Probabilistic PCA of censored data: Accounting for uncertainties in the visualization of high-throughput single-cell qPCR data

Buettner F, Moignard V, Goettgens B, Theis FJ (2014)

Publication Type: Journal article

Publication year: 2014

Journal

Bioinformatics Oxford University Press (OUP): Policy B - Oxford Open Option B

Book Volume: 30

Pages Range: 1867-1875

Journal Issue: 13

DOI: 10.1093/bioinformatics/btu134

Abstract

Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. © The Author 2014.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) University of Cambridge GB United Kingdom (GB)

How to cite

APA:

Buettner, F., Moignard, V., Goettgens, B., & Theis, F.J. (2014). Probabilistic PCA of censored data: Accounting for uncertainties in the visualization of high-throughput single-cell qPCR data. Bioinformatics, 30(13), 1867-1875. https://doi.org/10.1093/bioinformatics/btu134

MLA:

Buettner, Florian, et al. "Probabilistic PCA of censored data: Accounting for uncertainties in the visualization of high-throughput single-cell qPCR data." Bioinformatics 30.13 (2014): 1867-1875.

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