Showing 4 of total 4 results (show query)
topepo
caret:Classification and Regression Training
Misc functions for training and plotting classification and regression models.
Maintained by Max Kuhn. Last updated 4 months ago.
1.6k stars 19.24 score 61k scripts 303 dependents
bioc
BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes
Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.
Maintained by Matt Stone. Last updated 5 months ago.
softwareclusteringtranscriptomicsgeneexpressionsinglecellimmunooncologydataimportopenblascppopenmp
126 stars 7.90 score 278 scripts 1 dependents
myeomans
politeness:Detecting Politeness Features in Text
Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.
Maintained by Mike Yeomans. Last updated 2 months ago.
25 stars 7.49 score 41 scripts 1 dependentsbioc
MatrixQCvis:Shiny-based interactive data-quality exploration for omics data
Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.
Maintained by Thomas Naake. Last updated 5 months ago.
visualizationshinyappsguiqualitycontroldimensionreductionmetabolomicsproteomicstranscriptomics
4.74 score 4 scripts