Single-cell gene expression analysis of cryopreserved equine bronchoalveolar cells [PDF]
Frontiers in Immunology, 2022 The transcriptomic profile of a cell population can now be studied at the cellular level using single-cell mRNA sequencing (scRNA-seq). This novel technique provides the unprecedented opportunity to explore the cellular composition of the bronchoalveolar
Sophie E. Sage +6 more
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CoCoA-diff: counterfactual inference for single-cell gene expression analysis [PDF]
Genome Biology, 2021 Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data.
Yongjin P. Park, Manolis Kellis
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Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis. [PDF]
Genome Biol, 2014 Abstract Background A fundamental challenge for cancer therapy is that each tumor contains a highly heterogeneous cell population whose structure and mechanistic underpinnings remain incompletely understood.
Saadatpour A, Guo G, Orkin SH, Yuan GC.
europepmc +8 more sources
Single-Cell Gene Expression Analysis Revealed Immune Cell Signatures of Delta COVID-19 [PDF]
Cells, 2022 The coronavirus disease 2019 (COVID-19) is accompanied by a cytokine storm with the release of many proinflammatory factors and development of respiratory syndrome. Several SARS-CoV-2 lineages have been identified, and the Delta variant (B.1.617), linked
Abusaid M. Shaymardanov +19 more
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SCANPY: large-scale single-cell gene expression data analysis [PDF]
Genome Biology, 2018 Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory ...
F. Alexander Wolf +2 more
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ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. [PDF]
Genome Biol, 2015 AbstractSingle cell RNA-seq data allows insight into normal cellular function and diseases including cancer through the molecular characterisation of cellular state at the single-cell level. Dimensionality reduction of such high-dimensional datasets is essential for visualization and analysis, but single-cell RNA-seq data is challenging for classical ...
Pierson E, Yau C.
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Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data [PDF]
BMC Bioinformatics, 2019 Background The analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research.
Tianyu Wang +3 more
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Erratum: Single-cell gene expression analysis of cryopreserved equine bronchoalveolar cells [PDF]
Frontiers in Immunology, 2023 Frontiers Production Office
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Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis. [PDF]
Front Genet, 2019 ABSTRACTAlthough single cell RNA sequencing (scRNA-seq) technology is newly invented and promising one, because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each cell. Because of this insufficient information available, unsupervised clustering, e.g., t-Distributed Stochastic ...
Taguchi YH, Turki T.
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Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes [PDF]
BMC Bioinformatics, 2018 Background Type 2 diabetes (T2D) is one of the most common chronic diseases. Studies on T2D are mainly built upon bulk-cell data analysis, which measures the average gene expression levels for a population of cells and cannot capture the inter-cell ...
Lichun Ma, Jie Zheng
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