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From RNA-seq analysis to deep learning models Matrix multiplication is the heart of all bioinformatics computations.
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Explore the use of autoencoders to compress gene expression data and improve data analysis and visualization.
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Compare Alevin-fry’s pseudoalignment and Cell Ranger’s full alignment to find the right single-cell RNA-seq tool for your pipeline.
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There is more than just tSNE and UMAP in Single Cell Dimensional Reduction. In this post I will check Phate.
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Pseudotime trajectories and RNA velocity are computational approaches to analyze single cell dynamical process but, do they tell the same history?
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Explore SingleCellExperiment in R! Master data structures & workflows to handle single-cell RNA-seq data effectively in your research projects.
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