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Deconvolution analysis
Published: Mar 28, 2022 Views: 619
Original research article
The authors used this protocol in:
Jul 27, 2021
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#-----------------------------------------------------------------------------------------------------------------------------------------------
### download GSE file
#-----------------------------------------------------------------------------------------------------------------------------------------------
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GEOquery")
library(GEOquery)
mygeomat <- getGEO("GSE33630", GSEMatrix=TRUE)
dataMatrix<-mygeomat$GSE33630_series_matrix.txt.gz@assayData$exprs
rownames(dataMatrix)<-mygeomat$GSE33630_series_matrix.txt.gz@featureData@data$`Gene Symbol`
dataMatrix[1:5,1:15]
#-----------------------------------------------------------------------------------------------------------------------------------------------
### deconvolution using CELLCODE
#-----------------------------------------------------------------------------------------------------------------------------------------------
library(CellCODE)
# Marker file is a marker matrix file that can be a reference for bulk data deconvolution, composed of about 15 markers for each cell type.
# markers are composed of well-known markers and differentially expressed genes in each cell type.
# represent the cell type markers to "1", but others "0".
# example
| Fol | PTC | PTC2 | |
| TFF3 | 1 | 0 | 0 |
| TPO | 1 | 0 | 0 |
| MT1G | 1 | 0 | 0 |
| CRABP1 | 1 | 0 | 0 |
| SNHG9 | 1 | 0 | 0 |
| FN1 | 0 | 1 | 0 |
| NPC2 | 0 | 1 | 0 |
| PERP | 0 | 1 | 0 |
| KRT7 | 0 | 1 | 0 |
| HOPX | 0 | 1 | 0 |
| MDK | 0 | 0 | 1 |
| S100A13 | 0 | 0 | 1 |
| NPW | 0 | 0 | 1 |
| TESC | 0 | 0 | 1 |
| MS4A4A | 0 | 0 | 1 |
marker = read.table( "thyroid_cellcode.txt", sep = "\t", header = T)
rownames(marker)=make.names(marker[,1], unique=T)
marker=marker[,-1]
dim(marker)
head(marker)
marker[1:4,1:5]
grp <- c("ATC", "ATC", "ATC", "ATC", "ATC", "ATC", "ATC", "ATC", "ATC", "ATC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "PTC", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "NOM", "ATC")
dataMatrix=as.matrix(dataMatrix)
marker=as.matrix(marker)
grp=factor(grp)
colnames(dataMatrix)=grp
SPVs=getAllSPVs(dataMatrix , grp=grp, marker , method="mixed", plot=T, mix.par=0.3)
write.table(SPVs, file="GSE33630.txt", sep="\t", quote=F)
# plot using the ggplot2 or other plotting program
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