R/Matlab software for dimensionality reduction with density-based partitioning Unsupervised cell population identification R/Bioconductor software that uses manual gates to perform an extensive series of statistical quality assessment checks on gated cell subpopulations R/Bioconductor package that provides infrastructure to generate interactive HTML quality report Identify outlier samples (e.g., wells drying out, reagent issues) R package that removes spurious events based on time vs. R/Bioconductor package that enable automated negative control-based gating and plate-based analysis R/Bioconductor package that overcomes memory limitations when working with large datasets by storing FCS data in netCDF files on disk R/Bioconductor core infrastructure that makes manually gated data accessible to BioConductors computational flow tools by importing pre-processed and gated data from FlowJoĪdvanced method for large dataset processing Import manually gated data from FlowJo workspaces, represent manual and automated gating hierarchies efficiently R/Bioconductor infrastructure to optimize parameter choice for different transformations R/Bioconductor package to support Gating-ML specification to exchange gate coordinates between softwareĮstimate parameters for data transformation Import gates, transformation and compensation R/Biconductor software that collects several algorithms together for normalization and gating R/Bioconductor package that provides preprocessing, automated gating, and meta-analysis of cytometry dataĪutomatically calculates detector efficiency (Q), optical background (B), and instrinsic CV of the beadsĪdvanced statistical methods and functions, specialized and general gating algorithms R/Bioconductor package that includes normalization, single-cell deconvolution and compensation for Mass cytometry data Pipeline for preprocessing of mass cytometry data R/Bioconductor package that combines flow cytometry data multiplexed into tubes by common markers R/Bioconductor package that provides gating and normalization specific to bead dataĬombining multitube flow cytometry data by binning R/Bioconductor core infrastructure for representing cell populations and parent/child relationships among them Read/Write, process (transform, compensate) of flow data. R/Bioconductor package that removes mean variance correlations from cell populations R/Bioconductor software to adjust data to account for batch effects like laser drift This allows users to substitute new approaches to the same challenge as the field advances, an advantage over monolithic tools that attempt to solve a single or even multiple problems in isolation. Algorithms for data analysis are provided as packages that generally address a single step in the analysis pipelines, with interoperability enforced through Bioconductor. Many of the approaches have been released through the Bioconductor repository which enforces strict requirements on cross-platform compatibility and functional documentation. For example, the flowWorkspace package can export automated gating results in a format readable by FlowJo (FlowJo Inc., Ashland OR). However, these tools can be integrated into commercial tools familiar to users, facilitating adoption. These tools have been developed for high-throughput workflows, and are not generally amenable to graphical user interface manual interaction with individual files during the analysis process. The overwhelming majority have been developed and released as freely available, open-source tools using the R programming language. The Clambey laboratory has used this algorithm in publications for the analysis of CyTOF data as well as RNA flow cytometric data.More than 50 approaches to automate flow cytometry (FCM) data analysis are available ( Table 1). Within the VorteX Clustering Environment users can visualize their data by a number of methods (a three dimensional PCA plot, force directed layout, etc.).Īlthough X-shift/VorteX is a very powerful tool for analysis, it does require more advanced computational skills. ![]() ![]() These iterations are taken into account to determine the optimal number of clusters to prevent the underclustering or overfragmentation of the data via the “ elbow method”. X-shift calculates the impact of different numbers of nearest neighbors on the number of clusters discovered and produces 30+ analysis iterations. After the graph is constructed the cell event density is used to partition the data into clusters. The X-shift algorithm is a clustering algorithm that utilizes the data to construct a weighted k-nearest-neighbor density estimation (kNN-DE) graph.
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