Robust PCA based method for discovering differentially expressed genes

Abstract

How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.

Block-Constraint Robust Principal Component Analysis and its Application to Integrated Analysis of TCGA Data

“…In the earlier RPCA-based model using sparse perturbation matrix to identify the differentially expressed genes [19], observation data belong to only one category. Thus, for analyzing multi-assay genomic data, our idea is related to the so-called block-constraint to incorporate prior knowledge of the categories.…”

“…Given an appropriate constraint parameter, the sparse perturbation matrix S can be obtained. Following the earlier RPCA-based model [19], the sparse perturbation matrix S is used to rank features. Therefore the differentially expressed features can be determined by analyzing the sparse matrix S .…”

“…Chang et al proposed a RPCA-based method to clarify aberrant responses from multidimensional spatio-temporal data [18]. Liu et al used RPCA to discover differentially expressed genes [19]. Since TCGA dataset includes different data categories, the validity of RPCA method in an integrated analysis of these data is still needed to be studied.…”

Robust foreground detection in somatic copy number data

“…A key challenge in SCNA inference from DNA sequencing experiments is noise or bias in read depth data (8). SCNA inference relies on an affine relationship between copy number (CN) and read depth to identify segments of discrete CN states.…”

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

“…Early linear transformation methods include principal component analysis [1–3] (PCA), linear discriminant analysis [4–6] (LDA), and independent component analysis [7, 8] (ICA). The main methods of nonlinear transformation include kernel method [9], neural network [10, 11], manifold learning [12, 13], and sparse representation [14, 15].…”