Package: HTSCluster 2.0.11
HTSCluster: Clustering High-Throughput Transcriptome Sequencing (HTS) Data
A Poisson mixture model is implemented to cluster genes from high- throughput transcriptome sequencing (RNA-seq) data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).
Authors:
HTSCluster_2.0.11.tar.gz
HTSCluster_2.0.11.zip(r-4.5)HTSCluster_2.0.11.zip(r-4.4)HTSCluster_2.0.11.zip(r-4.3)
HTSCluster_2.0.11.tgz(r-4.4-any)HTSCluster_2.0.11.tgz(r-4.3-any)
HTSCluster_2.0.11.tar.gz(r-4.5-noble)HTSCluster_2.0.11.tar.gz(r-4.4-noble)
HTSCluster_2.0.11.tgz(r-4.4-emscripten)HTSCluster_2.0.11.tgz(r-4.3-emscripten)
HTSCluster.pdf |HTSCluster.html✨
HTSCluster/json (API)
NEWS
# Install 'HTSCluster' in R: |
install.packages('HTSCluster', repos = c('https://andreamrau.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andreamrau/htscluster/issues
Last updated 1 years agofrom:ca2ab810ac. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 30 2024 |
R-4.5-win | OK | Oct 30 2024 |
R-4.5-linux | OK | Oct 30 2024 |
R-4.4-win | OK | Oct 30 2024 |
R-4.4-mac | OK | Oct 30 2024 |
R-4.3-win | OK | Oct 30 2024 |
R-4.3-mac | OK | Oct 30 2024 |
Exports:emInithighDimensionARIHTSClusterUsersGuidekmeanInitlogLikePoisMixlogLikePoisMixDiffPoisMixClusPoisMixClusWrapperPoisMixMeanPoisMixSimprobaPostprobaPostInitsplitEMInit
Dependencies:capusheedgeRlatticelimmalocfitMASSplotrixstatmod
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Clustering high throughput sequencing (HTS) data | HTSCluster-package HTSCluster |
Calculate ARI for high-dimensional data via data splits | highDimensionARI |
View HTSCluster User's Guide | HTSClusterUsersGuide |
Parameter initialization for a Poisson mixture model. | emInit kmeanInit probaPostInit splitEMInit |
Log likelihood calculation for a Poisson mixture model | logLikePoisMix logLikePoisMixDiff mylogLikePoisMixObs |
Visualize results from clustering using a Poisson mixture model | plot.HTSCluster plot.HTSClusterWrapper |
Poisson mixture model estimation and model selection | PoisMixClus PoisMixClusWrapper |
Calculate the conditional per-cluster mean of each observation | PoisMixMean |
Simulate data from a Poisson mixture model | PoisMixSim |
Calculate the conditional probability of belonging to each cluster in a Poisson mixture model | probaPost |
Summarize results from clustering using a Poisson mixture model | summary.HTSCluster summary.HTSClusterWrapper |