TY  - GEN
A1  - Perscheid, Cindy
A1  - Faber, Lukas
A1  - Kraus, Milena
A1  - Arndt, Paul
A1  - Janke, Michael
A1  - Rehfeldt, Sebastian
A1  - Schubotz, Antje
A1  - Slosarek, Tamara
A1  - Uflacker, Matthias
T1  - A tissue-aware gene selection approach for analyzing multi-tissue gene expression data
T2  - 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
N2  - High-throughput RNA sequencing (RNAseq) produces large data sets containing expression levels of thousands of genes. The analysis of RNAseq data leads to a better understanding of gene functions and interactions, which eventually helps to study diseases like cancer and develop effective treatments. Large-scale RNAseq expression studies on cancer comprise samples from multiple cancer types and aim to identify their distinct molecular characteristics. Analyzing samples from different cancer types implies analyzing samples from different tissue origin. Such multi-tissue RNAseq data sets require a meaningful analysis that accounts for the inherent tissue-related bias: The identified characteristics must not originate from the differences in tissue types, but from the actual differences in cancer types. However, current analysis procedures do not incorporate that aspect. As a result, we propose to integrate a tissue-awareness into the analysis of multi-tissue RNAseq data. We introduce an extension for gene selection that provides a tissue-wise context for every gene and can be flexibly combined with any existing gene selection approach. We suggest to expand conventional evaluation by additional metrics that are sensitive to the tissue-related bias. Evaluations show that especially low complexity gene selection approaches profit from introducing tissue-awareness.
KW  - RNAseq
KW  - gene selection
KW  - tissue-awareness
KW  - TCGA
KW  - GTEx
Y1  - 2018
SN  - 978-1-5386-5488-0
U6  - https://doi.org/10.1109/BIBM.2018.8621189
SN  - 2156-1125
SN  - 2156-1133
SP  - 2159
EP  - 2166
PB  - IEEE
CY  - New York
ER  -