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1. Compression of quantification uncertainty for scRNA-seq counts

   https://doi.org/10.1093/bioinformatics/btab001  

   Van Buren, Scott; Sarkar, Hirak; Srivastava, Avi; Rashid, Naim U; Patro, Rob; Love, Michael I (January 2021, Bioinformatics)

   Birol, Inanc (Ed.)

   Abstract Motivation Quantification estimates of gene expression from single-cell RNA-seq (scRNA-seq) data have inherent uncertainty due to reads that map to multiple genes. Many existing scRNA-seq quantification pipelines ignore multi-mapping reads and therefore underestimate expected read counts for many genes. alevin accounts for multi-mapping reads and allows for the generation of ‘inferential replicates’, which reflect quantification uncertainty. Previous methods have shown improved performance when incorporating these replicates into statistical analyses, but storage and use of these replicates increases computation time and memory requirements. Results We demonstrate that storing only the mean and variance from a set of inferential replicates (‘compression’) is sufficient to capture gene-level quantification uncertainty, while reducing disk storage to as low as 9% of original storage, and memory usage when loading data to as low as 6%. Using these values, we generate ‘pseudo-inferential’ replicates from a negative binomial distribution and propose a general procedure for incorporating these replicates into a proposed statistical testing framework. When applying this procedure to trajectory-based differential expression analyses, we show false positives are reduced by more than a third for genes with high levels of quantification uncertainty. We additionally extend the Swish method to incorporate pseudo-inferential replicates and demonstrate improvements in computation time and memory usage without any loss in performance. Lastly, we show that discarding multi-mapping reads can result in significant underestimation of counts for functionally important genes in a real dataset. Availability and implementation makeInfReps and splitSwish are implemented in the R/Bioconductor fishpond package available at https://bioconductor.org/packages/fishpond. Analyses and simulated datasets can be found in the paper’s GitHub repo at https://github.com/skvanburen/scUncertaintyPaperCode. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. Projecting clumped transcriptomes onto single cell atlases to achieve single cell resolution

   https://doi.org/10.1101/2022.04.26.489628  

   Johansen, N.; Quon, G. (April 2022, bioRxiv)

   Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including the identification of spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells. 

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3. Ornaments for efficient allele-specific expression estimation with bias correction

   https://doi.org/10.1016/j.ajhg.2024.06.014  

   Adduri, Abhinav; Kim, Seyoung (August 2024, The American Journal of Human Genetics)

   Allele-specific expression has been used to elucidate various biological mechanisms, such as genomic imprinting and gene expression variation caused by genetic changes in cis-regulatory elements. However, existing methods for obtaining allele-specific expression from RNA-seq reads do not adequately and efficiently remove various biases, such as reference bias, where reads containing the alternative allele do not map to the reference transcriptome, or ambiguous mapping bias, where reads containing the reference allele map differently from reads containing the alternative allele. We present Ornaments, a computational tool for rapid and accurate estimation of allele-specific expression at unphased heterozygous loci from RNA-seq reads while correcting for allele-specific read mapping bias. Ornaments removes reference bias by mapping reads to a personalized transcriptome, and ambiguous mapping bias by probabilistically assigning reads to multiple transcripts and variant loci they map to. Ornaments is a lightweight extension of kallisto, a popular tool for fast RNA-seq quantification, that improves the efficiency and accuracy of WASP, a popular tool for bias correction in allele-specific read mapping. Our experiments on simulated and human lymphoblastoid cell-line RNA-seq reads with the genomes of the 1000 Genomes Project show that Ornaments is more accurate than WASP and kallisto and nearly as efficient as kallisto per sample, and despite the additional cost of constructing a personalized index for multiple samples, an order of magnitude faster than WASP. In addition, Ornaments detected imprinted transcripts with higher sensitivity, compared to WASP which detected the imprinted signals only at the gene level. 

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4. Optimal marker gene selection for cell type discrimination in single cell analyses

   https://doi.org/10.1038/s41467-021-21453-4  

   Dumitrascu, Bianca; Villar, Soledad; Mixon, Dustin G.; Engelhardt, Barbara E. (February 2021, Nature Communications)

   Abstract Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization. 

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5. Minnow : a principled framework for rapid simulation of dscRNA-seq data at the read level

   https://doi.org/10.1093/bioinformatics/btz351  

   Sarkar, Hirak; Srivastava, Avi; Patro, Rob (July 2019, Bioinformatics)

   Abstract SummaryWith 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 performance of some common processing pipelines to produce gene-by-cell count matrices from droplet-bases scRNA-seq data, demonstrate the effect that realistic levels of gene-level sequence ambiguity can have on accurate quantification and show a typical use-case of minnow in assessing the output generated by different quantification pipelines on the simulated experiment. Supplementary informationSupplementary data are available at Bioinformatics online. 

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