cytofkit-package {cytofkit} | R Documentation

## cytofkit: an integrated mass cytometry data analysis pipeline

### Description

This package is designed to facilitate the analysis workflow of mass cytometry data with automatic subset identification and mapping of cellular progression. Both command line and a GUI client are provided for executing the workflow easily.

### Details

This package integrates merging methods of multiple FCS files, dimension reduction methods (PCA, t-SNE and ISOMAP) and clustering methods (DensVM, ClusterX, and Rphenograph) for rapid subset detection. Analysis results can be visualized and explored interactively using a specially designed shiny web APP, see `cytofkitShinyAPP`. Moreover, the method isomap is provided to map the cellular progression. This workflow can be easily executed with the main function `cytofkit` or through the GUI client `cytofkit_GUI`.

Pre-processing

Using function `cytof_exprsMerge`, one or multiple FCS files will be loaded via the *read.FCS* function in the *flowCore* package. Then transformation will be applied to the expression value of each FCS file. Transformation methods include `autoLgcl`, `cytofAsinh`, `logicle` and `arcsinh`, where `cytofAsinh` is the default.Then mutilple FCS files are merged using one of the merging methods `all`, `min`, `fixed` or `ceil`.

Dimensionality reduction

Using function `cytof_dimReduction`, t-Distributed Stochastic Neighbor Embedding (`tsne`) is suggested for dimensionality reduction with selected markers, although we also provide methods like `isomap` and `pca`.

Cluster

Using function `cytof_cluster`, three cluster method are provided, `DensVM`, `ClusterX` `Rphenograph` and `FlowSOM`. `DensVM`, `densityClustX` are performend on the dimension reduced data, while `Rphenograph` works directed on the high dimensional expression data. Method `FlowSOM` is integrated from FlowSOM package (https://bioconductor.org/packages/release/bioc/html/FlowSOM.html).

Post-processing

- Using function `cytof_clusterPlot` to visualize the cluster results in a scatter plot, in which dots represent cells, colours indicate their assigned clusters and point shapes represent their belonging samples.

- Using function `cytof_heatmap` to generate heat map to visualize the mean expression of every marker in every cluster. This heat maps is useful to interrogate marker expression to identify each cluster's defining markers.

- Using function `cytof_progressionPlot` to visualize the expression pattern of selected markers against the estimated cellular progression order.

- Using function `cytof_addToFCS` to add any dimension reduced data, cluster results, progression data into the original FCS files, new FCS files will be saved for easy checking with other softwares like FlowJo.

All the above post processing can be automatically implemented and saved using one function `cytof_writeResults`.

### Author(s)

Hao Chen, Jinmiao Chen

### References

https://github.com/JinmiaoChenLab/cytofkit

### See Also

`cytofkit`, `cytofkit_GUI`

### Examples

    ## Run on GUI
    #cytofkit_GUI()  # remove the hash symbol to launch the GUI

    ## Run on command
    dir <- system.file('extdata',package='cytofkit')
    file <- list.files(dir, pattern='.fcs$', full=TRUE)
    parameters <- list.files(dir, pattern='.txt$', full=TRUE)
    ## remove the hash symbol to run the following command
    #results <- cytofkit(fcsFile = file, markers = parameters)

    ## Checking the vignettes for more details
    if(interactive()) browseVignettes(package = 'cytofkit')

[Package cytofkit version 1.12.0 Index]