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functions.R
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functions.R
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## 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(factoextra)
library(reshape2)
library(Hmisc)
library(vegan)
library(microbiome)
library(mediation)
library(karyoploteR)
mycolor2_blue_red = c("#28B6F6FF","#EE5250FF")
mycolor2_green_blue <- c("#2EC4B6","#235789")
mycolor3 <- c("#2EC4B6","#235789", "grey50") # c("#4f94cd","#ff4040", "grey50")
## 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
}
## 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$value), 2)
my_labels <- tibble(x.breaks = seq(1.3, 1.3, length.out = length(piepercent)),
y.breaks = cumsum(in1.tibble$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,
segment.color = 'grey50'
)
}
## 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)
}
shared_sv_dis<-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)
}
## 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)
}
get_PCs<-function(inDist, eig.cutoff = 0.6, pc.cutoff = 10){
#inDist <- all_msv_dist_std[[1]]
#eig.cutoff <- 0.6
#eig.cutoff<-as.numeric(eig.cutoff)
pcoa_res <- cmdscale(inDist, k=30, eig = T)
total_eig <- 0
i <- 0
while (total_eig<eig.cutoff & i< pc.cutoff) {
i<-i+1
total_eig <- total_eig + pcoa_res$eig[i]/100
}
if(i<=1){
pcoa<-data.frame(X1 = pcoa_res$points[, 1])
}else{
pcoa <- data.frame(pcoa_res$points)[,c(1:i)]
}
return(list(PCoA= pcoa,PC_num = i,Total_eig = total_eig))
}
## Batch adonis
my_adonis_terms<-function(inDist, inDf, covDf=NULL, covar=NULL,n_parallel = 4,n_permutations = 999){
require(vegan)
## test
#inDist <- distList[[1]]
#inDf <- lld_ba
#covDf <- ibd_covar
#covar <- inCovar
## test
inDf <- as.data.frame(inDf)
rownames(inDf) <- rownames(covDf)
covDf<- covDf[,covar]
inDist_rmna <- dist_rmna(inDist)
covDf_rmna <- na.omit(covDf)
my_adonis<-function(inMat, inVec, covdf = NULL){
## test
#inMat<-inDist_rmna
#inVec<-inDf[,976]
#covdf<-covDf_rmna
## test
inMat_covdf_inter <- intersect(rownames(inMat),rownames(covdf))
inMat_covdf_inVec_inter <- intersect(rownames(inDf)[!is.na(inVec)], inMat_covdf_inter)
inMat_rmna <- inMat[match(inMat_covdf_inVec_inter,rownames(inMat)),
match(inMat_covdf_inVec_inter,colnames(inMat))]
inVec_rmna <- inVec[match(inMat_covdf_inVec_inter, rownames(inDf))]
covdf_rmna <- covdf[match(inMat_covdf_inVec_inter, rownames(covdf)),]
in_cov <- data.frame(covdf_rmna,inVec_rmna)
sample_size<-nrow(in_cov)
if(sample_size>10){
adonis_res <- adonis2(as.dist(inMat_rmna)~.,data = in_cov,parallel = n_parallel,permutations = n_permutations)
return(list(adonis_res$aov.tab[ncol(in_cov),5],adonis_res$aov.tab[ncol(in_cov),6], sample_size))
}else{
return(list(NA,NA, sample_size))
}
}
adonis_res_batch <- apply(inDf, 2, my_adonis,inMat = inDist_rmna, covdf = covDf_rmna)
adonis_res_batch.unlist <- matrix(unlist(adonis_res_batch), ncol = 3, byrow = T)
colnames(adonis_res_batch.unlist)<-c("R2", "P", "N")
return(adonis_res_batch.unlist)
}
my_adonis_terms_adjAbun<-function(distList, inDf, covDf, covar,info,n_parallel = 4,n_permutations = 999){
#distList<-ibd_msv_dist_std[c(20)]
#inDf<-ibd_fmetab
#covDf<-ibd_covar
#covar<-covar1
#info<-info
res_table<-NULL
for (i in 1:length(distList)) { #
#i<-1
cat(paste(i,"\n"))
n<-names(distList)[i] %>%
str_replace_all("msv_","") %>%
match(., info$organism)
inCovar<-c(covar, info$organism[n])
adonis_res<-my_adonis_terms(distList[[i]], inDf, covDf = covDf, covar = inCovar,n_parallel = n_parallel,n_permutations = n_permutations)
adonis_res_df<-data.frame(Species = rep(info$Short_name[n], dim(adonis_res)[1]),
BA = colnames(inDf),
as.data.frame(adonis_res))
res_table<-rbind(res_table, adonis_res_df)
}
res_table$Species<-as.character(res_table$Species)
res_table$BA<-as.character(res_table$BA)
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$BA)),
byrow = T)
rownames(r2_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(r2_mat)<-colnames(inDf)
# P
p_mat<-matrix(data = res_table$P,
nrow = length(unique(res_table$Species)),
ncol = length(unique(res_table$BA)),
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$BA)),
byrow = T)
rownames(fdr_mat)<-names(distList)[1:length(distList)] %>% str_replace_all("msv_", "") %>% match(.,info$organism) %>% info$Short_name[.]
colnames(fdr_mat)<-colnames(inDf)
return(list(table = res_table,
r2 = r2_mat,
P = p_mat,
FDR = fdr_mat))
}
my_meta_p <- function(inVec, study_name, n_col, p_col) {
require(metap)
#require(metafor)
#inVec<-cbind_adonis_res.table[20,]
#study_name<-c("LLD", "300OB")
#n_col<-c(10,15)
#_col<-c(9,14)
study_n <- inVec[n_col] %>% as.numeric
study_p <- inVec[p_col] %>% as.numeric
inDf <- data.frame(study_name, study_n, study_p)
Wi=sqrt(study_n)
#Convert p-values to Z-scores
Zi=qnorm(1-(study_p/2))
# Meta-zscore
Z=(sum(Zi*Wi)/sqrt(sum(Wi^2)))
# Convert Z-score to p-value
MetaP=2*pnorm(-abs(Z))
#Cochran Q-test (test heterogeneity of your meta-analysis)
WeightedZ= sum(sqrt(study_n)*Zi)
totalSample=sum(study_n)
#Calculate expected Z
expected_Z= sqrt(study_n)*WeightedZ/totalSample
het_Sum= sum((Zi-expected_Z)*(Zi-expected_Z))
#Get p-value of heterogeneity test!
my_pvalue_co=pchisq(het_Sum, lower.tail = F, df=length(study_p)-1)
return(
list(
Meta.p = MetaP,
Meta.hetero = het_Sum,
Meta.hetero.p = my_pvalue_co
)
)
}
## Batch meta-analysis
my_batch_meta_p <- function(inDf,study_name,n_col,p_col,row_var_col = 1,col_var_col = 2) {
#inDf<-cbind_adonis_res.table[c(1:10),]
#study_name<-c("LLD", "300OB")
#n_col<-c(10,15)
#p_col<-c(9,14)
#row_var_col<-1
#col_var_col<-2
batch_res <- apply(
inDf,
1,
my_meta_p,
study_name = study_name,
n_col = n_col,
p_col = p_col
)
batch_res.unlist <- matrix(unlist(batch_res), ncol = 3, byrow = T)
colnames(batch_res.unlist) <-
c('Meta.p', "Meta.hetero", "Meta.hetero.p")
batch_res_edge <- cbind(inDf, batch_res.unlist)
rowName <- unique(inDf[, row_var_col])
colName <- unique(inDf[, col_var_col])
N_row <- length(rowName)
N_col <- length(colName)
batch_res.p <- matrix(batch_res.unlist[, 1], ncol = N_col, byrow = F)
colnames(batch_res.p) <- colName
rownames(batch_res.p) <- rowName
batch_res.p[is.na(batch_res.p)] <- 0
# add p adjust
batch_res_edge <- data.frame(
as.data.frame(batch_res_edge),
Meta.fdr.p = p.adjust(batch_res_edge$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$Meta.bonferroni.p,
nrow = N_row,
ncol = N_col,
byrow = T
)
colnames(batch_res.bon) <- colName
rownames(batch_res.bon) <- rowName
batch_res.bon[is.na(batch_res.bon)] <- 1
return(
list(
table = batch_res_edge,
p = batch_res.p,
fdr = batch_res.fdr,
bonferroni = batch_res.bon
)
)
}
## The Normal Quantile Transformation
qtrans<-function(x){
k<-!is.na(x)
k<-which(x!="-999")
ran<-rank(as.numeric(x[k]))
y<-qnorm((1:length(k)-0.5)/length(k))
x[k]<-y[ran]
x
}
## linear model
lm_btw_mats<-function(y_mat,x_mat,cov_mat,covar){
require(reshape2)
require(R.utils)
## test block
#y_mat<- y_mat_i #lld_intri[,c(1:3)]
#x_mat<- x_mat #lld_vsv[,c(1:3)]
#cov_mat<- cov_mat# lld_covar
#covar<- covar_i #covar
## test block
my_lm<-function(y,x){
# y<-y_mat[,1]
# x<-x_mat[,1]
beta <- NA
se <- NA
p.value <- NA
N <- NA
y_uniq_N <- NA
x_uniq_N <- NA
y_non_zero_N <-NA
x_non_zero_N <-NA
y_non_zero_rate <-NA
x_non_zero_rate <-NA
lm_input<-data.frame(Y = y, X = x,cov_mat[,covar]) %>% sapply(as.numeric) %>% na.omit
N <- nrow(lm_input)
y_uniq_N <- length(unique(lm_input[,"Y"]))
x_uniq_N <- length(unique(lm_input[,"X"]))
y_non_zero_N <- sum(!isZero(lm_input[,"Y"]))
x_non_zero_N <- sum(!isZero(lm_input[,"X"]))
y_non_zero_rate<-y_non_zero_N/N
x_non_zero_rate<-x_non_zero_N/N
lm_input <- apply(lm_input, 2, qtrans) %>% as.data.frame
try(lm_res <- summary(lm(Y~.,data = lm_input)), silent = T)
indv<-'X'
try(beta <- lm_res$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$coefficients)),4],silent = T)
try(return(list(beta = beta,
se = se,
p.value = p.value,
N = N,
y_uniq_N = y_uniq_N,
x_uniq_N = x_uniq_N,
y_non_zero_N = y_non_zero_N,
x_non_zero_N = x_non_zero_N,
y_non_zero_rate = y_non_zero_rate,
x_non_zero_rate= x_non_zero_rate)),
silent = T)
}
y_x<-sapply(
as.data.frame(y_mat),
function(x) Map(function(a,b) my_lm(a,b),
list(x),
as.data.frame(x_mat)
)
)
y_x.unlist <- matrix(unlist(y_x), ncol = 10, byrow = T)
# beta matrix
y_x.beta<-matrix(y_x.unlist[,1],ncol = ncol(y_mat), byrow = F)
colnames(y_x.beta)<-colnames(y_mat)
rownames(y_x.beta)<-colnames(x_mat)
y_x_edge_beta <- melt(y_x.beta)
y_x_edge<-data.frame(y_x_edge_beta[,c(2,1)],
as.data.frame(y_x.unlist),
fdr.p = p.adjust(y_x.unlist[,3], method = "fdr"),
bonferroni.p = p.adjust(y_x.unlist[,3], method = "bonferroni"))
colnames(y_x_edge)<-c("Y", "X", "Beta","SE", "p","N","y_uniq_N","x_uniq_N", "y_non_zero_N", "x_non_zero_N", "y_non_zero_rate","x_non_zero_rate","fdr.p","bonferroni.p")
return(y_x_edge)
}
lm_btw_mats_v2<-function(y_mat,x_mat,cov_mat,covar){
require(reshape2)
require(R.utils)
## test block
# y_mat<- all_abun_clr[,info$organism] #lld_intri[,c(1:3)]
# x_mat<- as.data.frame(all_phen[,c("IBD")]) #lld_vsv[,c(1:3)]
# cov_mat<- all_covar# lld_covar
# covar<- covar1 #covar
## 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
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)
try(anv_res <- anova(lm(Y~.,data = lm_input)), silent = T)
r_sq<-anv_res$`Sum Sq`/sum(anv_res$`Sum Sq`)
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(R_sq <- r_sq[match(indv, rownames(anv_res))])
try(anv_P <- anv_res[match(indv, rownames(anv_res)), 5])
try(return(list(beta = beta,
se = se,
p.value = p.value,
R_sq = R_sq,
anv_P = anv_P,
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 = 12, 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"),
anv_fdr.p = p.adjust(y_x.unlist[,5], method = "fdr"),
anv_bonferroni.p = p.adjust(y_x.unlist[,5], method = "bonferroni"))
colnames(y_x_edge)<-c("Y", "X", "Beta","SE", "p","R_sq","anv_p",
"N","y_uniq_N","x_uniq_N", "y_non_zero_N", "x_non_zero_N", "y_non_zero_rate","x_non_zero_rate",
"fdr.p","bonferroni.p", "anv_fdr.p", "anv_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<-vsv_crass_lm_res.edge[100,]
#study_name<-c("LLD", "300OB", "IBD")
#beta_col<-c(3,10,17)
#se_col<-c(4,11,18)
## 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)
inDf <- data.frame(study_name, study_beta, study_se)
m.hksj <- metagen(
study_beta,
study_se,
data = inDf,
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$I2$TE,
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$p,method = 'bonferroni')
return(y_x.edge)
}
lm_btw_mats_adjAbun2<-function(y_mat, x_mat, cov_mat, covar, abun, info, abun_col){
# y_mat<-all_vsv.sig
# x_mat<-all_exp.sig
# cov_mat<-all_covar
# covar<-covar
# abun<-all_abun_clr
# info<-info
# abun_col<-9
cov_mat<-cbind(cov_mat, abun)
y_x.edge<-NULL
for (i in c(1:nrow(info))){
# i<-7
cat(paste(i,info$organism[i],"\n"))
spe_name<-str_replace_all(info$organism[i],"\\[","\\\\\\[") %>% str_replace_all("\\]","\\\\\\]")
y_mat_i<-y_mat[,grep(spe_name,colnames(y_mat))]
if(!is.null(dim(y_mat_i)[2])){
if(dim(y_mat_i)[2]>0){
if(info[i,abun_col]%in%colnames(abun)){
covar_i<-c(covar,info[i,abun_col])
y_x_i <- lm_btw_mats(y_mat_i, x_mat, cov_mat, covar_i)
y_x_i$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$Adjust_Abundance<-"No"
y_x.edge<-rbind(y_x.edge,y_x_i)
}
}
}
}
y_x.edge<-as.data.frame(y_x.edge)
y_x.edge$fdr.p<-p.adjust(y_x.edge$p,method = 'fdr')
y_x.edge$bonferroni.p<-p.adjust(y_x.edge$p,method = 'bonferroni')
return(y_x.edge)
}
lm_btw_mats_adjAbunPCs<-function(y_mat, x_mat, cov_mat, covar, abun, pc_mat, info, abun_col){
# y_mat<-lld_ba[,c(1:10)]
# x_mat<-vsgv_lld[,c(1:100)]
# cov_mat<-lld_basic
# covar<-covar
# abun<-lld_abun_clr
# pc_mat<-lld_msv_pc_cum0.6
# info<-info
cov_mat<-cbind(cov_mat, abun,pc_mat)
y_x.edge<-NULL
for (i in c(1:nrow(info))){
# i<-19
cat(paste(i,info$organism[i],"\n"))
spe_name<-str_replace_all(info$organism[i],"\\[","\\\\\\[") %>% str_replace_all("\\]","\\\\\\]")
x_mat_i<-x_mat[,grep(spe_name,colnames(x_mat))]
if(dim(x_mat_i)[2]>0){
if(info[i,abun_col]%in%colnames(abun)){
covar_i<-c(covar,colnames(cov_mat)[grep(spe_name, colnames(cov_mat))])
y_x_i <- lm_btw_mats(y_mat, x_mat_i, cov_mat, covar_i)
y_x_i$Adjust_Abundance<-"Yes"
y_x.edge<-rbind(y_x.edge,y_x_i)
}else{
covar_i<-c(covar,colnames(cov_mat)[grep(spe_name, colnames(cov_mat))])
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$p,method = 'bonferroni')
return(y_x.edge)
}
## logistic model
lr_btw_mats<-function(y_mat,x_mat,cov_mat,covar, direction = c(1,1)){
# mat0: phenotypes, mat1: microbiome, mat2: covar
# direction: 1 means samples in row and variables in column; 2 means samples in column and variables in row
require(reshape2)
require(glmnet)
require(R.utils)
## test block
y_mat <- y_mat
#x_mat <- x_mat_i
#cov_mat <- cov_mat
#covar <- covar #covar
#direction<-c(1,1)
## test block
my_lm<-function(y,x){
#y<-y_mat[,2]
#x<-x_mat[,1]
beta <- NA
se <- NA
p.value <- NA
N <- NA
y_uniq_N <- NA
x_uniq_N <- NA
y_non_zero_N <-NA
x_non_zero_N <-NA
y_non_zero_rate <-NA
x_non_zero_rate <-NA
y_0_N<-NA
y_1_N<-NA
lm_input<-data.frame(Y = y, X = x,cov_mat[,covar]) %>% sapply(as.numeric) %>% na.omit
N <- nrow(lm_input)
y_uniq_N <- length(unique(lm_input[,"Y"]))
x_uniq_N <- length(unique(lm_input[,"X"]))
y_non_zero_N <- sum(!isZero(lm_input[,"Y"]))
x_non_zero_N <- sum(!isZero(lm_input[,"X"]))
y_non_zero_rate<-y_non_zero_N/N
x_non_zero_rate<-x_non_zero_N/N
y_0_N<-sum(lm_input[,"Y"]==0)
y_1_N<-sum(lm_input[,"Y"]==1)
lm_input_tmp <- apply(lm_input[,c(2:ncol(lm_input))], 2, qtrans) %>% as.data.frame
lm_input<-data.frame(Y = lm_input[,1],lm_input_tmp)
try(lm_res <- summary(glm(Y~., data = lm_input, family = 'binomial')))
indv<-'X'
try(beta <- lm_res$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$coefficients)),4],silent = T)
try(return(list(beta = beta,
se = se,
p.value = p.value,
N = N,
y_uniq_N = y_uniq_N,
x_uniq_N = x_uniq_N,
y_non_zero_N = y_non_zero_N,
x_non_zero_N = x_non_zero_N,
y_non_zero_rate = y_non_zero_rate,
x_non_zero_rate= x_non_zero_rate,
y_0_N = y_0_N,
y_1_N = y_1_N)),
silent = T)
}
y_x<-sapply(
as.data.frame(y_mat),
function(x) Map(function(a,b) my_lm(a,b),
list(x),
as.data.frame(x_mat)
)
)
y_x.unlist <- matrix(unlist(y_x), ncol = 12, byrow = T)
# beta matrix
y_x.beta<-matrix(y_x.unlist[,1],ncol = ncol(as.data.frame(y_mat)), byrow = F)
colnames(y_x.beta)<-colnames(y_mat)
rownames(y_x.beta)<-colnames(x_mat)
y_x_edge_beta <- melt(y_x.beta)
y_x_edge<-data.frame(y_x_edge_beta[,c(2:1)],
as.data.frame(y_x.unlist),
fdr.p = p.adjust(y_x.unlist[,3], method = "fdr"),
bonferroni.p = p.adjust(y_x.unlist[,3], method = "bonferroni"))
colnames(y_x_edge)<-c("Y", "X", "Beta","SE", "p","N","y_uniq_N","x_uniq_N", "y_non_zero_N", "x_non_zero_N", "y_non_zero_rate","x_non_zero_rate","y_0_N","y_1_N","fdr.p","bonferroni.p")
return(y_x_edge)
}
lr_btw_mats_adjAbun<-function(y_mat, x_mat, cov_mat, covar, abun,info, abun_col){
# y_mat<-all_phen[,c("IBD")]
# x_mat<-all_vsv
# cov_mat<-all_covar
# covar<-covar
# abun<-all_abun_clr
# info<-info
# abun_col = 9
cov_mat<-cbind(cov_mat, abun)
y_x.edge<-NULL
for (i in c(1:nrow(info))){
# i<-1
cat(paste(i,info$organism[i],"\n"))
spe_name<-str_replace_all(info$organism[i],"\\[","\\\\\\[") %>% str_replace_all("\\]","\\\\\\]")
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 <- lr_btw_mats(y_mat, x_mat_i, cov_mat, covar_i)
y_x.edge<-rbind(y_x.edge,y_x_i)
}else{
covar_i<-covar
y_x_i <- lr_btw_mats(y_mat, x_mat_i, cov_mat, covar_i)
y_x.edge<-rbind(y_x.edge,y_x_i)
}