WaveICA: WaveICA method to remove batch effects
In dengkuistat/WaveICA: WaveICA
Description
Usage
Arguments
Value
Author(s)
Examples
Description
Removing batch effects for metabolomics data.
Usage
1
Arguments
data
Sample-by-matrix metabolomics data.
wf
selecting wavelet functions, the default is "haar".
batch
batch labels.
group
denoting the biological group such as disease vs group.
This param is optional. The default is NULL.
K
The maximal component that ICA decomposes.
t
The threshold to consider a component associate with the batch,
should be between 0 and 1.
t2
The threshold to consider a component associate with the group,
should be between 0 and 1.
alpha
The trade-off value between the independence of samples and those of variables and should be between 0 and 1.
Value
A list that contains the clean data.
Author(s)
Kui deng
dengkui_stat@163.com
Examples
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## load the demo data
data(Amide_data, package = "WaveICA")
##create a folder for demo
dir.create("Demo for WaveICA")
setwd("Demo for WaveICA")
data_Amide_stat_merge_order<-Amide_data[order(Amide_data$injection.order),]
data.Amide.stat_order<-data_Amide_stat_merge_order[,-c(1:5)]
### Generating group and batch
data_Amide_stat_merge_order$group[data_Amide_stat_merge_order$group=="QC"]<-2
group_zong_Amide<-as.numeric(data_Amide_stat_merge_order$group)
batch_zong_Amide<-data_Amide_stat_merge_order$batch
group_sample_Amide<-group_zong_Amide[group_zong_Amide!=2]
batch_sample_Amide<-batch_zong_Amide[group_zong_Amide!=2]
batch_qc_Amide<-batch_zong_Amide[group_zong_Amide==2]
##### Separation of QC samples and subject samples
data.Amide.stat.order.sample<-data.Amide.stat_order[group_zong_Amide!=2,]
data.Amide.stat.order.QC<-data.Amide.stat_order[group_zong_Amide==2,]
#### PCA score plots
bitmap("PCA_original(group).tiff",type="jpeg",res=500)
pca(data=data.Amide.stat_order,id=group_zong_Amide+1,plot=2,label=c("CE","CRC","QC"))
dev.off()
bitmap("PCA_original(batch).tiff",type="jpeg",res=500)
pca(data=data.Amide.stat_order,id=batch_zong_Amide,plot=2,label=c("Batch1","Batch2","Batch3","Batch4"))
dev.off()
#### Dustances between QCS
pc.original<-prcomp(data.Amide.stat_order,scale.=T)
pc.original_select<-pc.original$x[group_zong_Amide==2,1:3]
dist.original<-dist(pc.original_select,method ="euclidean")
dist.original<-as.matrix(dist.original)
sum(dist.original)/(85*85-85)
##### Distances between subject samples
pc.original<-prcomp(data.Amide.stat_order,scale.=T)
pc.original_select<-pc.original$x[group_zong_Amide!=2,1:3]
dist.original<-dist(pc.original_select,method ="euclidean")
dist.original<-as.matrix(dist.original)
sum(dist.original)/(644*644-644)
## Variable selection
univariate_original_Amide<-var_select(data=data.Amide.stat.order.sample,label=group_sample_Amide,t.test=T,Wilcox=F,AUC=F,FDR=T,VIP=T,FC=T,comps=3)
var_select_original_Amide<-rownames(univariate_original_Amide[univariate_original_Amide$VIP>1&univariate_original_Amide$P.FDR<0.05,])
## Predictive accuracy
svm.model.Amide<-SVM_MODEL(X=data.Amide.stat.order.sample[,var_select_original_Amide],Y=group_sample_Amide,kernel="radial",kfold=5)
#### Predictive accuracy with the same number of variables
univariate_original_Amide_order<-univariate_original_Amide[order(univariate_original_Amide$VIP,decreasing=T),]
auc_zong_original_Amide<-c()
for (i in seq(50,1000,50)){
cat(paste("###########################",i,"variable####################\n"))
var_select_original<-rownames(univariate_original_Amide_order)[1:i]
svm.model.original<-SVM_MODEL(X=data.Amide.stat.order.sample[,var_select_original],Y=group_sample_Amide,kernel="radial",kfold=5)
AUC<-svm.model.original$auc
auc_zong_original_Amide<-c(auc_zong_original_Amide,AUC)
}
#### Heatmap of correlation in QCS
library(ggfortify)
library(RColorBrewer)
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
bitmap(file="heatmap_Original.tiff",type="jpeg",res=500)
rownames(data.Amide.stat.order.QC)<-paste("QC",1:85,sep="")
cor_original<-cor(t(data.Amide.stat.order.QC))
autoplot(cor_original,geom="tile")+scale_fill_gradientn(colors=myPalette(4),limits=c(0.5,1),name="r value")+labs(x="",y="")+
theme(axis.text.x=element_text(angle=90))+
scale_x_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
scale_y_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
theme(axis.text=element_text(face="bold"))+
guides(fill=F)
dev.off()
## Scatterplot Matrix
bitmap(file="QC_scatterplotmatrix_Original.tiff",type="jpeg",res=500)
pairs(~QC21+QC25+QC34+QC46+QC62+QC68,data=log(t(data.Amide.stat.order.QC)),pch=16,gap=0.5,font.labels = 2,cex=0.8)
dev.off()
######################## Applying WaveICA ######################
data_wave_reconstruct_Amide<-WaveICA(data=data.Amide.stat_order,wf="haar",batch=batch_zong_Amide,group=group_zong_Amide,K=20,t=0.05,t2=0.05,alpha=0)
data_Amide_zong_wave<-data_wave_reconstruct_Amide$data_wave
data_Amide_sample_wave<-data_Amide_zong_wave[group_zong_Amide!=2,]
data_Amide_qc_wave<-data_Amide_zong_wave[group_zong_Amide==2,]
### PCA score plot
bitmap(file="PCA_WaveICA(group).tiff",type="jpeg",res=500)
pca(data=data_Amide_zong_wave,id=group_zong_Amide+1,plot=2,label=c("CE","CRC","QC"))
dev.off()
bitmap(file="PCA_WaveICA(batch).tiff",type="jpeg",res=500)
pca(data=data_Amide_zong_wave,id=batch_zong_Amide,plot=2,label=c("Batch1","Batch2","Batch3","Batch4"))
dev.off()
#### Distances between QCS
pc.WaveICA<-prcomp(data_Amide_zong_wave,scale.=T)
pc.WaveICA_select<-pc.WaveICA$x[group_zong_Amide==2,1:3]
dist.WaveICA<-dist(pc.WaveICA_select,method ="euclidean")
dist.WaveICA<-as.matrix(dist.WaveICA)
sum(dist.WaveICA)/(85*85-85)
#### Distances between subject samples
pc.WaveICA<-prcomp(data_Amide_zong_wave,scale.=T)
pc.WaveICA_select<-pc.WaveICA$x[group_zong_Amide!=2,1:3]
dist.WaveICA<-dist(pc.WaveICA_select,method ="euclidean")
dist.WaveICA<-as.matrix(dist.WaveICA)
sum(dist.WaveICA)/(644*644-644)
### Variable selection
univariate_wave_Amide<-var_select(data=data_Amide_sample_wave,label=group_sample_Amide,t.test=T,Wilcox=F,AUC=F,FDR=T,VIP=T,FC=T,comps=3)
var_select_wave_Amide<-rownames(univariate_wave_Amide[univariate_wave_Amide$VIP>1&univariate_wave_Amide$P.FDR<0.05,])
## Predictive accuracy
svm.model.wave.Amide<-SVM_MODEL(X=data_Amide_sample_wave[,var_select_wave_Amide],Y=group_sample_Amide,kernel="radial",kfold=5)
### Predictive accuracy with the same number of variables
univariate_wave_Amide_order<-univariate_wave_Amide[order(univariate_wave_Amide$VIP,decreasing=T),]
auc_zong_wave_Amide<-c()
for (i in seq(50,1000,50)){
cat(paste("###########################",i,"variable####################\n"))
var_select_wave<-rownames(univariate_wave_Amide_order)[1:i]
svm.model.wave<-SVM_MODEL(X=data_Amide_sample_wave[,var_select_wave],Y=group_sample_Amide,kernel="radial",kfold=5)
AUC<-svm.model.wave$auc
auc_zong_wave_Amide<-c(auc_zong_wave_Amide,AUC)
}
## Pearson correlation coefficients
corr_wave_Amide<-correlation(data.Amide.stat.order.QC,data_Amide_qc_wave,method="pearson")
mean(corr_wave_Amide$cor_before)
mean(corr_wave_Amide$cor_after)
library(ggfortify)
library(RColorBrewer)
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
#### Heatmap of correlation in QCS
bitmap(file="Heatmap_WaveICA.tiff",type="jpeg",res=500)
rownames(data_Amide_qc_wave)<-paste("QC",1:85,sep="")
cor_WaveICA<-cor(t(data_Amide_qc_wave))
autoplot(cor_WaveICA,geom="tile")+scale_fill_gradientn(colors=myPalette(4),limits=c(0.5,1),name="r value")+labs(x="",y="")+
theme(axis.text.x=element_text(angle=90))+
scale_x_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
scale_y_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
theme(axis.text=element_text(face="bold"))+
guides(fill=F)
dev.off()
## Scatterplot Matrix
bitmap(file="QC_scatterplotmatrix_WaveICA.tiff",type="jpeg",res=500)
pairs(~QC21+QC25+QC34+QC46+QC62+QC68,data=log(t(data_Amide_qc_wave)),pch=16,gap=0.5,font.labels = 2,cex=0.8)
dev.off()
###### Comparing the predictive accuracy
data_auc<-data.frame(auc=c(auc_zong_original_Amide,auc_zong_wave_Amide),var=c(seq(50,1000,50),seq(50,1000,50)),
group=c(rep(1,length(auc_zong_original_Amide)),rep(2,length(auc_zong_wave_Amide))))
library(ggplot2)
bitmap(file="Comparing AUC.tiff",type="jpeg",res=500)
ggplot(data=data_auc,aes(x=var,y=auc,col=factor(group)))+geom_line(aes(linetype=factor(group)),size=1)+
scale_colour_brewer(palette="Set1",labels=c("Original","WaveICA","ComBat","QC-RLSC","ICA"))+
scale_linetype_manual(values=c(2,1,4,3,5),labels=c("Original","WaveICA","ComBat","QC-RLSC","ICA"))+
labs(x="# features selected",y="AUC")+
theme(legend.title = element_blank(),axis.title = element_text(size=rel(1.2),face="bold"),
legend.text=element_text(size=rel(1.2),face="bold"),legend.key.width=unit(1.5,"cm"))
dev.off()
dengkuistat/WaveICA documentation built on Nov. 6, 2021, 6:21 p.m.
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Description Usage Arguments Value Author(s) Examples
Description
Removing batch effects for metabolomics data.
Usage
1 |
Arguments
data |
Sample-by-matrix metabolomics data. |
wf |
selecting wavelet functions, the default is "haar". |
batch |
batch labels. |
group |
denoting the biological group such as disease vs group. This param is optional. The default is NULL. |
K |
The maximal component that ICA decomposes. |
t |
The threshold to consider a component associate with the batch, should be between 0 and 1. |
t2 |
The threshold to consider a component associate with the group, should be between 0 and 1. |
alpha |
The trade-off value between the independence of samples and those of variables and should be between 0 and 1. |
Value
A list that contains the clean data.
Author(s)
Kui deng dengkui_stat@163.com
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | ## load the demo data
data(Amide_data, package = "WaveICA")
##create a folder for demo
dir.create("Demo for WaveICA")
setwd("Demo for WaveICA")
data_Amide_stat_merge_order<-Amide_data[order(Amide_data$injection.order),]
data.Amide.stat_order<-data_Amide_stat_merge_order[,-c(1:5)]
### Generating group and batch
data_Amide_stat_merge_order$group[data_Amide_stat_merge_order$group=="QC"]<-2
group_zong_Amide<-as.numeric(data_Amide_stat_merge_order$group)
batch_zong_Amide<-data_Amide_stat_merge_order$batch
group_sample_Amide<-group_zong_Amide[group_zong_Amide!=2]
batch_sample_Amide<-batch_zong_Amide[group_zong_Amide!=2]
batch_qc_Amide<-batch_zong_Amide[group_zong_Amide==2]
##### Separation of QC samples and subject samples
data.Amide.stat.order.sample<-data.Amide.stat_order[group_zong_Amide!=2,]
data.Amide.stat.order.QC<-data.Amide.stat_order[group_zong_Amide==2,]
#### PCA score plots
bitmap("PCA_original(group).tiff",type="jpeg",res=500)
pca(data=data.Amide.stat_order,id=group_zong_Amide+1,plot=2,label=c("CE","CRC","QC"))
dev.off()
bitmap("PCA_original(batch).tiff",type="jpeg",res=500)
pca(data=data.Amide.stat_order,id=batch_zong_Amide,plot=2,label=c("Batch1","Batch2","Batch3","Batch4"))
dev.off()
#### Dustances between QCS
pc.original<-prcomp(data.Amide.stat_order,scale.=T)
pc.original_select<-pc.original$x[group_zong_Amide==2,1:3]
dist.original<-dist(pc.original_select,method ="euclidean")
dist.original<-as.matrix(dist.original)
sum(dist.original)/(85*85-85)
##### Distances between subject samples
pc.original<-prcomp(data.Amide.stat_order,scale.=T)
pc.original_select<-pc.original$x[group_zong_Amide!=2,1:3]
dist.original<-dist(pc.original_select,method ="euclidean")
dist.original<-as.matrix(dist.original)
sum(dist.original)/(644*644-644)
## Variable selection
univariate_original_Amide<-var_select(data=data.Amide.stat.order.sample,label=group_sample_Amide,t.test=T,Wilcox=F,AUC=F,FDR=T,VIP=T,FC=T,comps=3)
var_select_original_Amide<-rownames(univariate_original_Amide[univariate_original_Amide$VIP>1&univariate_original_Amide$P.FDR<0.05,])
## Predictive accuracy
svm.model.Amide<-SVM_MODEL(X=data.Amide.stat.order.sample[,var_select_original_Amide],Y=group_sample_Amide,kernel="radial",kfold=5)
#### Predictive accuracy with the same number of variables
univariate_original_Amide_order<-univariate_original_Amide[order(univariate_original_Amide$VIP,decreasing=T),]
auc_zong_original_Amide<-c()
for (i in seq(50,1000,50)){
cat(paste("###########################",i,"variable####################\n"))
var_select_original<-rownames(univariate_original_Amide_order)[1:i]
svm.model.original<-SVM_MODEL(X=data.Amide.stat.order.sample[,var_select_original],Y=group_sample_Amide,kernel="radial",kfold=5)
AUC<-svm.model.original$auc
auc_zong_original_Amide<-c(auc_zong_original_Amide,AUC)
}
#### Heatmap of correlation in QCS
library(ggfortify)
library(RColorBrewer)
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
bitmap(file="heatmap_Original.tiff",type="jpeg",res=500)
rownames(data.Amide.stat.order.QC)<-paste("QC",1:85,sep="")
cor_original<-cor(t(data.Amide.stat.order.QC))
autoplot(cor_original,geom="tile")+scale_fill_gradientn(colors=myPalette(4),limits=c(0.5,1),name="r value")+labs(x="",y="")+
theme(axis.text.x=element_text(angle=90))+
scale_x_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
scale_y_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
theme(axis.text=element_text(face="bold"))+
guides(fill=F)
dev.off()
## Scatterplot Matrix
bitmap(file="QC_scatterplotmatrix_Original.tiff",type="jpeg",res=500)
pairs(~QC21+QC25+QC34+QC46+QC62+QC68,data=log(t(data.Amide.stat.order.QC)),pch=16,gap=0.5,font.labels = 2,cex=0.8)
dev.off()
######################## Applying WaveICA ######################
data_wave_reconstruct_Amide<-WaveICA(data=data.Amide.stat_order,wf="haar",batch=batch_zong_Amide,group=group_zong_Amide,K=20,t=0.05,t2=0.05,alpha=0)
data_Amide_zong_wave<-data_wave_reconstruct_Amide$data_wave
data_Amide_sample_wave<-data_Amide_zong_wave[group_zong_Amide!=2,]
data_Amide_qc_wave<-data_Amide_zong_wave[group_zong_Amide==2,]
### PCA score plot
bitmap(file="PCA_WaveICA(group).tiff",type="jpeg",res=500)
pca(data=data_Amide_zong_wave,id=group_zong_Amide+1,plot=2,label=c("CE","CRC","QC"))
dev.off()
bitmap(file="PCA_WaveICA(batch).tiff",type="jpeg",res=500)
pca(data=data_Amide_zong_wave,id=batch_zong_Amide,plot=2,label=c("Batch1","Batch2","Batch3","Batch4"))
dev.off()
#### Distances between QCS
pc.WaveICA<-prcomp(data_Amide_zong_wave,scale.=T)
pc.WaveICA_select<-pc.WaveICA$x[group_zong_Amide==2,1:3]
dist.WaveICA<-dist(pc.WaveICA_select,method ="euclidean")
dist.WaveICA<-as.matrix(dist.WaveICA)
sum(dist.WaveICA)/(85*85-85)
#### Distances between subject samples
pc.WaveICA<-prcomp(data_Amide_zong_wave,scale.=T)
pc.WaveICA_select<-pc.WaveICA$x[group_zong_Amide!=2,1:3]
dist.WaveICA<-dist(pc.WaveICA_select,method ="euclidean")
dist.WaveICA<-as.matrix(dist.WaveICA)
sum(dist.WaveICA)/(644*644-644)
### Variable selection
univariate_wave_Amide<-var_select(data=data_Amide_sample_wave,label=group_sample_Amide,t.test=T,Wilcox=F,AUC=F,FDR=T,VIP=T,FC=T,comps=3)
var_select_wave_Amide<-rownames(univariate_wave_Amide[univariate_wave_Amide$VIP>1&univariate_wave_Amide$P.FDR<0.05,])
## Predictive accuracy
svm.model.wave.Amide<-SVM_MODEL(X=data_Amide_sample_wave[,var_select_wave_Amide],Y=group_sample_Amide,kernel="radial",kfold=5)
### Predictive accuracy with the same number of variables
univariate_wave_Amide_order<-univariate_wave_Amide[order(univariate_wave_Amide$VIP,decreasing=T),]
auc_zong_wave_Amide<-c()
for (i in seq(50,1000,50)){
cat(paste("###########################",i,"variable####################\n"))
var_select_wave<-rownames(univariate_wave_Amide_order)[1:i]
svm.model.wave<-SVM_MODEL(X=data_Amide_sample_wave[,var_select_wave],Y=group_sample_Amide,kernel="radial",kfold=5)
AUC<-svm.model.wave$auc
auc_zong_wave_Amide<-c(auc_zong_wave_Amide,AUC)
}
## Pearson correlation coefficients
corr_wave_Amide<-correlation(data.Amide.stat.order.QC,data_Amide_qc_wave,method="pearson")
mean(corr_wave_Amide$cor_before)
mean(corr_wave_Amide$cor_after)
library(ggfortify)
library(RColorBrewer)
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
#### Heatmap of correlation in QCS
bitmap(file="Heatmap_WaveICA.tiff",type="jpeg",res=500)
rownames(data_Amide_qc_wave)<-paste("QC",1:85,sep="")
cor_WaveICA<-cor(t(data_Amide_qc_wave))
autoplot(cor_WaveICA,geom="tile")+scale_fill_gradientn(colors=myPalette(4),limits=c(0.5,1),name="r value")+labs(x="",y="")+
theme(axis.text.x=element_text(angle=90))+
scale_x_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
scale_y_discrete(limits=paste("QC",c(1,c(1:17)*5),sep=""))+
theme(axis.text=element_text(face="bold"))+
guides(fill=F)
dev.off()
## Scatterplot Matrix
bitmap(file="QC_scatterplotmatrix_WaveICA.tiff",type="jpeg",res=500)
pairs(~QC21+QC25+QC34+QC46+QC62+QC68,data=log(t(data_Amide_qc_wave)),pch=16,gap=0.5,font.labels = 2,cex=0.8)
dev.off()
###### Comparing the predictive accuracy
data_auc<-data.frame(auc=c(auc_zong_original_Amide,auc_zong_wave_Amide),var=c(seq(50,1000,50),seq(50,1000,50)),
group=c(rep(1,length(auc_zong_original_Amide)),rep(2,length(auc_zong_wave_Amide))))
library(ggplot2)
bitmap(file="Comparing AUC.tiff",type="jpeg",res=500)
ggplot(data=data_auc,aes(x=var,y=auc,col=factor(group)))+geom_line(aes(linetype=factor(group)),size=1)+
scale_colour_brewer(palette="Set1",labels=c("Original","WaveICA","ComBat","QC-RLSC","ICA"))+
scale_linetype_manual(values=c(2,1,4,3,5),labels=c("Original","WaveICA","ComBat","QC-RLSC","ICA"))+
labs(x="# features selected",y="AUC")+
theme(legend.title = element_blank(),axis.title = element_text(size=rel(1.2),face="bold"),
legend.text=element_text(size=rel(1.2),face="bold"),legend.key.width=unit(1.5,"cm"))
dev.off()
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