Showing 12 of total 12 results (show query)
bioc
tidytof:Analyze High-dimensional Cytometry Data Using Tidy Data Principles
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.
Maintained by Timothy Keyes. Last updated 5 months ago.
singlecellflowcytometrybioinformaticscytometrydata-sciencesingle-celltidyversecpp
37.0 match 19 stars 7.26 score 35 scriptsbioc
sccomp:Tests differences in cell-type proportion for single-cell data, robust to outliers
A robust and outlier-aware method for testing differences in cell-type proportion in single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.
Maintained by Stefano Mangiola. Last updated 16 days ago.
bayesianregressiondifferentialexpressionsinglecellbatch-correctioncompositioncytofdifferential-proportionmicrobiomemultilevelproportionsrandom-effectssingle-cellunwanted-variation
12.3 match 99 stars 8.41 score 69 scripts
niaid
HDStIM:High Dimensional Stimulation Immune Mapping ('HDStIM')
A method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, 'HDStIM' labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.
Maintained by Rohit Farmer. Last updated 1 years ago.
complexheatmapassaycytofcytometrycytometry-analysis-pipelineflowcytometrystimulation
14.5 match 3 stars 4.41 score 17 scripts
yannabraham
bodenmiller:Profiling of Peripheral Blood Mononuclear Cells using CyTOF
This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.
Maintained by Yann Abraham. Last updated 4 years ago.
bioinformaticscytofdatasetscience
13.9 match 2 stars 4.45 score 28 scriptsbioc
CyTOFpower:Power analysis for CyTOF experiments
This package is a tool to predict the power of CyTOF experiments in the context of differential state analyses. The package provides a shiny app with two options to predict the power of an experiment: i. generation of in-sicilico CyTOF data, using users input ii. browsing in a grid of parameters for which the power was already precomputed.
Maintained by Anne-Maud Ferreira. Last updated 6 hours ago.
flowcytometrysinglecellcellbiologystatisticalmethodsoftware
8.8 match 4.18 score 2 scriptsbioc
CATALYST:Cytometry dATa anALYSis Tools
CATALYST provides tools for preprocessing of and differential discovery in cytometry data such as FACS, CyTOF, and IMC. Preprocessing includes i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. For differential discovery, the package provides a number of convenient functions for data processing (e.g., clustering, dimension reduction), as well as a suite of visualizations for exploratory data analysis and exploration of results from differential abundance (DA) and state (DS) analysis in order to identify differences in composition and expression profiles at the subpopulation-level, respectively.
Maintained by Helena L. Crowell. Last updated 4 months ago.
clusteringdataimportdifferentialexpressionexperimentaldesignflowcytometryimmunooncologymassspectrometrynormalizationpreprocessingsinglecellsoftwarestatisticalmethodvisualization
2.5 match 67 stars 11.06 score 362 scripts 2 dependents
cran
RPointCloud:Visualizing Topological Loops and Voids
Visualizations to explain the results of a topological data analysis. The goal of topological data analysis is to identify persistent topological structures, such as loops (topological circles) and voids (topological spheres), in data sets. The output of an analysis using the 'TDA' package is a Rips diagram (named after the mathematician Eliyahu Rips). The goal of 'RPointCloud' is to fill in these holes in the data by providing tools to visualize the features that help explain the structures found in the Rips diagram. See McGee and colleagues (2024) <doi:10.1101/2024.05.16.593927>.
Maintained by Kevin R. Coombes. Last updated 7 months ago.
9.0 match 2.78 scorebioc
cytofQC:Labels normalized cells for CyTOF data and assigns probabilities for each label
cytofQC is a package for initial cleaning of CyTOF data. It uses a semi-supervised approach for labeling cells with their most likely data type (bead, doublet, debris, dead) and the probability that they belong to each label type. This package does not remove data from the dataset, but provides labels and information to aid the data user in cleaning their data. Our algorithm is able to distinguish between doublets and large cells.
Maintained by Jill Lundell. Last updated 5 months ago.
5.0 match 2 stars 4.30 score 3 scriptsbioc
Sconify:A toolkit for performing KNN-based statistics for flow and mass cytometry data
This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.
Maintained by Tyler J Burns. Last updated 5 months ago.
immunooncologysinglecellflowcytometrysoftwaremultiplecomparisonvisualization
4.3 match 4.74 score 11 scriptsbioc
tidyFlowCore:tidyFlowCore: Bringing flowCore to the tidyverse
tidyFlowCore bridges the gap between flow cytometry analysis using the flowCore Bioconductor package and the tidy data principles advocated by the tidyverse. It provides a suite of dplyr-, ggplot2-, and tidyr-like verbs specifically designed for working with flowFrame and flowSet objects as if they were tibbles; however, your data remain flowCore data structures under this layer of abstraction. tidyFlowCore enables intuitive and streamlined analysis workflows that can leverage both the Bioconductor and tidyverse ecosystems for cytometry data.
Maintained by Timothy Keyes. Last updated 5 months ago.
singlecellflowcytometryinfrastructure
3.4 match 1 stars 4.30 score 7 scriptsbioc
scDataviz:scDataviz: single cell dataviz and downstream analyses
In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease.
Maintained by Kevin Blighe. Last updated 5 months ago.
singlecellimmunooncologyrnaseqgeneexpressiontranscriptionflowcytometrymassspectrometrydataimport
1.2 match 63 stars 6.30 score 16 scriptsbioc
diffcyt:Differential discovery in high-dimensional cytometry via high-resolution clustering
Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics.
Maintained by Lukas M. Weber. Last updated 1 months ago.
immunooncologyflowcytometryproteomicssinglecellcellbasedassayscellbiologyclusteringfeatureextractionsoftware
0.5 match 20 stars 9.98 score 225 scripts 5 dependents