Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases.

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Vallania F,Tam A,Lofgren S,Schaffert S,Azad TD,Bongen E,Haynes W,Alsup M,Alonso M,Davis M,Engleman E,Khatri P

doi

10.1038/s41467-018-07242-6

subject

Has Abstract

pub_date

2018-11-09 00:00:00

pages

4735

issue

1

issn

2041-1723

pii

10.1038/s41467-018-07242-6

journal_volume

9

pub_type

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