bumped to v2.0.0

DESCRIPTION

@@ -1,21 +1,17 @@
 Package: FRmatch
-Title: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test
-Version: 1.1.0
+Title: Cell type matching in single-cell RNA-sequencing data using FR-Match
+Version: 2.0.0
 Authors@R: c(
     person(given = "Yun",
            family = "Zhang",
            role = c("aut", "cre"),
            email = "[email protected]"),
-    person(given = "Brian",
-           family = "Aevermann",
-           role = c("ctb"),
-           email = "[email protected]"),
     person(given = "Richard",
            family = "Scheuermann",
            role = c("ctb"),
            email = "[email protected]")
     )
-Description: FR-Match is a cell type cluster mapping algorithm for single cell RNA sequencing (scRNAseq) data. It is based on a statistical test called Friedman-Rafsky (FR) test, which is a multivariate generalization of nonparametric two-sample test. This package also provides visualization tools for the implemented method.
+Description: FR-Match is a cell type cluster mapping algorithm for single cell RNA-sequencing (scRNA-seq) data. The core method is based on a statistical test called Friedman-Rafsky (FR) test, which is a multivariate non-parametric test. This package implements the core algorithms and companion visualization tools for the method.
 Depends: R (>= 4.0.0), shiny (>= 1.2.0), SingleCellExperiment, pbmcapply
 Imports: methods, S4Vectors, SummarizedExperiment, Seurat, scmap, lsa, igraph, ade4, tibble, dplyr, tidyr, forcats, magrittr, pheatmap, RColorBrewer, ggplot2, gridExtra, viridis
 Suggests: 

R/FRmatch.R

@@ -32,10 +32,6 @@
 #' \item{statmat}{A matrix of FR statistics. Rows are reference clusters, and columns are query clusters.}
 #' If \code{return.all = TRUE}, more intermediate results are returned.
 #'
-#' @author Yun Zhang, \email{[email protected]};
-#' Brian Aevermann, \email{[email protected]};
-#' Richard Scheuermann, \email{[email protected]}.
-#'
 #' @seealso Visualization of matching results using \code{\link[FRmatch]{plot_FRmatch}}, \code{\link[FRmatch]{plot_bi_FRmatch}}.
 #'
 #' @examples

R/FRmatch_cell2cluster.R

@@ -38,10 +38,6 @@
 #' from the iterative procedure and assigned a higher p-value. The output from this implementation includes a cell-by-cluster
 #' (a.k.a. query cell by reference cluster) matrix of p-values.
 #'
-#' @author Yun Zhang, \email{[email protected]};
-#' Brian Aevermann, \email{[email protected]};
-#' Richard Scheuermann, \email{[email protected]}.
-#'
 # @seealso Visualization of matching results using \code{\link[FRmatch]{plot_FRmatch_cell2cluster}}}.
 #'
 #' @examples

README.md

@@ -1,4 +1,4 @@
-# FR-Match: A cluster-to-cluster cell type matching method for single cell RNA sequencing experiments
+# Cell type matching in single-cell RNA-sequencing data using FR-Match
 
 <img src="vignettes/FRmatch-sticker-2.png" width="110" height="125">
 
@@ -8,7 +8,7 @@ Zhang et al. 2020. FR-Match: robust matching of cell type clusters from single c
 
 ## Description
 
-Recently, the emergence of single cell RNA sequencing (scRNAseq) is providing large amounts of single cell transcriptomics data for the unbiased quantifications of cellular heterogeneity. Though scRNAseq data have been successfully generated by many labs, less attention has been paid to how knowledge derived from these data can be integrated across studies and leveraged by the whole single cell community.  In this R package, we provide a user-friendly scRNAseq integration tool that uses statistical methods to map new/query cell cluster data to the reference cell clusters.
+Recently, the emergence of single cell RNA-sequencing (scRNA-seq) is providing large amounts of single cell transcriptomics data for the unbiased quantifications of cellular heterogeneity. Though scRNAseq data have been successfully generated by many labs, less attention has been paid to how knowledge derived from these data can be integrated across studies and leveraged by the whole single cell community.  In this R package, we provide a user-friendly scRNAseq integration tool that uses statistical methods to map new/query cell cluster data to the reference cell clusters.
 
 Our method, FR-Match, is a novel application of the Friedman-Rafsky (FR) test, a non-parametric statistical test for multivariate data comparison in the context of single cell clustering results. We tailor the classical testing procedure for scRNAseq experiment data under the null hypothesis that there is no distributional difference in the two comparing clusters (i.e. a match) and the alternative hypothesis that the distributions of the two comparing clusters are different (i.e. a non-match) in the high-dimensional data space defined by selected gene features. Our procedure takes clustered gene expression matrices of query and reference experiments, and returns the FR statistic with adjusted p-value as evidence that the pair of comparing cell clusters is matched or not.
 
@@ -43,21 +43,21 @@ FRmatch::runShiny()
 
 ## Versioning
 
-This is version 1.0.0. Please report issues using the [issue traker](https://github.com/JCVenterInstitute/FRmatch/issues).
+The development of FR-Match follows a CI/CD (continuous integration and continuous delivery) coding practice. Stable releases and version control are managed in GitHub.
 
-FR-Match is undergoing active development. Latest version and version control are managed in GitHub.
+Please report issues using the [issue traker](https://github.com/JCVenterInstitute/FRmatch/issues).
 
 ## Authors
 
 * Yun (Renee) Zhang [email protected]
-* Brian Aevermann [email protected]
 * Richard Scheuermann [email protected]
 
-## License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
 ## Acknowledgments
 
 * Allen Institute for Brain Science
-* Chan Zuckerberg Initiative
+* Chan Zuckerberg Initiative (Completed grant: DAF 2018–182730)
+* The NIH BRAIN Initiative (Active grant: 1RF1MH123220)
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).