文件列表:

DESCRIPTION (700, 2018-12-02)
NAMESPACE (252, 2018-12-02)
R (0, 2018-12-02)
R\PAM50.R (3175, 2018-12-02)
R\data.R (48, 2018-12-02)
R\data_utils.R (2315, 2018-12-02)
R\plot_utils.R (4413, 2018-12-02)
R\sysdata.rda (865364, 2018-12-02)
_pkgdown.yml (679, 2018-12-02)
data (0, 2018-12-02)
data\es.rda (2008200, 2018-12-02)
docs (0, 2018-12-02)
docs\articles (0, 2018-12-02)
docs\articles\PAM50_tutorial.html (123352, 2018-12-02)
docs\articles\PAM50_tutorial_files (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\crosstalk-1.0.0 (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\crosstalk-1.0.0\css (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\crosstalk-1.0.0\css\crosstalk.css (784, 2018-12-02)
docs\articles\PAM50_tutorial_files\crosstalk-1.0.0\js (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\crosstalk-1.0.0\js\crosstalk.js (49022, 2018-12-02)
docs\articles\PAM50_tutorial_files\crosstalk-1.0.0\js\crosstalk.js.map (54360, 2018-12-02)
docs\articles\PAM50_tutorial_files\crosstalk-1.0.0\js\crosstalk.min.js (19163, 2018-12-02)
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docs\articles\PAM50_tutorial_files\datatables-binding-0.5 (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\datatables-binding-0.5\datatables.js (45750, 2018-12-02)
docs\articles\PAM50_tutorial_files\datatables-css-0.0.0 (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\datatables-css-0.0.0\datatables-crosstalk.css (108, 2018-12-02)
docs\articles\PAM50_tutorial_files\dt-core-1.10.16 (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\dt-core-1.10.16\css (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\dt-core-1.10.16\css\jquery.dataTables.extra.css (240, 2018-12-02)
docs\articles\PAM50_tutorial_files\dt-core-1.10.16\css\jquery.dataTables.min.css (14961, 2018-12-02)
docs\articles\PAM50_tutorial_files\dt-core-1.10.16\js (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\dt-core-1.10.16\js\jquery.dataTables.min.js (81906, 2018-12-02)
docs\articles\PAM50_tutorial_files\figure-html (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\figure-html\unnamed-chunk-8-1.png (248970, 2018-12-02)
docs\articles\PAM50_tutorial_files\figure-html\unnamed-chunk-9-1.png (102596, 2018-12-02)
docs\articles\PAM50_tutorial_files\htmlwidgets-1.3 (0, 2018-12-02)
docs\articles\PAM50_tutorial_files\htmlwidgets-1.3\htmlwidgets.js (31037, 2018-12-02)
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# PAM50 PAM50 package is a trained classifier that predicts PAM50 subtypes using patient's own gene expression data (NanoString, microarray, or RNA-seq). It can predict 5 subtypes for breast cancer patients: **Luminal A**, **Luminal B**, **HER2**, **Basal**, and **Normal**. Patients with Luminal A subtype usually have the best prognosis. Click [here](https://ww5.komen.org/BreastCancer/SubtypesofBreastCancer.html) for more detailed description of each subtype. gPAM50 uses 50 genes to make the prediction. As long as a patient has those 50 genes' expression data and Gene ID (Entrez ID), you can then predict PAM50 subtype for that patient. **Yes, it works for a single patient's data!** Interestingly, even the patient is not breast cancer patient, gPAM50 can still produce a prediction which may or may not be meaningful. But why don't you give it a try? Maybe you will discovery novel pathway or genes. Project page: https://ccchang0111.github.io/PAM50/ Source code: https://github.com/ccchang0111/PAM50 ## Installation You can install gPAM50 from github with: ``` r # install.packages("devtools") devtools::install_github("ccchang0111/gPAM50") ``` ## Tutorial Magics can be found here: https://ccchang0111.github.io/PAM50/articles/PAM50_tutorial.html

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