Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold.

BACKGROUND: The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis. RESULTS: We evaluated the effect of sample sizes, cell type-specific proportion variability, and mean squared error on sensitivity of cell type-specific differential expression detection using linear regression. Each parameter affected variability of cell type-specific expression estimates and, subsequently, the sensitivity of differential expression detection. We provide the R package, LRCDE, which performs linear regression-based cell type-specific differential expression (deconvolution) detection on a gene-by-gene basis. Accounting for variability around cell type-specific gene expression estimates, it computes per-gene t-statistics of differential detection, p-values, t-statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic metrics. CONCLUSIONS: The sensitivity of linear regression-based cell type-specific differential expression detection differed for each gene as a function of mean squared error, per group sample sizes, and variability of the proportions of target cell (cell type being analyzed). We demonstrate that LRCDE, which uses Welch's t-test to compare per-gene cell type-specific gene expression estimates, is more sensitive in detecting cell type-specific differential expression at α < 0.05 missed by the global false discovery rate threshold FDR < 0.3.

Detrimental effects of duplicate reads and low complexity regions on RNA- and ChIP-seq data.

BACKGROUND: Adapter trimming and removal of duplicate reads are common practices in next-generation sequencing pipelines. Sequencing reads ambiguously mapped to repetitive and low complexity regions can also be problematic for accurate assessment of the biological signal, yet their impact on sequencing data has not received much attention. We investigate how trimming the adapters, removing duplicates, and filtering out reads overlapping low complexity regions influence the significance of biological signal in RNA- and ChIP-seq experiments. METHODS: We assessed the effect of data processing steps on the alignment statistics and the functional enrichment analysis results of RNA- and ChIP-seq data. We compared differentially processed RNA-seq data with matching microarray data on the same patient samples to determine whether changes in pre-processing improved correlation between the two. We have developed a simple tool to remove low complexity regions, RepeatSoaker, available at https://github.com/mdozmorov/RepeatSoaker, and tested its effect on the alignment statistics and the results of the enrichment analyses. RESULTS: Both adapter trimming and duplicate removal moderately improved the strength of biological signals in RNA-seq and ChIP-seq data. Aggressive filtering of reads overlapping with low complexity regions, as defined by RepeatMasker, further improved the strength of biological signals, and the correlation between RNA-seq and microarray gene expression data. CONCLUSIONS: Adapter trimming and duplicates removal, coupled with filtering out reads overlapping low complexity regions, is shown to increase the quality and reliability of detecting biological signals in RNA-seq and ChIP-seq data.

B-Cell and Monocyte Contribution to Systemic Lupus Erythematosus Identified by Cell-Type-Specific Differential Expression Analysis in RNA-Seq Data.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by complex interplay among immune cell types. SLE activity is experimentally assessed by several blood tests, including gene expression profiling of heterogeneous populations of cells in peripheral blood. To better understand the contribution of different cell types in SLE pathogenesis, we applied the two methods in cell-type-specific differential expression analysis, csSAM and DSection, to identify cell-type-specific gene expression differences in heterogeneous gene expression measures obtained using RNA-seq technology. We identified B-cell-, monocyte-, and neutrophil-specific gene expression differences. Immunoglobulin-coding gene expression was altered in B-cells, while a ribosomal signature was prominent in monocytes. On the contrary, genes differentially expressed in the heterogeneous mixture of cells did not show any functional enrichment. Our results identify antigen binding and structural constituents of ribosomes as functions altered by B-cell- and monocyte-specific gene expression differences, respectively. Finally, these results position both csSAM and DSection methods as viable techniques for cell-type-specific differential expression analysis, which may help uncover pathogenic, cell-type-specific processes in SLE.