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

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

SUBMITTER: Buettner F 

PROVIDER: S-EPMC4071202 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature