Merge branch 'devel'

CHANGELOG.md

@@ -1,42 +1,47 @@
 ## Upcoming
 
-## [1.0.7] - 2020-11-14
+## [1.0.8] - 2021 December 12
+
+- Fixed indexing error in writing the *csv files for the p2 app grids (e.g. "Gene sets in Aspect")
+
+
+## [1.0.7] - 2021 November 14
 
 - Added ability to export data in grids as *csv
 - Small revisions to docs
 
-## [1.0.6] - 2020-10-06
+## [1.0.6] - 2021 October 06
 
 - Fixed `read10xMatrix()` to work with new 10x files
 
-## [1.0.5] - 2020-08-11
+## [1.0.5] - 2021 August 11
 
 ### Changed
 
 - Removed the vignettes and dependency on the drat repository at https://github.com/kharchenkolab/p2data
 
-## [1.0.4] - 2020-06-28
+## [1.0.4] - 2021 June 28
 
 ### Changed
 
 - Fix `self$counts <- counts`
 - Fix `Knn()` in `pagoda2WebApp.R`
 
-## [1.0.3] - 2020-05-01
+## [1.0.3] - 2021 May 01
 
 ### Changed
 
 - Removed `jsDist()` as it's in sccore
 - Removed `multi2dend()` as it's in sccore
 - Removed strong dependency for p2data
 
-## [1.0.2] - 2020-03-03
+## [1.0.2] - 2021 March 03
 
 ### Changed
 
 - Revised vignettes figures for the HTML tutorial
 
-## [1.0.1] - 2020-02-25
+## [1.0.1] - 2021 February 25
 
 ### Added
 - Returned copy constructor
@@ -46,7 +51,7 @@
 - Updated Dockerfile
 - Fixed 'batch' param in Pagoda2 constructor
 
-## [1.0.0] - 2020-01-28
+## [1.0.0] - 2021 January 28
 - The package was edited extensively to upload to CRAN
 
 ### Added
@@ -60,14 +65,14 @@
 - revise `message()` spacing for `verbose` statements
 - various changes for CRAN, e.g. `par()`, removing calls to `installed.packages()`, etc.
 
-## [0.1.4] - 2020-10-05
+## [0.1.4] - 2020 October 05
 
 ### Added
 - added `p2app4conos()` for rendering Conos to pagoda2 application
 - README edits
 - vignettes edits
 
-## [0.1.3] - 2020-10-02
+## [0.1.3] - 2020 October 02
 
 ### Added
 - Makefile.win, Makevars.win
@@ -76,7 +81,7 @@
 ### Changed
 - Now should (natively) install on Mac OS for all users
 
-## [0.1.2] - 2020-09-24
+## [0.1.2] - 2020 September 24
 
 ### Added
 
@@ -88,7 +93,7 @@
 - Added LICENSE (July 2020)
 - Changed `std::cout` to `Rcpp::Rcout` (July 2020)
 
-## [0.1.1] - 2019-11-26
+## [0.1.1] - 2019 November 26
 
 ### Added
 
@@ -103,7 +108,7 @@
 - Fixed problem with duplicated observations during t-SNE estimation
 - Added option `var.scale` to `calculatePcaReduction`, which allow to disable variance scaling on counts
 
-## [0.1.0] - 2019-04-20
+## [0.1.0] - 2019 April 20
 
 ### Added
 

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: pagoda2
 Title: Single Cell Analysis and Differential Expression
-Version: 1.0.7
+Version: 1.0.8
 Authors@R: c(person("Nikolas","Barkas", email="[email protected]", role="aut"), person("Viktor", "Petukhov", email="[email protected]", role="aut"), person("Peter", "Kharchenko", email = "[email protected]", role = "aut"), person("Simon", "Steiger", email = "[email protected]", role = "ctb"), person("Evan", "Biederstedt", email="[email protected]", role=c("cre", "aut")))
 Description: Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.
 License: GPL-3 

README.md

@@ -101,5 +101,5 @@ If you find `pagoda2` useful for your publication, please cite:
 ```
 Nikolas Barkas, Viktor Petukhov, Peter Kharchenko and Evan
 Biederstedt (2021). pagoda2: Single Cell Analysis and Differential
-Expression. R package version 1.0.7.
+Expression. R package version 1.0.8.
 ```

doc/pagoda2.walkthrough.R

@@ -102,7 +102,7 @@ r$plotEmbedding(type='PCA', show.legend=FALSE, mark.groups=TRUE, min.cluster.siz
 
 
 ## ---- fig.height=6, fig.width=6-----------------------------------------------
-r$getEmbedding(type='PCA', embeddingType='tSNE', perplexity=50,verbose=FALSE)
+r$getEmbedding(type='PCA', embeddingType='tSNE', perplexity=50, verbose=FALSE)
 r$plotEmbedding(type='PCA', embeddingType='tSNE', show.legend=FALSE, mark.groups=TRUE, min.cluster.size=1, shuffle.colors=FALSE, font.size=3, alpha=0.3, title='clusters (tSNE)', plot.theme=theme_bw() + theme(plot.title = element_text(hjust = 0.5)))
 
 

doc/pagoda2.walkthrough.Rmd

@@ -193,7 +193,7 @@ r$plotEmbedding(type='PCA', show.legend=FALSE, mark.groups=TRUE, min.cluster.siz
 
 We next can construct and plot a tSNE embedding. (This can take some time to complete.)
 ```{r, fig.height=6, fig.width=6}
-r$getEmbedding(type='PCA', embeddingType='tSNE', perplexity=50,verbose=FALSE)
+r$getEmbedding(type='PCA', embeddingType='tSNE', perplexity=50, verbose=FALSE)
 r$plotEmbedding(type='PCA', embeddingType='tSNE', show.legend=FALSE, mark.groups=TRUE, min.cluster.size=1, shuffle.colors=FALSE, font.size=3, alpha=0.3, title='clusters (tSNE)', plot.theme=theme_bw() + theme(plot.title = element_text(hjust = 0.5)))
 ```
 

doc/pagoda2.walkthrough.html

@@ -476,7 +476,7 @@ <h3>Part 2: Analysing Data with Pagoda2</h3>
 
 <p>We next can construct and plot a tSNE embedding. (This can take some time to complete.)</p>
 
-<pre><code class="r">r$getEmbedding(type=&#39;PCA&#39;, embeddingType=&#39;tSNE&#39;, perplexity=50,verbose=FALSE)
+<pre><code class="r">r$getEmbedding(type=&#39;PCA&#39;, embeddingType=&#39;tSNE&#39;, perplexity=50, verbose=FALSE)
 r$plotEmbedding(type=&#39;PCA&#39;, embeddingType=&#39;tSNE&#39;, show.legend=FALSE, mark.groups=TRUE, min.cluster.size=1, shuffle.colors=FALSE, font.size=3, alpha=0.3, title=&#39;clusters (tSNE)&#39;, plot.theme=theme_bw() + theme(plot.title = element_text(hjust = 0.5)))
 </code></pre>
 

doc/pagoda2.walkthrough.md

@@ -327,7 +327,7 @@ r$plotEmbedding(type='PCA', show.legend=FALSE, mark.groups=TRUE, min.cluster.siz
 We next can construct and plot a tSNE embedding. (This can take some time to complete.)
 
 ```r
-r$getEmbedding(type='PCA', embeddingType='tSNE', perplexity=50,verbose=FALSE)
+r$getEmbedding(type='PCA', embeddingType='tSNE', perplexity=50, verbose=FALSE)
 r$plotEmbedding(type='PCA', embeddingType='tSNE', show.legend=FALSE, mark.groups=TRUE, min.cluster.size=1, shuffle.colors=FALSE, font.size=3, alpha=0.3, title='clusters (tSNE)', plot.theme=theme_bw() + theme(plot.title = element_text(hjust = 0.5)))
 ```
 

inst/rookServerDocs/js/aspectsTableViewer.js

@@ -108,7 +108,7 @@ aspectsTableViewer.prototype.generateTables = function() {
             tooltip: 'Export Selected',
             handler: function(){
               var grid = Ext.getCmp('genesetsAspectTable');
-              var csvContent = "data:text/csv;charset=utf-8\n";
+              var csvContent = "data:text/csv; charset=utf-8,";
 
               var columns = grid.columnManager.columns;
               var columnsCount = columns.length;
@@ -117,31 +117,24 @@ aspectsTableViewer.prototype.generateTables = function() {
                       csvContent += columns[i].text + ",";
                   }
               }
-              csvContent = csvContent.substring(0, csvContent.length-1);
+              csvContent = csvContent.substring(0, csvContent.length - 1);
               csvContent += "\r";
 
-              for (var j = 0; j<columnsCount; j++) {
-                  if(!columns[j].hidden){
-                      console.log(columns[j]);
-                  }
-              }
-
               var rows = grid.store.data.items;
               var rowsCount = rows.length;
 
               for (var i = 0; i < rowsCount; i++) {
                   var row = rows[i].data;
-                  for (var j = 1; j<columnsCount; j++) {
+                  for (var j = 0; j<columnsCount; j++) {
                       if (!columns[j].hidden) {
                           var value = row[columns[j].dataIndex];
-                          csvContent += "\"" + value + "\"" + ",";
+                          csvContent += value + ",";
                       }
                   }
-                  csvContent = csvContent.substring(0, csvContent.length-1);
+                  csvContent = csvContent.substring(0, csvContent.length - 1);
                   csvContent += "\r";
               }
 
-
               var encodedUri = encodeURI(csvContent);
               var is_safari = navigator.userAgent.toLowerCase().indexOf('safari/') > -1;