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AutoPipe
View on CRAN: Click
here
Download and install AutoPipe package within the R console
Install from CRAN:
install.packages("AutoPipe")
Install from Github:
library("remotes")
install_github("cran/AutoPipe")
Install by package version:
library("remotes")
install_version("AutoPipe", "0.1.6")
Attach the package and use:
library("AutoPipe")
Maintained by
Karam Daka
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-27
Latest Update: 2019-02-27
Description:
An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses.
We rely on the 'pamr' clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the
least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.
How to cite:
Karam Daka (2019). AutoPipe: Automated Transcriptome Classifier Pipeline: Comprehensive Transcriptome Analysis. R package version 0.1.6, https://cran.r-project.org/web/packages/AutoPipe
Previous versions and publish date:
No previous versions
Other packages that cited AutoPipe R package
View AutoPipe citation profile
Other R packages that AutoPipe depends,
imports, suggests or enhances
Functions, R codes and Examples using
the AutoPipe R package
Some associated functions: AutoPipe_tSNE . Groups_Sup . Supervised_Cluster_Heatmap . TopPAM . UnSuperClassifier . cons_clust . read_expression_file . rna . top_supervised .
Some associated R codes: AutoPipe_tSNE.R . Groups_Sup.R . Supervised_Cluster_Heatmap.R . TopPAM.R . UnSuperClassifier.R . cons_clust.R . data.R . entrez_to_name.R . new_nchheatmap.R . pathway_fgsea.R . read_expression_file.R . sil_width.R . supVisGenes.R . top_supervised.R . Full AutoPipe package functions and examples
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