RNA-seq数据分析

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https://colauttilab.github.io/NGS/TuxedoTutorial.html

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https://www.frontiersin.org/articles/10.3389/fbinf.2021.693836/full

reads normalization,
scatter plots,
linear/non-linear correlations,
PCA,
clustering (hierarchical, k-means, t-SNE, SOM),
differential expression analyses,
pathway enrichments,
evolutionary analyses,
pathological analyses,
and protein-protein interaction (PPI) identifications.

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03549-8
BEAVR: a browser-based tool for the exploration and visualization of RNA-seq data

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http://master.bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html
RNA-seq workflow: gene-level exploratory analysis and differential expression
http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html

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https://hbctraining.github.io/scRNA-seq/lessons/02_SC_generation_of_count_matrix.html

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https://atap.psu.ac.th/

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https://degust.erc.monash.edu/

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9130758/

使用Python分析RNA数据所缺少的功能

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https://www.reneshbedre.com/blog/expression_units.html
Gene expression units explained: RPM, RPKM, FPKM, TPM, DESeq, TMM, SCnorm, GeTMM, and ComBat-Seq

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https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8

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https://www.intechopen.com/chapters/55603
RNA‐seq: Applications and Best Practices

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https://geoexplorer.rosalind.kcl.ac.uk/