FAST

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FAST is a probabilistic factor analysis method for spatially-aware dimension reduction across multi-section spatial transcriptomics data. FAST is also a package for analyzing multiple spatially resolved transcriptomics (SRT) datasets, developed by the Jin Liu’s lab.

FAST can be used to compare and contrast experimental datasets in a variety of contexts, for instance:

  • Across experimental batches
  • Across individuals
  • Across different conditions (i.e., case and control)
  • Across datasets with only partially shared cell/domain clusters

Once the embeddings of multiple datasets are estimated by FAST, the package provides functionality for further data exploration, analysis, and visualization. Users can:

  • Align the embeddings obtained by FAST
  • Identify clusters using all data information
  • Recover comparable gene expression matrices among datasets
  • Find significant shared (and dataset-specific) gene markers
  • Visuzlize extracted embeddings using 3-dim tSNE and UMAP
  • Visualize clusters and gene expression using tSNE and UMAP

Installation

“FAST” depends on the ‘Rcpp’ and ‘RcppArmadillo’ package, which requires appropriate setup of computer. For the users that have set up system properly for compiling C++ files, the following installation command will work.

if (!require("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_github("feiyoung/FAST")

# If some dependent packages (such as `scater`) on Bioconductor can not be installed nomrally, use following commands, then run abouve command.
if (!require("BiocManager", quietly = TRUE)) ## install BiocManager
    install.packages("BiocManager")
# install the package on Bioconducter
BiocManager::install(c("scater"))

Usage

For usage examples and guided walkthroughs, check the vignettes directory of the repo.

For the users that don’t have set up system properly, the following setup on different systems can be referred. ## Setup on Windows system First, download Rtools; second, add the Rtools directory to the environment variable.

Setup on MacOS system

First, install Xcode. Installation about Xcode can be referred here.

Second, install “gfortran” for compiling C++ and Fortran at here.

Setup on Linux system

If you use conda environment on Linux system and some dependent packages (such as scater) can not normally installed, you can search R package at anaconda.org website. We take the scater package as example, and its search result is https://anaconda.org/bioconda/bioconductor-scater. Then you can install it in conda environment by following command.


conda install -c bioconda bioconductor-scater

For the user not using conda environment, if dependent packages (such as scater) not normally installed are in Bioconductor, then use the following command to install the dependent packages.

# install BiocManager
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
# install the package on Bioconducter
BiocManager::install(c("scater"))

If dependent packages (such as DR.SC) not normally installed are in CRAN, then use the following command to install the dependent packages.

# install the package on CRAN
install.packages("DR.SC")

NEWs

FAST version 1.2 (2023-07-20)