A curated selection of blog posts on single-cell data analysis. This list is a (constant) work in progress.
The creation of this list was inspired by Gopher Reading List.
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Introduction to single-cell RNA-seq technologies with slides here
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Current best practices in scRNA-seq data analysis paper (Mol Syst Biol 2019)
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Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data paper (Nature Protocols 2020)
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Next-generation computational tools for interrogating cancer immunity paper (Nature Genetics Reviews 2019)
- scRNA-seq analyses: challanges, opportunities, and best practices (2020) primer by Stephen Fleming (Broad Institute)
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Batch correction methods (2020), also see the GitHub repository
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Data integration methods (2020), also see the GitHub repository
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Systematic comparison of single-cell and single-nucleus RNA-sequencing methods (2020), also see the Bitbucket repository
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Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape (2021)
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Unique Molecular Identifiers – the problem, the solution and the proof
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Comment on Villani2017 paper in Science about new types of dendritic cells and monocytes in human blood
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Droplet scRNA-seq is not zero-inflated (2020) and UMI or not UMI, that is the question for scRNA-seq zero-inflation (2021)
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Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis (2021), which highlights the importance of disentangling expression and measurement models, and states that a Poisson measurement model is usually the most appropriate one to use
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Equivalence classes in RNA-seq analyses
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Normalization and variance stabilization and analytic Pearson residuals for scRNA-seq data
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Transformation and preprocessing of scRNA-seq data.
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Linking scRNA-seq to scDNA-seq data by Kieran Campbell
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Low Rank Approximation of Kernels — intuition behind FIt-tSNE. Also see this paper about revealing fine-grained structure by adjusting degree of freedom
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RNA velocity of single cells (2018) and dynamic modelling with scVelo (2020)
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Optimizing expression quantitative trait locus mapping workflows for single-cell studies (Genome Biology 2021)
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Count based autoencoders and the future for scRNA-seq analysis by Valentine Svensson