batch_res.fdr) <- rowName
batch_res.fdr[is.na(batch_res.fdr)] <- 1
# bonferroni matrix
batch_res.bon <- matrix(
data = batch_res_edge$Meta.bonferroni.p,
nrow = N_row,
ncol = N_col,
byrow = T
)
colnames(batch_res.bon) <- prog
rownames(batch_res.bon) <- rowName
batch_res.bon[is.na(batch_res.bon)] <- 1
return(
list(
table = batch_res_edge,
p = batch_res.p,
fdr = batch_res.fdr,
bonferroni = batch_res.bon
)
)
}
## The Normal Quantile Transformation
qtrans<-function(x){
k<-!is.na(x)
k<-which(x!="-999")
ran<-rank(as.numeric(x[k]))
y<-qnorm((1:length(k)-0.5)/length(k))
x[k]<-y[ran]
x
}
## linear model
lm_btw_mats<-function(y_mat,x_mat,cov_mat,covar){
require(reshape2)
require(R.utils)
## test block
# y_mat<- all_ba
# x_mat<- all_abun_clr[,-86]
# cov_mat<- all_covar
# covar<- covar1
## test block
my_lm<-function(y,x){
# y<-y_mat[,1]
# x<-x_mat[,1]
beta <- NA
se <- NA
p.value <- NA
N <- NA
#y_uniq_N <- NA
#x_uniq_N <- NA
#y_non_zero_N <-NA
#x_non_zero_N <-NA
#y_non_zero_rate <-NA
#x_non_zero_rate <-NA
lm_input<-data.frame(Y = y, X = x,cov_mat[,covar]) %>% sapply(as.numeric) %>% na.omit
#lm_input<-data.frame(Y = y, X = x) %>% sapply(as.numeric) %>% na.omit
N <- nrow(lm_input)
#y_uniq_N <- length(unique(lm_input[,"Y"]))
#x_uniq_N <- length(unique(lm_input[,"X"]))
#y_non_zero_N <- sum(!isZero(lm_input[,"Y"]))
#x_non_zero_N <- sum(!isZero(lm_input[,"X"]))
#y_non_zero_rate<-y_non_zero_N/N
#x_non_zero_rate<-x_non_zero_N/N
lm_input <- apply(lm_input, 2, qtrans) %>% as.data.frame
try(lm_res <- summary(lm(Y~.,data = lm_input)), silent = T)
indv<-'X'
try(beta <- lm_res$coefficients[match(indv,rownames(lm_res$coefficients)),1],silent = T)
try(se <- lm_res$coefficients[match(indv,rownames(lm_res$coefficients)),2], silent = T)
try(p.value <- lm_res$coefficients[match(indv,rownames(lm_res$coefficients)),4],silent = T)
try(return(list(beta = beta,
se = se,
p.value = p.value,
N = N)),
#y_uniq_N = y_uniq_N,
#x_uniq_N = x_uniq_N,
#y_non_zero_N = y_non_zero_N,
#x_non_zero_N = x_non_zero_N,
#y_non_zero_rate = y_non_zero_rate,
#x_non_zero_rate= x_non_zero_rate)),
silent = T)
}
y_x<-sapply(
as.data.frame(y_mat),
function(x) Map(function(a,b) my_lm(a,b),
list(x),
as.data.frame(x_mat)
)
)
y_x.unlist <- matrix(unlist(y_x), ncol = 4, byrow = T)
# beta matrix
y_x.beta<-matrix(y_x.unlist[,1],ncol = ncol(y_mat), byrow = F)
colnames(y_x.beta)<-colnames(y_mat)
rownames(y_x.beta)<-colnames(x_mat)
y_x_edge_beta <- melt(y_x.beta)
y_x_edge<-data.frame(y_x_edge_beta[,c(2,1)],
as.data.frame(y_x.unlist),
fdr.p = p.adjust(y_x.unlist[,3], method = "fdr"),
bonferroni.p = p.adjust(y_x.unlist[,3], method = "bonferroni"))
colnames(y_x_edge)<-c("Y", "X", "Beta","SE", "p","N","fdr.p","bonferroni.p")
return(y_x_edge)
}
## Meta-analysis
my_meta_lm <- function(inVec, study_name, beta_col, se_col) {
require(meta)
require(metafor)
## test start
#inVec<-inDf[80,]
#study_name<-c("PRJEB22863", "PRJNA397906","PRJNA399742","PRJNA751792","PRJNA762360","PRJNA770295")
#beta_col<-seq(15,86,by=12)
#se_col<-seq(16,86,by=12)
## test end
study_beta <- inVec[beta_col] %>% as.numeric
study_se <- inVec[se_col] %>% as.numeric
#study_beta<-c(0.7, 0.65)
#study_se<-c(0.07, 0.08)
#stydy_n<-c(1000, 300)
in_file <- data.frame(study_name, study_beta, study_se)
m.hksj <- metagen(
study_beta,
study_se,
data = in_file,
studlab = study_name
)
m.hksj.res <- summary(m.hksj)
return(
list(
Meta.beta = m.hksj.res$random$TE,
Meta.se = m.hksj.res$random$seTE,
Meta.p = m.hksj.res$random$p,
Meta.I2 = m.hksj.res$I2,
Meta.hetero.p = m.hksj$pval.Q
)
)
}
## Batch meta-analysis
my_batch_meta_lm <- function(inDf,study_name,beta_col,se_col) {
## test start
#inDf<-vsv_crass_lm_res.edge[c(1:10),]
#study_name<-c("LLD", "300OB","IBD")
#beta_col<-c(3,10,17)
#se_col<-c(4,11,18)
## test end
batch_res <- apply(
inDf,
1,
my_meta_lm,
study_name = study_name,
beta_col = beta_col,
se_col = se_col
)
batch_res.unlist <- matrix(unlist(batch_res), ncol = 5, byrow = T)
colnames(batch_res.unlist) <-
c('Meta.beta', 'Meta.SE', "Meta.p", "Meta.I2", "Meta.hetero.p")
batch_res_edge <- cbind(inDf, batch_res.unlist)
# add p adjust
batch_res_edge <- data.frame(
as.data.frame(batch_res_edge),
Meta.fdr.p = p.adjust(batch_res_edge$Meta.p, method = "fdr"),
Meta.bonferroni.p = p.adjust(batch_res_edge$Meta.p, method = "bonferroni")
)
return(batch_res_edge)
}
lm_btw_mats_adjAbun<-function(y_mat, x_mat, cov_mat, covar, abun, info, abun_col){
#y_mat<-ob_contin[,c(1:10)]
#x_mat<-ob_vsv[,c(1:100)]
#cov_mat<-ob_basic
#covar<-covar
#abun<-ob_abun_clr
#info<-info
cov_mat<-cbind(cov_mat, abun)
y_x.edge<-NULL
for (i in c(1:nrow(info))){
#i<-19
cat(paste(i,info$organism[i],"\n"))
spe_name<-str_replace_all(info$organism[i],"\\[","\\\\\\[") %>% str_replace_all("\\]","\\\\\\]")
x_mat_i<-x_mat[,grep(spe_name,colnames(x_mat))]
if(dim(x_mat_i)[2]>0){
if(info[i,abun_col]%in%colnames(abun)){
covar_i<-c(covar,info[i,abun_col])
y_x_i <- lm_btw_mats(y_mat, x_mat_i, cov_mat, covar_i)
y_x_i$Adjust_Abundance<-"Yes"
y_x.edge<-rbind(y_x.edge,y_x_i)
}else{
covar_i<-covar
y_x_i <- lm_btw_mats(y_mat, x_mat_i, cov_mat, covar_i)
y_x_i$Adjust_Abundance<-"No"
y_x.edge<-rbind(y_x.edge,y_x_i)
}
}
}
y_x.edge<-as.data.frame(y_x.edge)
y_x.edge$fdr.p<-p.adjust(y_x.edge$p,method = 'fdr')
y_x.edge$bonferroni.p<-p.adjust(y_x.edge$p,method = 'bonferroni')
return(y_x.edge)
}
lm_btw_mats_adjAbun2<-function(y_mat, x_mat, cov_mat, covar, abun, info, abun_col){
#y_mat<-vsgv_lld
#x_mat<-lld_exp[,c(1:10)]
#cov_mat<-lld_basic
#covar<-covar
#abun<-lld_abun_clr
#info<-info
#abun_col<-9
cov_mat<-cbind(cov_mat, abun)
y_x.edge<-NULL
for (i in c(1:nrow(info))){
i<-1
cat(paste(i,info$organism[i],"\n"))
spe_name<-str_replace_all(info$organism[i],"\\[","\\\\\\[") %>% str_replace_all("\\]","\\\\\\]")
y_mat_i<-y_mat[,grep(spe_name,colnames(y_mat))]
if(dim(y_mat_i)[2]>0){
if(info[i,abun_col]%in%colnames(abun)){
covar_i<-c(covar,info[i,abun_col])
y_x_i <- lm_btw_mats(y_mat_i, x_mat, cov_mat, covar_i)
y_x_i$Adjust_Abundance<-"Yes"
y_x.edge<-rbind(y_x.edge,y_x_i)
}else{
covar_i<-covar
y_x_i <- lm_btw_mats(y_mat_i, x_mat, cov_mat, covar_i)
y_x_i$Adjust_Abundance<-"No"
y_x.edge<-rbind(y_x.edge,y_x_i)