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1. Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

   https://doi.org/10.1093/bioinformatics/btab257  

   Qiu, Yixuan ; Wang, Jiebiao ; Lei, Jing ; Roeder, Kathryn ( April 2021 , Bioinformatics)

   Mathelier, Anthony (Ed.)

   Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single-cell type, can be identified from the single-cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online. 

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2. DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species

   https://doi.org/10.10106  

   Hu Y, Tattikota SG ( January 2021 , Computational and Structural Biotechnology Journal)

   null (Ed.)

   With the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in stud-ies involving scRNA-seq of several tissues across diverse species includingDrosophila. Although a fewdatabases exist for users to query genes of interest within the scRNA-seq studies, search tools that enableusers to find orthologous genes and their cell type-specific expression patterns across species are limited.Here, we built a new search database, DRscDB (https://www.flyrnai.org/tools/single_cell/web/), toaddress this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets forDrosophilaand relevant datasets from human and other model organisms. DRscDB is based on manualcuration ofDrosophilascRNA-seq studies of various tissue types and their corresponding analogoustissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides mostof the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seqdatasets. Finally, DRscDB serves as a web-based user interface that allows users to mine gene expressiondata from scRNA-seq studies and perform cell cluster enrichment analyses pertaining to variousscRNA-seq studies, both within and across species. 

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3. Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

   https://doi.org/10.1093/bib/bbab265  

   Nadel, Brian B ; Oliva, Meritxell ; Shou, Benjamin L ; Mitchell, Keith ; Ma, Feiyang ; Montoya, Dennis J ; Mouton, Alice ; Kim-Hellmuth, Sarah ; Stranger, Barbara E ; Pellegrini, Matteo ; et al ( November 2021 , Briefings in Bioinformatics)

   Abstract Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. Although these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare 12 cell type quantification tools and evaluate their performance while using each of 10 separate reference profiles. Specifically, we have run each tool on over 4000 samples with known cell type proportions, spanning both immune and stromal cell types. A total of 12 of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single-cell data. A final 3728 clinical samples have been collected from the Framingham cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool Estimating the Proportions of Immune and Cancer cells (EPIC) produces the highest correlation, whereas Gene Expression Deconvolution Interactive Tool (GEDIT) produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. We find that optimal reference depends on the tool used, and report suggested references to be used with each tool. Most tools return results within minutes, but on large datasets runtimes for CIBERSORT can exceed hours or even days. We conclude that deconvolution methods are capable of returning high-quality results, but that proper reference selection is critical. 

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4. nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

   https://doi.org/10.1038/s41467-023-39748-z  

   Weber, Lukas M. ; Saha, Arkajyoti ; Datta, Abhirup ; Hansen, Kasper D. ; Hicks, Stephanie C. ( December 2023 , Nature Communications)

   Abstract Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG . 

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5. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

   https://doi.org/10.1038/s41467-020-15816-6  

   Jew, Brandon ; Alvarez, Marcus ; Rahmani, Elior ; Miao, Zong ; Ko, Arthur ; Garske, Kristina M. ; Sul, Jae Hoon ; Pietiläinen, Kirsi H. ; Pajukanta, Päivi ; Halperin, Eran ( April 2020 , Nature Communications)

   We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.

    

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