Package: cytoMEM 1.11.0

Jonathan Irish

cytoMEM: Marker Enrichment Modeling (MEM)

MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD.

Authors:Sierra Lima [aut], Kirsten Diggins [aut], Jonathan Irish [aut, cre]

cytoMEM_1.11.0.tar.gz
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cytoMEM.pdf |cytoMEM.html
cytoMEM/json (API)
NEWS

# Install 'cytoMEM' in R:
install.packages('cytoMEM', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cytolab/cytomem/issues

Datasets:

On BioConductor:cytoMEM-1.9.0(bioc 3.20)cytoMEM-1.8.0(bioc 3.19)

proteomicssystemsbiologyclassificationflowcytometrydatarepresentationdataimportcellbiologysinglecellclustering

4.18 score 15 scripts 198 downloads 3 exports 16 dependencies

Last updated 23 days agofrom:d2f1603c61. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-winNOTEOct 30 2024
R-4.5-linuxNOTEOct 30 2024
R-4.4-winNOTEOct 30 2024
R-4.4-macNOTEOct 30 2024
R-4.3-winNOTEOct 30 2024
R-4.3-macNOTEOct 30 2024

Exports:build_heatmapsMEMMEM_RMSD

Dependencies:BHBiobaseBiocGenericsbitopscaToolscpp11cytolibflowCoregplotsgtoolsKernSmoothmatrixStatsRcppRhdf5libRProtoBufLibS4Vectors

Intro_to_Marker_Enrichment_Modeling_Analysis

Rendered fromIntro_to_Marker_Enrichment_Modeling_Analysis.Rmdusingknitr::rmarkdownon Oct 30 2024.

Last update: 2022-03-18
Started: 2022-03-18