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Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a ‘direction of change’ and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration. 

Novel discoveries of biomarkers predictive of drug-specific responses not only play a pivotal role in revealing the drug mechanisms in cancers, but are also critical to personalized medicine. In this study, we identified drug-specific biomarkers by integrating protein expression data, drug treatment data and survival outcome of 7076 patients from The Cancer Genome Atlas (TCGA). We first defined cancer-drug groups, where each cancer-drug group contains patients with the same cancer and treated with the same drug. For each protein, we stratified the patients in each cancer-drug group by high or low expression of the protein, and applied log-rank test to examine whether the stratified patients show significant survival difference. We examined 336 proteins in 98 cancer-drug groups and identified 65 protein-cancer-drug combinations involving 55 unique proteins, where the protein expression levels are predictive of drug-specific survival outcomes. Some of the identified proteins were supported by published literature. Using the gene expression data from TCGA, we found the mRNA expression of ∼11% of the drug-specific proteins also showed significant correlation with drug-specific survival, and most of these drug-specific proteins and their corresponding genes are strongly correlated. 

Long-time evolution has shaped a harmonious host-microbiota symbiosis consisting of intestinal microbiota in conjunction with the host immune system. Inflammatory bowel disease (IBD) is a result of the dysbiotic microbial composition together with aberrant mucosal immune responses, while the underlying mechanism is far from clear. In this report, we creatively proposed that when correlating with the host metabolism, functional microbial communities matter more than individual bacteria. Based on this assumption, we performed a systematic analysis to characterize the co-metabolism of host and gut microbiota established on a set of newly diagnosed Crohn’s disease (CD) samples and healthy controls. From the host side, we applied gene set enrichment analysis on host mucosal proteome data to identify those host pathways associated with CD. At the same time, we applied community detection analysis on the metagenomic data of mucosal microbiota to identify those microbial communities, which were assembled for a functional purpose. Then, the correlation analysis between host pathways and microbial communities was conducted. We discovered two microbial communities negatively correlated with IBD enriched host pathways. The dominant genera for these two microbial communities are known as health-benefits and could serve as a reference for designing complex beneficial microorganisms for IBD treatment. The correlated host pathways are all relevant to MHC antigen presentation pathways, which hints toward a possible mechanism of immune-microbiota cross talk underlying IBD.