contamDE-lm: linear model-based differential gene expression analysis using next-generation RNA-seq data from contaminated tumor samples

Abstract

Motivation Tumor and adjacent normal RNA samples are commonly used to screen differentially expressed genes between normal and tumor samples or among tumor subtypes. Such paired-sample design could avoid numerous confounders in differential expression (DE) analysis, but the cellular contamination of tumor samples can be an important noise and confounding factor, which can both inflate false-positive rate and deflate true-positive rate. The existing DE tools that use next-generation RNA-seq data either do not account for cellular contamination or are computationally extensive with increasingly large sample size. Results A novel linear model was proposed to avoid the problem that could arise from tumor–normal correlation for paired samples. A statistically robust and computationally very fast DE analysis procedure, contamDE-lm, was developed based on the novel model to account for cellular contamination, boosting DE analysis power through the reduction in individual residual variances using gene-wise information. The desired advantages of contamDE-lm over some state-of-the-art methods (limma and DESeq2) were evaluated through the applications to simulated data, TCGA database and Gene Expression Omnibus (GEO) database. Availability and implementation The proposed method contamDE-lm was implemented in an updated R package contamDE (version 2.0), which is freely available at https://github.com/zhanghfd/contamDE. Supplementary information Supplementary data are available at Bioinformatics online.

Interrogating colorectal cancer metastasis to liver: a search for clinically viable compounds and mechanistic insights in colorectal cancer Patient Derived Organoids

“…Notably, metastatic samples localized, in the PCA graph, between primary tumor and healthy liver samples, suggesting that the metastatic lesions were endowed with a higher level of similarity to the uninvolved tissue than primary tumors. We performed the same analysis on two publicly available datasets [ 24 , 25 ] including matched primary and metastatic CRC samples as well as adjacent tissues. In both cases, PCA analysis provided similar results (Fig.…”

“…We verified whether the identified DE genes could be detected in independent datasets retrieved from public databases. In detail, the comparison between primary and metastatic samples from two analyzed independent datasets [ 24 , 25 ] resulted in 65 genes upregulated in metastasis versus primary CRCs and common to both the public datasets and to our samples (Table S 3 ). 22 out of the 65 genes were included within the more stringent core of the 43 DE genes derived by the intersection of fully matched samples (Table 1 and Table S 3 ).…”

“…The Sequence Alignment/Map format and SAMtools were used to mark duplicated sequences and Stringtie (v. 1.3.6) to estimate gene abundances. The same pipeline was used to analyze raw FASTQ data from two additional datasets obtained from ENA at EMBL-EBI ( https://www.ebi.ac.uk ) under the accession number PRJNA288518 and PRJNA603221 [ 24 , 25 ]. The first dataset include quadruple-matched tissues (primary colorectal carcinomas, liver metastasis, normal adjacent liver and colon samples) from five patients while the second dataset include matched samples from primary colon cancer, liver metastasis and normal colon tissue from three patients.…”