Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Vallania, Francesco; Tam, Andrew; Lofgren, Shane; Schaffert, Steven; Azad, Tej; Bongen, Erika; Haynes, Winston; Alsup, Meia; Alonso, Michael; Davis, Mark; Engleman, Edgar; Khatri, Purvesh
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 8
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-07242-6
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of deconvolution method. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We find that immunoStates significantly reduces biological and technical biases. Importantly, we find that different methods have virtually no or minimal effect once the basis matrix is chosen. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy. Cell type deconvolution from bulk expression data rely on a reference expression matrix. Here, the authors introduce a basis matrix built using data from both healthy and diseased samples profiled on 42 platforms, reducing biases introduced by single-platform matrices built using healthy samples.