The cytofkit package is designed to facilitate the analysis of mass cytometry data from one or multiple FCS files. User can customize their data merging strategy to combine data from multiple files with proper transformation; apply state-of-the-art clustering methods like DensVM  and PhenoGraph  as well as a in-house developed algorithm ClusterX to automatically detect cell subsets; visualize the high dimensional data with cluster labels by either linear transformation like PCA or non-linear dimensionality reduction like ISOMAP  or t-SNE ; map the progression of cellular subtypes estimated by ISOMAP or Diffusion map  and profile the marker expression along the cell progression. In addition to providing the research community with an one-stop analysis platform, cytofkit is shipped together with an intuitively designed GUI and an interactive web app to facilitate the easy analysis and interpretation of the mass cytometry data.
A schematic overview of the cytofkit pipeline is as below, it contains four major components:
In this step, one or multiple selected FCS files will be loaded into the R environment using the
read.FCS function of flowCore package. Expression values of selected markers are then extracted and transformed with a proper transformation method (including cytofAsinh (suggest for CyTOF data), autoLgcl (suggest for FCM data), logicle and arcsinh), expression matrixes from multiple files are then combined into a single matrix. For multiple FCS files, cytofkit provides four data combining methods including i) ceil which samples up to a user specified number of cells without replacement from each FCS file then combine, ii) all which takes all cells from each FCS file then combine, iii) min which samples a minimum number of cells among all FCS files from each FCS file then combine and iv) fixed which samples an user specified number of cells (with replacement when the total number of cell in the file is less than the specified number) from each FCS file then combine. After combination, the whole dataset will be stored in a data matrix, with row names represented as filename_cellID, column mames represented as marker_name
cytof_exprsMerge, read the details of this funcion:
The subset detection is performed by clustering algorithms. cytofkit is shipped with two
state-of-the-art clustering methods DensVM and PhenoGraph and an in-house developed clustering
algorithm ClusterX. Among them, DensVM and ClusterX are density-based clustering algorithms
estimated from the t-SNE embedded map. While PhenoGraph is a graph based clustering algorithm
performed directly on the high dimensional data. For version later than 1.4.4, we plugged in the FlowSOM clustering algorithm . Check the function
cytof_cluster for implementation of this step.
Three dimensionality reduction methods are integrated into cytofkit for visualizing the high
dimensional mass cytometry data into an information-condensed and relationship-preserved
two-dimensional map. These included one linear transformation method PCA and two non-linear
transformation methods ISOMAP and t-SNE. PCA is a widely used technique to remove the linear correlation between variables. After PCA, data is represented by principal components, and those
two or three principal components with highest variance are used to visualize the data. ISOMAP is
used to embed the high dimensional data points into low dimensions while preserving the intrinsic
geometry of the data structure. t-SNE finds the two-dimensional representation of high-dimensional
data that best preserve both local and global geometry. After dimensional reduction, cytofkit plots the cell points into these transformed two-dimensional map with point color representing its
cell type detected from last step, and point shape representing which sample the cell is from. To
summarize the expression profile of each cell subset, a heat map can be generated to visualize the
mean or median expression for each marker in each cell type. This heat map can greatly facilitate
the interpretation of known cell types with prior knowledge of their special marker expression
features as well as detection of new cell types with novel expression patterns. Check the function
cytof_dimReduction for implementation of this step.
Instead of inferring the subset progression using all cells, we perform the progression path
estimation based on the detected subsets of cells. Specifically, we down sample the number of
cells to a fixed number for each cell subset, thus to make every subset has the equal number of
cells. This removes the potential dominant influence from cell subtypes with most number of
cells. Then Diffusion Map or ISOMAP is used to infer the relationships among the down sampled subsets. Diffusion Map and ISOMAP are both
able to compare the overall relatedness of cells and embed the cells taking into account the nonlinear geodesic distances between cells. By plotting the marker expression along the Diffusion Map or ISOMAP dimension 1 or dimension 2 with lowess regression, markers with expression patterns that highly correlated with progression can be defined as regulatory markers. Check the function
cytof_progression for implementation of this step.
The cluster results can be visualized with dimension transformed data from t-SNE, PCA and ISOMAP. The cluster coordinates, together with the t-SNE, PCA and ISOMAP coordinates, will be stored to the FCS files as additional parameters and for post analysis, all result files and plots can be saved using the function
Among the saved results, an R data object will be saved with suffix of .RData, this is specially for loading to the shiny web APP for interactive visualization and exploration of the analysis results. The shiny APP can be launched with command
cytofkitShinyAPP() or through this link https://cytofkit.shinyapps.io/shiny/.
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