- scAI is an unsupervised approach for integrative analysis of gene expression and chromatin accessibility or DNA methylation proflies measured in the same individual cells.
- scAI infers a set of biologically relevant factors, which enable various downstream analyses, including the identification of cell clusters, cluster-specific markers and regulatory relationships.
- scAI provides an intuitive way to visualize features (i.e., genes and loci) alongside the cells in two dimensions.
- scAI aggegrates chromatin profiles of similar cells in an unsupervised and iterative manner, which opens up new avenues for analyzing extremely sparse, binary scATAC-seq data.
Once the single cell multi-omics data are decomposed into multiple biologically relevant factors, the package provides functionality for further data exploration, analysis, and visualization. Users can:
- Visualize the latent biological patterns of the multi-omics data
- Visualize both genes and loci alongside cells onto the same two-dimensional space
- Identify cell clusters from the inferred joint cell loading matrix and cluster-specific markers
- Visualize clusters and gene expression in the low-dimensional space such as VscAI, t-SNE and UMAP
- Infer regulatory relationships between cluster-specific chromatin region and marker genes
Check out our paper (Suoqin Jin#, Lihua Zhang# & Qing Nie*, Genome Biology, 2020) for the detailed methods and applications.
scAI has been implemented as both R package and MATLAB package under the license GPL-3. In each package, we provide example workflows that outline the key steps and unique features of scAI. The MATLAB package and examples are available here.
devtools::install_github("sqjin/scAI")
Download source codes here and type (in R)
install.packages(path_to_file, type = 'source', rep = NULL) # The path_to_file would represent the full path and file name
This website shows other ways for building and installing an R package.
All the R markdown used to generate the walkthroughs can be found under the /examples directory.
- Simulated single cell RNA-seq and ATAC-seq data (Walkthrough): This simulated data were generated based on bulk RNA-seq and DNase-seq profiles from the same sample using MOSim package.
- Simulated single cell RNA-seq and ATAC-seq dataset 8 (Walkthrough): This simulated data set consists of five imbalanced cell clusters with five clusters in scRNA-seq data and three clusters in scATAC-seq data.
- Paired single cell RNA-seq and ATAC-seq data of A549 cells (Walkthrough): This data describes lung adenocarcinoma-derived A549 cells after 0, 1 and 3 hours of 100 nM dexamethasone treatment.
- Paired single-cell RNA-seq and single-cell methylation data of mESC (Walkthrough): This data describes the differentiation of mouse embryonic stem cells (mESC).
- Paired single cell RNA-seq and ATAC-seq data of Kidney cells (Walkthrough): This data describes various subpopulations of Kidney cells, including scRNA-seq and scATAC-seq data of 8837 co-assayed cells.
object <- run_scAI(object, K, do.fast = TRUE)
Feature selection can reduce the running time in both scAI model and downstream analysis such as dimension reduction.
- Using informative genes for scRNA-seq data:
The most informative genes can be selected based on their average expression and Fano factor (see our paper for details).
object <- selectFeatures(object, assay = "RNA")
object <- run_scAI(object, K, do.fast = TRUE, hvg.use1 = TRUE)
- Using informative loci for scATAC-seq or single cell methylation data:
Unlike scRNA-seq data, the largely binary nature of scATAC-seq data makes it challenging to perform ‘variable’ feature selection. One option is to select the nearby chromsome regions of the informative genes.
object <- selectFeatures(object, assay = "RNA")
loci.use <- searchGeneRegions(genes = object@var.features[[1]], species = "mouse")
object@var.features[[2]] <- loci.use
object <- run_scAI(object, K, do.fast = TRUE, hvg.use1 = TRUE, hvg.use2 = TRUE)
Another option is to use only the top n% of features or remove features present in less that n cells. This method is used in Signac.
- Please consider install RcppEigen and rfunctions if they are not automatically installed.
if(!require(devtools)){ install.packages("devtools")}
install.packages("RcppEigen")
devtools::install_github("jaredhuling/rfunctions")
Troubleshooting: Installing RcppEigen and rfunctions on R>=3.5 requires Clang >= 6 and gfortran-6.1. For MacOS, it's recommended to follow guidance on the official R page here OR the post. For Windows, please ensure that Rtools is installed.
- Install other dependencies
scAI provides functionality for further data exploration, analysis, and visualization. A couple of excellent packages need to be installed.
library(devtools)
install_github('linxihui/NNLM')
install_github("yanwu2014/swne")
install_github("jokergoo/ComplexHeatmap")
-
Install Leiden algorithm for identifying cell clusters: pip install leidenalg. Please check here if there is any trouble.
-
Install UMAP and FIt-SNE for faster dimension reduction in
reducedDims
Using UMAP and FIt-SNE is recommended for computational efficiency when using reducedDims on very large datasets.
-- install UMAP Python package: pip install umap-learn. Please check here if there is any trouble.
-- install FIt-SNE R package: Installing and compiling the necessary software requires the use of FIt-SNE and FFTW. For detailed instructions of installation, please visit this page.
If you get the error "clang: error: unsupported option '-fopenmp'" when installing R package, please consider the configuration in ~/.R/Makevars and see this post for detailed configuration. In addition, you may can also reinstall your R because -fopenmp option is usually added by R automatically if openmp is available.
If you are using macOS Mojave Version (10.14) and you might get the error "/usr/local/clang6/bin/../include/c++/v1/math.h:301:15: fatal error: 'math.h' file not found", please check the post. This error can be solved if running the following on the terminal:
sudo installer -pkg \
/Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg \
-target /
If you have any problems, comments or suggestions, please contact us at Suoqin Jin (suoqin.jin@uci.edu) or Lihua Zhang (lihuaz1@uci.edu).
Jin, S., Zhang, L. & Nie, Q. scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles. Genome Biol 21, 25 (2020). https://doi.org/10.1186/s13059-020-1932-8