Poisson factor models with applications to non-normalized microRNA profiling

Motivation: Next-generation (NextGen) sequencing is becoming increasingly popular as an alternative for transcriptional profiling, as is the case for micro RNAs (miRNA) profiling and classification. miRNAs are a new class of molecules that are regulated in response to differentiation, tumorigenesis or infection. Our primary motivating application is to identify different viral infections based on the induced change in the host miRNA profile. Statistical challenges are encountered because of special features of NextGen sequencing data: the data are read counts that are extremely skewed and non-negative; the total number of reads varies dramatically across samples that require appropriate normalization. Statistical tools developed for microarray expression data, such as principal component analysis, are sub-optimal for analyzing NextGen sequencing data. Results: We propose a family of Poisson factor models that explicitly takes into account the count nature of sequencing data and automatically incorporates sample normalization through the use of offsets. We develop an efficient algorithm for estimating the Poisson factor model, entitled Poisson Singular Value Decomposition with Offset (PSVDOS). The method is shown to outperform several other normalization and dimension reduction methods in a simulation study. Through analysis of an miRNA profiling experiment, we further illustrate that our model achieves insightful dimension reduction of the miRNA profiles of 18 samples: the extracted factors lead to more accurate and meaningful clustering of the cell lines.