Determining the Malignant Progression Degree of Melanoma from Deconvolution Results
Utilizing the melanoma malignant progression map we've built at single-cell resolution, 'MelMal.Detector' will deduce the proportions of melanoma cell infiltration linked to various levels of malignancy within bulk samples. Employing a scoring model, 'MelMal.Detector' could compute the melanoma progression status for each sample, which will serve as a basis for subsequent functional and clinical-related analyses.
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R packages:
cluster, factoextra, ggplot2, ggpubr, ggthemes, NbClust, RColorBrewer, reshape2, Seurat, survival, survminer
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If all dependent packages and commands have been installed, please use the following codes to install/update the package in R terminal.
library("devtools");
install_github("Bonnie19920126/MelMal.Detector")
- load our scRNA_seq datas:
library(MelMal.Detector)
load(system.file("extdata/sc.dat.filtered.pc.rda", package = "MelMal.Detector"))
dim(sc.dat.filtered.pc)
#[1] 29470 16252
- load our test bulk_seq data or input your bulk_seq count data to calculate cell ratio
data(bk.dat,package="MelMal.Detector")
bk.dat[1:4,1:4]
# TSPAN6 TNMD DPM1 SCYL3
# TCGA-EB-A6QY 3148 1 3466 477
# TCGA-D9-A4Z5 4371 5 882 511
# TCGA-W3-A828 2178 37849 1240 552
# TCGA-LH-A9QB 803 7 2028 841
sc.dat.filtered.pc[1:4,1:4]
# A1BG A1CF A2M A2ML1
# D1_140460_SampleTag12_hs_1 0 0 0 0
# D1_789896_SampleTag12_hs_1 0 0 0 0
# D1_747152_SampleTag12_hs_1 0 0 0 0
# D1_13402_SampleTag12_hs_1 0 0 0 0
res0 <- Melanocyte_proportion(bk.dat=bk.dat, out_dir='./', key="Melanocyte_cluster4")
- load our test clinical data or input your clinical data
data(TCGA_SKCM_surv,package="MelMal.Detector")
head(TCGA_SKCM_surv)
# OS.time OS SampleType
# TCGA-EB-A6QY 382 0 Primary
# TCGA-D9-A4Z5 218 0 Primary
# TCGA-W3-A828 3683 1 Metastatic
# TCGA-LH-A9QB 11217 0 Metastatic
# TCGA-EB-A5VU 321 1 Primary
# TCGA-GN-A268 1910 1 Metastatic
Clinical<-TCGA_SKCM_surv[,'SampleType',drop=FALSE]
theta<-read.csv("./theta.csv", row.names=1)
res1 <- Melanocyte_proportion_clinical(out_dir='./', theta=theta, Clinical=Clinical, plot_var=c("progression_index"), tag=list(c("Primary","Metastatic")), cols=c("Metastatic"="#7876b1", "Primary"="#6f99ad"))
- load our test survival data or input your survival data
theta<-read.csv("./theta.csv", row.names=1)
res2 <- Melanocyte_proportion_survival(out_dir='./', theta=theta, Survival=TCGA_SKCM_surv[,c('OS.time','OS')])
Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology
Tinyi Chu, Zhong Wang, Dana Pe’er & Charles G. Danko