- Gorodkin, Jan
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- National Science Foundation
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scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profilingABSTRACT: Motivation Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then, a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity and extra (e.g. spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Results Here, we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMFmore »
Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analysed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doorsmore »
Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction.
In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes inmore »
Availability and implementation
Source code is available at: https://github.com/CompbioLabUcf/IntMTQ.
Supplementary data are available at Bioinformatics online.
Abstract Summary With the advancements of high-throughput single-cell RNA-sequencing protocols, there has been a rapid increase in the tools available to perform an array of analyses on the gene expression data that results from such studies. For example, there exist methods for pseudo-time series analysis, differential cell usage, cell-type detection RNA-velocity in single cells, etc. Most analysis pipelines validate their results using known marker genes (which are not widely available for all types of analysis) and by using simulated data from gene-count-level simulators. Typically, the impact of using different read-alignment or unique molecular identifier (UMI) deduplication methods has not been widely explored. Assessments based on simulation tend to start at the level of assuming a simulated count matrix, ignoring the effect that different approaches for resolving UMI counts from the raw read data may produce. Here, we present minnow, a comprehensive sequence-level droplet-based single-cell RNA-sequencing (dscRNA-seq) experiment simulation framework. Minnow accounts for important sequence-level characteristics of experimental scRNA-seq datasets and models effects such as polymerase chain reaction amplification, cellular barcodes (CB) and UMI selection and sequence fragmentation and sequencing. It also closely matches the gene-level ambiguity characteristics that are observed in real scRNA-seq experiments. Using minnow, we explore the performancemore »
Abstract Motivation Advances in sequencing technology, inference algorithms and differential testing methodology have enabled transcript-level analysis of RNA-seq data. Yet, the inherent inferential uncertainty in transcript-level abundance estimation, even among the most accurate approaches, means that robust transcript-level analysis often remains a challenge. Conversely, gene-level analysis remains a common and robust approach for understanding RNA-seq data, but it coarsens the resulting analysis to the level of genes, even if the data strongly support specific transcript-level effects. Results We introduce a new data-driven approach for grouping together transcripts in an experiment based on their inferential uncertainty. Transcripts that share large numbers of ambiguously-mapping fragments with other transcripts, in complex patterns, often cannot have their abundances confidently estimated. Yet, the total transcriptional output of that group of transcripts will have greatly reduced inferential uncertainty, thus allowing more robust and confident downstream analysis. Our approach, implemented in the tool terminus, groups together transcripts in a data-driven manner allowing transcript-level analysis where it can be confidently supported, and deriving transcriptional groups where the inferential uncertainty is too high to support a transcript-level result. Availability and implementation Terminus is implemented in Rust, and is freely available and open source. It can be obtained from https://github.com/COMBINE-lab/Terminus.more »