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Deconvolution analysis
Last updated date: Mar 28, 2022 Views: 619 Forks: 0
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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
- Wei, T, Park, J, Xu, H, Luo, H and Kim, G(2022). Deconvolution analysis. Bio-protocol Preprint. bio-protocol.org/prep1602.
- Luo, H., Xia, X., Kim, G. D., Liu, Y., Xue, Z., Zhang, L., Shu, Y., Yang, T., Chen, Y., Zhang, S., Chen, H., Zhang, W., Li, R., Tang, H., Dong, B., Fu, X., Cheng, W., Zhang, W., Yang, L., Peng, Y., Dai, L., Hu, H., Jiang, Y., Gong, C., Hu, Y., Zhu, J., Li, Z., Caulin, C., Wei, T., Park, J. and Xu, H.(2021). Characterizing dedifferentiation of thyroid cancer by integrated analysis . Science Advances 7(31). DOI: 10.1126/sciadv.abf3657
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