The goal of PureMeta is to provide functionality for extracting tumor cells-based gene expression profiles (GEP) from bulk RNA-seq or microarray data, and generating tumor cells-based metabolic phenotype (i.e. downregulated, neutral, upregulated) for seven metabolic pathways.
You can install the development version of PureMeta from GitHub with:
# install.packages("devtools")
devtools::install_github("WangKang-Leo/PureMeta")This is a basic example which shows you how to run PureMeta:
Step1: Extracting tumor cell GEP from bulk RNA-seq/microarray data, where you also need tumor adjacent normal tissue GEP as control. The bulk tumor and normal GEP should be generated from the same experiments (no significant batch effect existed there). Make sure GEP data is in normal (not log) space. This example used 30 tumor and 5 non-cancerous breast tissue (normal) GEP from GSE87455.
library(PureMeta)
## Only run 10 samples, this example would take ~10min
TC_GEP=Extract_TC_GEP(tumor[,1:10],normal) The results included inferred tumor purity(0-1) as well as tumor cell GEP.
To show distribution of purity.
hist(TC_GEP$purity,xlab = 'tumor purity', main = 'Distribution of purity derived from PureMeta')
Step2: Generating metabolic phenotype using tumor cell GEP derived from step1, or using tumor bulk GEP.
For bulk GEP
# run MetaPhenotype for 10 samples, p cut-off value was set as 0.1
Bulk_phenotype=MetaPhenotype(tumor[,1:10],0.1)
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
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#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
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#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
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#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
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#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
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#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...Calculate and show proportion of metabolic phenotype for seven pathway
library(ggpubr);library(tidyverse)
phenotype=Bulk_phenotype$Metabolite_phenotype
data=phenotype[,2:8]
proportion_matrix <-data%>%
pivot_longer(cols = everything()) %>%
group_by(name, value) %>%
summarize(percentage = n() / nrow(data)) %>%
pivot_wider(names_from = value, values_from = percentage, values_fill = 0)
meta_name=proportion_matrix$name
proportion_matrix$name=NULL
proportion_matrix=t(proportion_matrix)
colnames(proportion_matrix)=meta_name
barplot(proportion_matrix, legend.text=T,
names.arg=c("Amino_acid","Lipid","Carbohydrate","TCA_cycle","Energy","Nucleotide","Vitamin"),
col =c("#99CCFF","#FF6666","#CCCCCC"))
For Tumor cell GEP
TC_phenotype=MetaPhenotype(TC_GEP$tumor_cell_GEP,0.1)
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...
#> preparing geneSet collections...
#> GSEA analysis...
#> leading edge analysis...
#> done...Calculate and show proportion of metabolic phenotype for seven pathway
library(ggpubr);library(tidyverse)
phenotype=TC_phenotype$Metabolite_phenotype
data=phenotype[,2:8]
proportion_matrix <-data%>%
pivot_longer(cols = everything()) %>%
group_by(name, value) %>%
summarize(percentage = n() / nrow(data)) %>%
pivot_wider(names_from = value, values_from = percentage, values_fill = 0)
meta_name=proportion_matrix$name
proportion_matrix$name=NULL
proportion_matrix=t(proportion_matrix)
colnames(proportion_matrix)=meta_name
barplot(proportion_matrix, legend.text=T,
names.arg=c("Amino_acid","Lipid","Carbohydrate","TCA_cycle","Energy","Nucleotide","Vitamin"),
col =c("#99CCFF","#FF6666","#CCCCCC"))
If PureMeta software is used in your publication, please cite the following paper(s):
- Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer. Kang Wang et al. . (application of PureMeta to the longitudinal breast cancer cohort).