Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Wang, Z.; Morris, J. S.; Cao, S.; Ahn, J.; Liu, R.; Tyekucheva, S.; Li, B.; Lu, W.; Tang, X.; Wistuba, I. I.; Bowden, M.; Mucci, L.; Loda, M.; Parmigiani, G.; Holmes, C. C.; Wang, W.

doi:10.1101/146795

bioRxiv · 10.1101/146795

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Wang, Z.·Morris, J. S.·Cao, S.·Ahn, J.·Liu, R.·Tyekucheva, S.·Li, B.·Lu, W.·Tang, X.·Wistuba, I. I.·Bowden, M.·Mucci, L.·Loda, M.·Parmigiani, G.·Holmes, C. C.·Wang, W.

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Abstract

Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step towards linking tumor transcriptomic data with clinical outcomes. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.

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Wang, Z., Morris, J. S., Cao, S., Ahn, J., Liu, R., Tyekucheva, S., Li, B., Lu, W., Tang, X., Wistuba, I. I., Bowden, M., Mucci, L., Loda, M., Parmigiani, G., Holmes, C. C., Wang, W.. 2017-06-08. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. https://doi.org/10.1101/146795

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