knitr::opts_chunk$set(echo = TRUE, cache = FALSE, eval = TRUE, warning = TRUE, message = TRUE)
Dear flowSpy users:
To improve the identification of this package and avoid awkward duplication of names in some situations, we decided to change the name of flowSpy
to CytoTree
. The package name of CytoTree
more fits the functional orientation of this software. The usage and update of flowSpy
and CytoTree
will be consistent until the end of Bioc 3.11. And for the 3.12 devel, flowSpy will be deprecated.
The package CytoTree
has been added to Bioconductor (https://bioconductor.org/packages/CytoTree/), we recommend that users can download this package and replace flowSpy
as soon as possible.
We apologized for the inconvenience.
flowSpy team
2020-07-09
See the quick start tutorial of flowSpy, please visit Quick start of flowSpy.
See the basic tutorial of flowSpy, please visit Tutorial of flowSpy.
See time-course data analysis of flowSpy, please visit Time-course workflow of flowSpy.
Although multidimensional single-cell-based flow and mass cytometry have been increasingly applied to microenvironmental composition and stem-cell research, integrated analysis workflows to facilitate the interpretation of experimental cytometry data remain underdeveloped. We present flowSpy, a comprehensive R package designed for the analysis and interpretation of flow and mass cytometry data. We applied flowSpy to mass cytometry and time-course flow cytometry data to demonstrate the usage and practical utility of its computational modules. flowSpy is a reliable tool for multidimensional cytometry data workflows and produces compelling results for trajectory construction and pseudotime estimation.
The flowSpy package is developed to complete the majority of standard analysis and visualization workflow for FCS data. In flowSpy workflow, an S4 object in R is built to implement the statistical and computational approach, and all computational modules are integrated into one single channel which only requires a specified input data format.
flowSpy
can help you to perform four main types of analysis:
Clustering. flowSpy
can help you to discover and identify subtypes of cells.
Dimensionality Reduction. Several dimensionality reduction methods are provided in flowSpy
package such as Principal Components Analysis (PCA), t-distributed Stochastic Neighbor Embedding (tSNE), Diffusion Maps and Uniform Manifold Approximation and Projection (UMAP). flowSpy provides both cell-based and cluster-based dimensionality reduction.
Trajectory Inference. flowSpy
can help you to construct the cellular differential based on minimum spanning tree (MST) algorithm.
Pseudotime and Intermediate states definition. The root cells need to be defined by users. The trajctroy value will be calculated based on Shortest Path from root cells and leaf cells using R igraph
package. Subset FCS data set in flowSpy
and find the key intermediate cell states based on trajectory value.
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