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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. 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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3. tauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

   https://doi.org/10.1038/s41467-024-48041-6  

   Duan, Junyan; Ngo, Michelle N; Karri, Satya Swaroop; Tsoi, Lam C; Gudjonsson, Johann E; Shahbaba, Babak; Lowengrub, John; Andersen, Bogi (December 2024, Nature Communications)

   Abstract As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher’s performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research. 

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4. Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

   https://doi.org/10.1186/s13059-024-03292-w  

   Hu, Mengying; Chikina, Maria (December 2024, Genome Biology)

   Abstract BackgroundComputational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. ResultsIn our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. ConclusionsOur heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly packagehttps://github.com/humengying0907/deconvBenchmarkingandhttps://doi.org/10.5281/zenodo.8206516, enabling further developments in deconvolution methods. 

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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)

   Abstract 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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