Variance component testing for identifying differentially expressed genes in RNA-seq data

lncRNA Profiling of Exosomes and Its Communication Role in Regulating Silica-Stimulated Macrophage Apoptosis and Fibroblast Activation

Long-term silica particle exposure leads to interstitial pulmonary inflammation and fibrosis, called silicosis. Silica-activated macrophages secrete a wide range of cytokines resulting in persistent inflammation. In addition, silica-stimulated activation of fibroblast is another checkpoint in the progression of silicosis. The pathogenesis after silica exposure is complex, involving intercellular communication and intracellular signaling pathway transduction, which was ignored previously. Exosomes are noteworthy because of their crucial role in intercellular communication by delivering bioactive substances, such as lncRNA. However, the expression profile of exosomal lncRNA in silicosis has not been reported yet. In this study, exosomes were isolated from the peripheral serum of silicosis patients or healthy donors. The exosomal lncRNAs were profiled using high-throughput sequencing technology. Target genes were predicted, and functional annotation was performed using differentially expressed lncRNAs. Eight aberrant expressed exosomal lncRNAs were considered to play a key role in the process of silicosis according to the OPLS-DA. Furthermore, the increased expression of lncRNA MSTRG.43085.16 was testified in vitro. Its target gene PARP1 was critical in regulating apoptosis based on bioinformatics analysis. In addition, the effects of exosomes on macrophage apoptosis and fibroblast activation were checked based on a co-cultured system. Our findings suggested that upregulation of lncRNA MSTRG.43085.16 could regulate silica-induced macrophage apoptosis through elevating PARP1 expression, and promote fibroblast activation, implying that the exosomal lncRNA MSTRG.43085.16 might have potential as a biomarker for the early diagnosis of silicosis.

Identifying Differentially Expressed Genes of Zero Inflated Single Cell RNA Sequencing Data Using Mixed Model Score Tests

Single cell RNA sequencing (scRNA-seq) allows quantitative measurement and comparison of gene expression at the resolution of single cells. Ignoring the batch effects and zero inflation of scRNA-seq data, many proposed differentially expressed (DE) methods might generate bias. We propose a method, single cell mixed model score tests (scMMSTs), to efficiently identify DE genes of scRNA-seq data with batch effects using the generalized linear mixed model (GLMM). scMMSTs treat the batch effect as a random effect. For zero inflation, scMMSTs use a weighting strategy to calculate observational weights for counts independently under zero-inflated and zero-truncated distributions. Counts data with calculated weights were subsequently analyzed using weighted GLMMs. The theoretical null distributions of the score statistics were constructed by mixed Chi-square distributions. Intensive simulations and two real datasets were used to compare edgeR-zinbwave, DESeq2-zinbwave, and scMMSTs. Our study demonstrates that scMMSTs, as supplement to standard methods, are advantageous to define DE genes of zero-inflated scRNA-seq data with batch effects.

Identifying and exploiting gene-pathway interactions from RNA-seq data for binary phenotype

Background

RNA sequencing (RNA-seq) technology has identified multiple differentially expressed (DE) genes associated to complex disease, however, these genes only explain a modest part of variance. Omnigenic model assumes that disease may be driven by genes with indirect relevance to disease and be propagated by functional pathways. Here, we focus on identifying the interactions between the external genes and functional pathways, referring to gene-pathway interactions (GPIs). Specifically, relying on the relationship between the garrote kernel machine (GKM) and variance component test and permutations for the empirical distributions of score statistics, we propose an efficient analysis procedure as Permutation based gEne-pAthway interaction identification in binary phenotype (PEA).

Results

Various simulations show that PEA has well-calibrated type I error rates and higher power than the traditional likelihood ratio test (LRT). In addition, we perform the gene set enrichment algorithms and PEA to identifying the GPIs from a pan-cancer data (GES68086). These GPIs and genes possibly further illustrate the potential etiology of cancers, most of which are identified and some external genes and significant pathways are consistent with previous studies.

Conclusions

PEA is an efficient tool for identifying the GPIs from RNA-seq data. It can be further extended to identify the interactions between one variable and one functional set of other omics data for binary phenotypes.

Electronic supplementary material

The online version of this article (10.1186/s12863-019-0739-7) contains supplementary material, which is available to authorized users.