This is an integrated toolkits focusing on data analysis of omics data like transcriptomic, proteomic, metabolic and any other quantitative omics dataset.
Under development...
An toolkit for consensus clustering with multi-clustering algorithms.
Currently supported clustering methods:
hc_ward.D, hc_ward.D2, hc_single, hc_complete, hc_average, hc_mcquitty, hc_median, hc_centroid, hc_diana, kmeans, pam, fanny, hkmeans, Mclust, dbscan, con_kmeans, con_pam, con_hc_ward.D2, con_hc_complete, con_hc_average.
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Consensus differential expression analysis
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Network analysis
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Regression analysis
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Time serie anaylsis
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data normalization
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TBD
Details for future development plans can be viewed at https://github.com/FanqianYin/omicstoolkits/Features_under_developing.Rmd.
You can install the source package throuhg:
install.packages("devtools")
devtools::install_github("FanqianYin/omicstoolkits")
Or the latest development version:
install.packages("devtools")
devtools::install_github("FanqianYin/omicstoolkits-dev")
If you encounter any problem, don't hesitate to cantact me: fanqianyin@gmail.com or yinfanqian@mail.kiz.ac.cn.
Or report bugs at: https://github.com/FanqianYin/omicstoolkits/issues.
Suggestion: If you have any adivce on these tools, please let me know;
Collaboration: If you are interested in integrating these omicstoolkits, welcome to cooperate.
Under development: Differential expression analysis, data normalization
7/30/2020 Consensus_Cluster_Analysis: an toolkit focus on sample-based subtyping by using consensus clustering result of multi-clustering algorithms.
Current version: v0.1.2
v0.1.2 10/27/2020
Add methods for QC-based or QC-free data normalization (could be used at metabolomic or proteomic data, or other similar dataset)
v0.1.1 8/2/2020
Consensus_Cluster_Analysis toolkit: Add plot_consensus_clusters.heatmap(), plot_consensus_clusters.pca(), plot_cluster_algorithms.pca(), plot_cluster.pca(), for visulaztion result of Consensus_Cluster_Analysis().
v0.1.0 7/30/2020
Consensus_Cluster_Analysis toolkit: First release.