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 Communjournal_title
Nature communicationsauthors
Vallania F,Tam A,Lofgren S,Schaffert S,Azad TD,Bongen E,Haynes W,Alsup M,Alonso M,Davis M,Engleman E,Khatri Pdoi
10.1038/s41467-018-07242-6subject
Has Abstractpub_date
2018-11-09 00:00:00pages
4735issue
1issn
2041-1723pii
10.1038/s41467-018-07242-6journal_volume
9pub_type
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