spelling fixes and added ORCID into DESCRIPTION

DESCRIPTION

@@ -2,7 +2,15 @@ Package: APackOfTheClones
 Type: Package
 Title: Visualization of Clonal Expansion for Single Cell Immune Profiles
 Version: 1.2.0.9000
-Authors@R: c(person("Qile", "Yang", email = "qile.yang@berkeley.edu", role = c("cre","aut","cph")))
+Authors@R: c(
+    person(
+        "Qile",
+        "Yang",
+        email = "qile.yang@berkeley.edu",
+        role = c("cre","aut","cph"),
+        comment = c(ORCID = "0009-0005-0148-2499")
+    )
+)
 Maintainer: Qile Yang <qile.yang@berkeley.edu>
 Description: Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles. The method was originally implemented by Murray Christian and Ben Murrell in the following immunology study: Ma et al. (2021) <doi:10.1126/sciimmunol.abg6356>.
 BugReports: https://github.com/Qile0317/APackOfTheClones/issues/

joss/paper.md

@@ -31,11 +31,11 @@ The R [@r2023r] package APackOfTheClones implements and *extends* a novel method
 
 # Statement of need
 
-Cells in single-cell experiments are conventionally visualized on a reduction scatterplot where each point represents a cell after all its features have been projected into two dimensions. The points can be colored by different factors to display useful information, including its asssigned identity. [@andrews2021tutorial]
+Cells in single-cell experiments are conventionally visualized on a reduction scatterplot where each point represents a cell after all its features have been projected into two dimensions. The points can be colored by different factors to display useful information, including its assigned identity. [@andrews2021tutorial]
 
-Understanding the role of specific cells in various contexts requires understanding of relationships between cell populations and their behaviors. One attribute is the clonal expansion dynamics, which are inferred from downstream analyses. [@adams2020clonal] Overlaying clonal expansion information on a by-clonotype basis onto the reduction scatterplot of the cells of an experiment aids in adding an additional layer of insight, allowing for a swift, intuitive understanding of how clonal dynamics relate to the identified cellular subsets. For example, it can help gauge the presence of hyperexpanded clones for each cell type; compare potential changes in frequencies after certain theraputic treatments, etc.
+Understanding the role of specific cells in various contexts requires understanding of relationships between cell populations and their behaviors. One attribute is the clonal expansion dynamics, which are inferred from downstream analyses. [@adams2020clonal] Overlaying clonal expansion information on a by-clonotype basis onto the reduction scatterplot of the cells of an experiment aids in adding an additional layer of insight, allowing for a swift, intuitive understanding of how clonal dynamics relate to the identified cellular subsets. For example, it can help gauge the presence of hyper expanded clones for each cell type; compare potential changes in frequencies after certain therapeutic treatments, etc.
 
-There is no standardized convention to visualize this single cell level clonotype data on the dimensional reduction. Some of the current approaches include 1. Using a color gradient corresponding to each frequency to highlight each individual point by the clonal expansion, implpemented in *scRepertoire* and *scirpy* [sturm2020scirpy] 2. Overlaying a 2D contour where points representing clones with higher frequencies have elevated levels [@andreatta2023tcell] 3. Increasing sizes of points based on clonal frequency, used in figure 2c of @wang2021single.
+There is no standardized convention to visualize this single cell level clonotype data on the dimensional reduction. Some of the current approaches include 1. Using a color gradient corresponding to each frequency to highlight each individual point by the clonal expansion, implemented in *scRepertoire* and *scirpy* [sturm2020scirpy] 2. Overlaying a 2D contour where points representing clones with higher frequencies have elevated levels [@andreatta2023tcell] 3. Increasing sizes of points based on clonal frequency, used in figure 2c of @wang2021single.
 
 These approaches capture approximate, global trends but do not provide an exact representation. They lack precision in depicting the true diversity and abundance of clonal populations for every cell subset. From a visual standpoint, This limitation hinders subtle variations in clonal expansion patterns. APackOfTheClones solves this issue by representing exact sizes of each clonotype, in a manner that corresponds exactly to the relevant cell subset. This level of granularity is helpful uncovering hidden patterns, identifying rare clonal populations, and precisely quantifying the impact of therapeutic interventions on immune responses.
 
@@ -49,16 +49,16 @@ The main clonal expansion visualization that APackOfTheClones implements is show
 
 The visualization gives the immediate insight that certain cell subsets such as those in cluster four contains many more expanded clones both by quantity and frequency.
 
-The package extends objects and the functionality of the *Seurat* and *scRepertoire* package, and given a correctly processed seurat object of scRNA-seq data that was combined with paired TCR/BCRs, only a few functions need to be used to as little or as much customization of function arguments as needed to produce a ggplot object [@wickham2016ggplot2] that fits into the conventional plotting ecosystem of R. Functions are accelerated with a `c++` layer via the *Rcpp* package [@eddelbuettel2011rcpp] to deliver all plots and R objects quickly in time complexity linearly proportional to the number of cells, with the main time bottleneck being the plot display time.
+The package extends objects and the functionality of the *Seurat* and *scRepertoire* package, and given a correctly processed seurat object of scRNA-seq data that was combined with paired TCR/BCRs, only a few functions need to be used to as little or as much customization of function arguments as needed to produce a `ggplot` object [@wickham2016ggplot2] that fits into the conventional plotting ecosystem of R. Functions are accelerated with a `c++` layer via the *Rcpp* package [@eddelbuettel2011rcpp] to deliver all plots and R objects quickly in time complexity roughly linearly proportional to the number of cells, with the main time bottleneck being the plot display time.
 
 <!--
-- I should probably write about other novelty features - customizing, highlighting, clone links?
+- should I write about other novelty features - customizing, highlighting, clone links?
 - maybe an "implementation details" section about how the user can store "runs" of this plot with different parameters and manually customize them?
 -->
 
 # Conclusion
 
-APackOfTheClones offers a fast, and simple interface to produce an intuitive, easily extendible, and *publication ready* visualization of clonal expansion on a cell-by-cell basis, and slots seamlessly into existing analysis pipelines. It can be a useful subfigure in any immunological/theraputic study involving single cell omics and immune repertoire to provide an additional degree of understanding for readers and researchers like. However, it should be noted that prescise statistical/biological statements about clonal dynamics still obiouvsly require rigorous analysis.
+APackOfTheClones offers a fast, and simple interface to produce an intuitive, easily extendible, and *publication ready* visualization of clonal expansion on a cell-by-cell basis, and slots seamlessly into existing analysis pipelines. It can be a useful sub-figure in any immunological/therapeutic study involving single cell omics and immune repertoire to provide an additional degree of understanding for readers and researchers like. However, it should be noted that prescise statistical/biological statements about clonal dynamics still obiouvsly require rigorous analysis.
 
 # Acknowledgements