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InfinityLoop edited this page May 5, 2021 · 6 revisions

🤔What's TCC?

TCC^[1] is a R/Bioconductor package provides a series of functions for performing differential expression (DE) analysis from RNA-seq count data using a robust normalization strategy (called DEGES).

The basic idea of DEGES is that potential differentially expressed genes (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing the multi-step normalization procedures based on DEGES (DEG elimination strategy) implemented in TCC.

TCC internally uses functions provided by edgeR^[2], DESeq^[3], DESeq2^[4], and baySeq^[5] . The multi-step normalization of TCC can be done by using functions in the four packages.

🔬TCC-GUI: Graphical User Interface for TCC package

In this GUI version of TCC (TCC-GUI), all parameter settings are available just like you are using the original one. Besides, it also provides lots of plotting functions where the original package is unsupported now.

🛠Function

  • Generalization of Simulation data .
  • Dataset summarization and sample distribution plot for sample quality control.
  • Detection of differentially expressed genes (DEGs).
  • Interactive visualization of MA plot, Volcano plot, expression level plot and so on.
  • PCA and heatmap analysis (clustering included).
  • Output result in table, figure, code or report (.md, .pdf) (Under developing).

Please check other tab in Guidance for details.

📧Contact

If you have any question about the application, comment and advise, please contact 📧koji.kadota(at)gmail.com or 📧swsoyee(at)gmail.com.

Also, you can access 🔗Github and open a new issue for bug report or function requirement (you can write in English, Chinese or Japanese as you like).

📚References

[1] Sun J, Nishiyama T, Shimizu K, et al. TCC: an R package for comparing tag count data with robust normalization strategies. BMC bioinformatics, 2013, 14(1): 219.
[2] Robinson M D, McCarthy D J, Smyth G K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 2010, 26(1): 139-140.
[3] Anders S, Huber W. Differential expression analysis for sequence count data. Genome biology, 2010, 11(10): R106.
[4] Love M I, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 2014, 15(12): 550.
[5] Hardcastle T J, Kelly K A. baySeq : empirical Bayesian methods for identifying differential expression in sequence count data. BMC bioinformatics, 2010, 11(1): 422.

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