Updated news, bumped version to 2.4.1, moved R version requirement back to 4.0

Author: joshua-d-campbell

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: singleCellTK
 Type: Package
 Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
-Version: 2.4.0
+Version: 2.4.1
 Authors@R: c(person(given="Yichen", family="Wang", email="wangych@bu.edu", role=c("aut", "cre"),
                     comment = c(ORCID = "0000-0003-4347-5199")),
              person(given="Irzam", family="Sarfraz", email="irzam9095@gmail.com", role=c("aut")),
@@ -22,12 +22,12 @@ Authors@R: c(person(given="Yichen", family="Wang", email="wangych@bu.edu", role=
              person(given=c("Joshua", "David"), family="Campbell", email="camp@bu.edu", role=c("aut"))
              )
 Depends:
-    R (>= 4.1),
+    R (>= 4.0),
     SummarizedExperiment,
     SingleCellExperiment,
     DelayedArray,
     Biobase
-Description: singleCellTK is a comprehensive platform that provides an interface to popular scRNA-seq data QC, processing, and visualization tools. By integrating functions and methods from various publically available dependencies, SCTK allows users to perform different types of analyses seamlessly. SCTK can be installed locally on popular operating systems such as Windows, Linux and MacOS. The architecture of the toolkit includes basically the R console based function sets, as well as a GUI, powered by R Shiny. Besides, Docker images of SCTK Apps and web-server based access are also supported. For specific analyses in SCTK, the results can be summarized and easily shared using comprehensive HTML reports, templated with Rmarkdown. 
+Description: The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general “a la carte” workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.
 License: MIT + file LICENSE
 Encoding: UTF-8
 biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment,

NEWS.md

@@ -1,7 +1,11 @@
-Latest Developmental Changes
-================================================================================
-* Added functions plotDEGVolcano() & plotPathway()
-* Added Pathway Analysis section to tutorial
+Changes in Version 2.4.1 (2021-12-22)
+================================================================================
+* Added new function fora DEG volcano plot (plotDEGVolcano)
+* Added new function for plotting pathway scores (plotPathway)
+* Added Pathway Analysis section to the tutorials
+* Added seed parameter to several functions and UI for reproducibility
+* Updated R console and GUI tutorials to match each other
+* Fixed console logging in the GUI
 
 Changes in Version 2.4.0 (2021-10-27)
 ================================================================================