IKCscore is an R package to predict the treatment response of immunotherapy in patients with NSCLC.
1.IKCscore was designed to predict the response to immunotherapy in patients with NSCLC. 2.This package consists a IKC model based on expression profiles of 41 gene, which were selected through gene expression pattern. 3.This package provides functions for different scenarios and allows visual assessment to determine whether patients are suitable for immunotherapy.
It is essential that you have R 3.6.3 or above already installed on your computer or server. Before installing IKCscore, please install all dependencies by executing the following command in R console:
The dependencies includesIOBR,crayon,ggplot2.
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
depens<-c( "crayon", "ggplot2")
for(i in 1:length(depens)){
depen<-depens[i]
if (!requireNamespace(depen, quietly = TRUE)) BiocManager::install(depen,update = FALSE)
}
if (!requireNamespace("IOBR", quietly = TRUE)) devtools::install_github("IOBR/IOBR")Then, you can start to install IOBR from github by typing the following code into your R session:
if (!requireNamespace("IKCscore", quietly = TRUE))
remotes::install_github("LiaoWJLab/IKCscore")Load the IOBR package in your R session after the installation is complete:
library(IKCscore)
library(IOBR)
library(tidyverse)For transcriptome data of TCGA data sets, we strongly recommend user to use UCSCXenaTools R package. Here, we download counts data of TCGA-LUAD from UCSC using UCSCXenaTools R package
if (!requireNamespace("UCSCXenaTools", quietly = TRUE))
BiocManager::install("UCSCXenaTools")
library(UCSCXenaTools)
eset<-XenaGenerate(subset = XenaCohorts =="GDC TCGA Lung Adenocarcinoma (LUAD)") %>%
XenaFilter(filterDatasets = "TCGA-LUAD.htseq_counts.tsv") %>%
XenaQuery() %>%
XenaDownload() %>%
XenaPrepare()eset<- readr::read_tsv("TCGA-LUAD.htseq_counts.tsv.gz")
# Remove the version numbers in Ensembl ID.
eset$Ensembl_ID<-substring(eset$Ensembl_ID, 1, 15)
eset<-column_to_rownames(eset, var = "Ensembl_ID")
# Revert back to original format because the data from UCSC was log2(x+1)transformed.
eset<-(2^eset)-1
head(eset[1:5,1:5])
#> TCGA-97-7938-01A TCGA-55-7574-01A TCGA-05-4250-01A
#> ENSG00000000003 2032 1000 5355
#> ENSG00000000005 15 0 5
#> ENSG00000000419 1220 744 2898
#> ENSG00000000457 876 560 734
#> ENSG00000000460 250 325 785
#> TCGA-55-6979-11A TCGA-95-A4VK-01A
#> ENSG00000000003 516 2269
#> ENSG00000000005 0 1
#> ENSG00000000419 589 819
#> ENSG00000000457 361 1477
#> ENSG00000000460 88 327
#remove normal sample of TCGA-LUAD
eset<- eset[,!substring(colnames(eset), 14,16)=="11A"]
colnames(eset)<- substring(colnames(eset), 1,12)
summary(duplicated(colnames(eset)))
#> Mode FALSE TRUE
#> logical 513 14
eset<- eset[,!duplicated(colnames(eset))]Transform count data to TPM using IOBR package.
eset_tpm <- count2tpm(countMat = eset, idType = "Ensembl")
#> >>>--- Using variables (anno_grch38) and gene lengths (eff_length) built into the IOBR package to perform TPM transformation
#> >>>--- The gene lengths (eff_length) was estimated by function `getGeneLengthAndGCContent` from EDASeq package with default parameters at 2023-02-10
#> Warning in count2tpm(countMat = eset, idType = "Ensembl"): >>>--- Omit 3985
#> genes of which length is not available !
#using phenotype data derived from TCGA paper [https://pubmed.ncbi.nlm.nih.gov/34019806/]
data(pdata_luad)
summary(pdata_luad$ID%in%colnames(eset))
#> Mode FALSE TRUE
#> logical 3 495Annotating genes in expression matrix and remove duplicate genes using IOBR package.
eset <- anno_eset(eset = eset, annotation = anno_grch38, probe = "id", symbol = "symbol")
#> Row number of original eset:
#> >>>> 60488
#> >>> 99.73% of probe in expression set was annotated
#> Row number after filtering duplicated gene symbol:
#> >>>> 56011res <- ikc_score(eset = eset_luad, pdata = pdata_luad, scale = TRUE)
#> >>>-- Scaling data...
#> >>>-- Predicting new data with IKC model...
#> Estimating ssGSEA scores for 3 gene sets.
#> [1] "Calculating ranks..."
#> [1] "Calculating absolute values from ranks..."
#> | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | |====================== | 32% | |======================= | 32% | |======================= | 33% | |======================= | 34% | |======================== | 34% | |======================== | 35% | |========================= | 35% | |========================= | 36% | |========================== | 36% | |========================== | 37% | |========================== | 38% | |=========================== | 38% | |=========================== | 39% | |============================ | 39% | |============================ | 40% | |============================ | 41% | |============================= | 41% | |============================= | 42% | |============================== | 42% | |============================== | 43% | |=============================== | 44% | |=============================== | 45% | |================================ | 45% | |================================ | 46% | |================================= | 47% | |================================= | 48% | |================================== | 48% | |================================== | 49% | |=================================== | 49% | |=================================== | 50% | |=================================== | 51% | |==================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 53% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 57% | |======================================== | 58% | |========================================= | 58% | |========================================= | 59% | |========================================== | 59% | |========================================== | 60% | |========================================== | 61% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 62% | |============================================ | 63% | |============================================ | 64% | |============================================= | 64% | |============================================= | 65% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================ | 68% | |================================================ | 69% | |================================================= | 69% | |================================================= | 70% | |================================================= | 71% | |================================================== | 71% | |================================================== | 72% | |=================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |==================================================== | 75% | |===================================================== | 75% | |===================================================== | 76% | |====================================================== | 77% | |====================================================== | 78% | |======================================================= | 78% | |======================================================= | 79% | |======================================================== | 79% | |======================================================== | 80% | |======================================================== | 81% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 82% | |========================================================== | 83% | |=========================================================== | 84% | |=========================================================== | 85% | |============================================================ | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================= | 88% | |============================================================== | 88% | |============================================================== | 89% | |=============================================================== | 89% | |=============================================================== | 90% | |=============================================================== | 91% | |================================================================ | 91% | |================================================================ | 92% | |================================================================= | 92% | |================================================================= | 93% | |================================================================= | 94% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%
#>
#> [1] "Normalizing..."
#> # A tibble: 6 × 5
#> ID immune KRT immune_checkpoint IKCscore
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 TCGA-05-4244 0.169 -0.0771 0.0999 0.346
#> 2 TCGA-05-4249 0.338 -0.233 0.0149 0.585
#> 3 TCGA-05-4250 0.243 -0.345 0.312 0.901
#> 4 TCGA-05-4382 0.219 -0.219 0.195 0.633
#> 5 TCGA-05-4384 0.255 0.0300 -0.279 -0.0535
#> 6 TCGA-05-4389 0.119 -0.166 0.274 0.558
#> >>>-- DONE!p1<- ggplot(res, aes(x= IKCscore)) +
geom_histogram(bins = 30, colour = "grey", alpha = 0.5)+
design_mytheme(axis_angle = 0)+
scale_fill_manual(values = palettes(palette = "jco", show_col = FALSE))
#> >>>>Options for `theme`: light, bw, classic and classic2library(survminer)
res<- best_cutoff(pdata = res, time = "os_time", status = "os_status", variable = "IKCscore", PrintResult = FALSE)
# help("surv_group")
p2<- surv_group(
input_pdata = res,
target_group = "IKCscore_binary",
levels = c("High", "Low"),
reference_group = "High",
project = "TCGA-LUAD",
time = "os_time",
status = "os_status",
time_type = "day",
break_month = 20,
cols = NULL,
palette = "jama",
mini_sig = "",
save_path = paste0("man/figures"))
#> High Low
#> 231 247
#> [1] High Low
#> Levels: High Low
p1+p2
1.Wu JN, …, Liao WJ, Zeng DQ; Tumor microenvironment assessment-based signatures for predicting response to immunotherapy in non-small cell lung cancer. (2024) Under Review.
Please report bugs to the Github issues page
E-mail any questions to dongqiangzeng0808@gmail.com