KEYES-TIMOTHY . GITHUB . IO {
}
Title:
Analyze High-dimensional Cytometry Data Using Tidy Data Principles • tidytof
Description:
This package implements an interactive, scientific analysis pipeline for high-dimensional cytometry data built using tidy data principles. It is specifically designed to play well with both the tidyverse and Bioconductor software ecosystems, with functionality for reading/writing data files, data cleaning, preprocessing, clustering, visualization, modeling, and other quality-of-life functions. tidytof implements a "grammar" of high-dimensional cytometry data analysis.
Website Age:
12 years and 3 months (reg. 2013-03-08).
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Keywords {🔍}
dbl, cluster, patient, data, chr, tibble, tidytof, cells, stimulation, basal, files, phenograph, cdpop, column, dataset, toftbl, citrusdata, cytometry, head, groupcols, method, clusters, function, columns, bcrxl, analysis, fcs, expression, phenographdata, upn, package, functions, phenographcluster, tsne, model, singlecell, cell, output, read, code, condition, populationid, argument, tofdownsample, conditions, csv, sample, clustering, result, tidytofs,
Topics {✒️}
3-x86_64/resources/library/tidytof/extdata/phenograph bcr-xl condition compared jensen-shannon divergence index full-spectrum flow cytometry patient_id = patient_id represent high-dimensional datasets generalized-linear mixed modeling reduced-dimension feature embeddings ignoring time common data-processing tasks high-dimensional cytometry data multiple-hypothesis-corrected p write human-readable pipelines sample-level summary statistics sample-level clinical annotations aggregating single-cell measurements low-dimensional tsne embedding �simple” split cells’ b-cell receptors single-cell data directly single-cell data analysis k-fold cross-validation write single-cell data bcr-xl �bcr-xl” keyes-timothy/tidytof fcs cluster2 fcs cluster3 store single-cell data split high cd11b expression differential discovery analysis low-pstat5 expression groups lower cd11b expression patient-level data cluster-level identifying clusters paired t-test method unsupervised machine learning recommended list high-impact manuscript multiple-hypothesis correction phenograph cluster id high-level api antigens uniform manifold approximation /esm/art%3a10 common preprocessing steps paired t-tests principal component analysis data processing steps
Questions {❓}
- The non-numeric columns represent clinical metadata about each sample (run ?
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