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

Francesco Vallania; Andrew Tam; Shane Lofgren; Steven Schaffert; Tej D Azad; Erika Bongen; Winston Haynes; Meia Alsup; Michael Alonso; Mark Davis; Edgar Engleman; Purvesh Khatri

doi:10.1038/s41467-018-07242-6

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

Nat Commun. 2018 Nov 9;9(1):4735. doi: 10.1038/s41467-018-07242-6.

Authors

Francesco Vallania^{1

2}, Andrew Tam^{1

3}, Shane Lofgren^{1

2}, Steven Schaffert^{1

2}, Tej D Azad¹, Erika Bongen¹, Winston Haynes², Meia Alsup^{1

3}, Michael Alonso⁴, Mark Davis¹, Edgar Engleman⁴, Purvesh Khatri^{5

6}

Affiliations

¹ Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, 94305, CA, USA.
² Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, 94305, CA, USA.
³ Stanford Institutes of Medicine Summer Research Program, Stanford University, Stanford, 94305, CA, USA.
⁴ Department of Pathology, Stanford University, Stanford, 94305, CA, USA.
⁵ Institute for Immunity, Transplantation and Infection, Stanford University, Stanford, 94305, CA, USA. pkhatri@stanford.edu.
⁶ Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, 94305, CA, USA. pkhatri@stanford.edu.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Databases as Topic*
Disease
Humans
Leukocytes, Mononuclear / metabolism*
Microarray Analysis
ROC Curve

Abstract

Publication types

MeSH terms

Grants and funding