Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
Zeya Wang, Shaolong Cao, Jeffrey S. Morris, Jaeil Ahn, Rongjie Liu, Svitlana Tyekucheva, Fan Gao, Bo Li, Wei Lü, Ximing Tang, Ignacio Ivan Wistuba, Michaela Bowden, Lorelei Ann Mucci, Massimo F. Loda, Giovanni Parmigiani, , Wenyi Wang
DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.
iScience · Nov 1, 2018Peer-reviewedBioinformatics