Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.
High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1578-y) contains supplementary material, which is available to authorized users.
Effective monitoring of glucose levels is necessary for patients to achieve greater control over their diabetes. However, only about a quarter of subjects with diabetes who requires close serum glucose monitoring, regularly check their serum glucose daily. One of the potential barriers to patient compliance is the blood sampling requirement. Saliva and its protein contents can be altered in subjects with diabetes, possibly due to changes in glycemic control. We propose here that salivary proteomes of subjects with diabetes may be different based on their glycemic control as reflected in A1C levels. A total of 153 subjects with type 1 or 2 diabetes were recruited. Subjects in each type of diabetes were divided into 5 groups based on their A1C levels; <7, 7–8, 8–9, 9–10, >10. To examine the global proteomic changes associated with A1C, the proteomic profiling of pooled saliva samples from each group was created using label-free quantitative proteomics. Similar proteomic analysis for individual subjects (N=4, for each group) were then applied to examine proteins that may be less abundant in pooled samples. Principle component analysis (PCA) and cluster analysis (p<0.01 and p<0.001) were used to define the proteomic differences. We, therefore, defined the salivary proteomic changes associated with A1C changes. This study demonstrates that differences exist between salivary proteomic profiles in subjects with diabetes based on the A1C levels.
Summary Single-cell assay of transposase-accessible chromatin followed by sequencing (scATAC-seq) is an emerging new technology for the study of gene regulation with single-cell resolution. The data from scATAC-seq are unique—sparse, binary and highly variable even within the same cell type. As such, neither methods developed for bulk ATAC-seq nor single-cell RNA-seq data are appropriate. Here, we present Destin, a bioinformatic and statistical framework for comprehensive scATAC-seq data analysis. Destin performs cell-type clustering via weighted principle component analysis, weighting accessible chromatin regions by existing genomic annotations and publicly available regulomic datasets. The weights and additional tuning parameters are determined via model-based likelihood. We evaluated the performance of Destin using downsampled bulk ATAC-seq data of purified samples and scATAC-seq data from seven diverse experiments. Compared to existing methods, Destin was shown to outperform across all datasets and platforms. For demonstration, we further applied Destin to 2088 adult mouse forebrain cells and identified cell-type-specific association of previously reported schizophrenia GWAS loci. Availability and implementation Destin toolkit is freely available as an R package at https://github.com/urrutiag/destin. Supplementary information Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
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