AdaTiSS is a R package to calculate tissue specificity scores, developed in the paper AdaTiSS: A Novel Data-Adaptive Robust Method for Quantifying Tissue Specificity Scores, published in Bioinformatics, 2021.
Please contact Meng Wang by email mengw1@stanford.edu for questions.
- R (version >= 3.3.0)
To illustrate the usage of our package, we took a small set of gene expression in raw TPM from GTEx project in version 7 as an example.
The data folder includes raw TPM expression for 2000 genes from 181 samples across 32 tissues and a pheonotype info of the tissue type for each sample.
To call the AdaTiSS source function
source('./R/AdaTiSS_fn.R')
To load expression data
dat.rna = readRDS(file='./data/raw_tpm_expression.rds')
To preprocess raw data and obtain filtered gene expression in log scale (for more options, see the file 'AdaTiSS_fn.R')
X = preproc.filter.fn (dat.rna, dat.type = "TPM or RPKM", proc.zero = "perturbed by a small value", filter.col.prp = 1, exp.thres=1)
To load phenotype data
p.dat = read.csv('./data/sample_phenotype.csv')
To obtain gene expression in tissue level
tiss.abd = tiss.abd.fn(X, p.dat)
To call AdaTiSS (for more options, see the file 'AdaTiSS_fn.R')
result = AdaTiSS(X, tiss.abd=tiss.abd, adjust=TRUE, adjust.opt=0)
Output:
head(result$ada.s) --- sample normalized scores
head(result$ada.z) --- tissue specificity scores
head(result$pop.fit.mx) --- population fitting info
For more statistical analysis, to check our AdaReg package.
For a related robust normalization work, to check our RobNorm package.