DepecheR - Determination of essential phenotypic elements of clusters in high-dimensional entities

The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.

Last updated 26 days ago

softwarecellbasedassaystranscriptiondifferentialexpressiondatarepresentationimmunooncologytranscriptomicsclassificationclusteringdimensionreductionfeatureextractionflowcytometryrnaseqsinglecellvisualization

5.13 score 15 scripts 382 downloads

flowSpecs - Tools for processing of high-dimensional cytometry data

This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data.

Last updated 26 days ago

softwarecellbasedassaysdatarepresentationimmunooncologyflowcytometrysinglecellvisualizationnormalizationdataimport

4.38 score 6 stars 7 scripts 216 downloads