Merge pull request #25 from jamesdalg/dev

CRAN fixes--rebinGenomicInteractions

Author: jamesdalg

DESCRIPTION

@@ -1,13 +1,13 @@
 Package: CNVScope
 Type: Package
 Title: A Versatile Toolkit for Copy Number Variation Relationship Data Analysis and Visualization
-Version: 2.8.2
-Date: 2019-10-23
+Version: 2.9.2
+Date: 2019-12-5
 Author: James Dalgeish, Yonghong Wang, Jack Zhu, Paul Meltzer
 Maintainer: James Dalgleish <[email protected]>
 BugReports: https://github.com/jamesdalg/CNVScope/issues/
 Depends: R (>= 3.5.0),ggplot2
-Imports: InteractionSet,BiocManager,tidyr,S4Vectors,circlize,igraph,visNetwork,reshape2,magrittr,htmltools,htmlwidgets,jointseg,plotly,shinyjs,logging,shiny,RCurl,foreach,GenomicFeatures,GenomicInteractions,HiCseg,IRanges,Matrix,OpenImageR,biomaRt,heatmaply,matrixStats,shinythemes,shinycssloaders,spatialfil,GenomicRanges,plyr,data.table,rslurm,dplyr,numbers,rtracklayer,BSgenome.Hsapiens.UCSC.hg19,doParallel,stringr,tibble,GenomeInfoDb
+Imports: InteractionSet,BiocManager,tidyr,S4Vectors,circlize,igraph,visNetwork,reshape2,magrittr,htmltools,htmlwidgets,jointseg,plotly,shinyjs,logging,shiny,RCurl,foreach,GenomicFeatures,GenomicInteractions,HiCseg,IRanges,Matrix,OpenImageR,biomaRt,heatmaply,matrixStats,shinythemes,shinycssloaders,spatialfil,GenomicRanges,plyr,data.table,rslurm,dplyr,numbers,rtracklayer,BSgenome.Hsapiens.UCSC.hg19,doParallel,stringr,tibble,GenomeInfoDb,Biostrings
 Suggests: knitr, rmarkdown,remotes,pwr,ComplexHeatmap
 VignetteBuilder: knitr
 URL: https://github.com/jamesdalg/CNVScope/

NEWS.md

@@ -1,4 +1,10 @@
-CNVScope v2.8.1 (Release Date 2019-10-23)
+CNVScope v2.9.2 (Release Date 2019-12-5)
+==============
+*Added an explicit dependency, Biostrings to keep CRAN check errors on debian from occurring.
+*Made a small fix to rebinGenomicInteractions, fixing a check at the end to handle a vector input
+**This will make the package compatible with R 4.0.
+
+CNVScope v2.8.2 (Release Date 2019-10-23)
 ==============
 *Removed all blockseg references in code. It was a nice feature to have a third algorithm, but blockseg will not be on CRAN shortly.
 

R/rebinGenomicInteractions.R

@@ -63,10 +63,16 @@ rebinGenomicInteractions<-function(gint=NULL,whole_genome_matrix=NULL,rownames_g
     names(outputline)<-outputnames
     outputline
   }
-  if(class(output)=="character" & is.null(nrow(output))){output_df<-as.data.frame(t(output))
-  #colnames(output_df)<-c("row_bin_index","col_bin_index","row_bin_label","col_bin_label",as.character(names(gint)))
-  } else {
+
+  if(class(output)[1]=="character" & is.null(nrow(output))){
+  output_df<-as.data.frame(t(output))
+  colnames(output_df)<-c("row_bin_index","col_bin_index","row_bin_label","col_bin_label",as.character(names(gint)))
+  
+    } else {
+    print("arrived in else condition")
   output_df<-as.data.frame(matrix(as.character(output),nrow=nrow(output)))
-  colnames(output_df)<-c("row_bin_index","col_bin_index","row_bin_label","col_bin_label",as.character(colnames(as.data.frame(gint[1]))))}
+  colnames(output_df)<-c("row_bin_index","col_bin_index","row_bin_label","col_bin_label",as.character(colnames(as.data.frame(gint[1]))))
+  }
+  
   return(output_df)
 }

doc/additonal_examples.Rmd

@@ -53,7 +53,10 @@ sample_aggregated_segvals_aml<-formSampleMatrixFromRawGDCData(tcga_files = targe
 saveRDS(sample_aggregated_segvals_aml,"aml_sample_matched_input_matrix.rds")
 ```
 
-Now that we've created and saved the AML matrix, let's visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene(. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations(reference: http://www.bloodjournal.org/blood/article/130/Supplement%201/3800/71708/Recurrent-Copy-Number-Variants-Are-Highly?searchresult=1).
+Now that we've created and saved the AML matrix, let's visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations.
+
+Reference: Tarlock et al.; Recurrent Copy Number Variants Are Highly Prevalent
+in Acute Myeloid Leukemia. Blood 2017; 130 (Supplement 1): 3800.).
 
 Some of the bins are invariant and correlation requires that the standard deviation be a value other than zero (otherwise correlation cannot be calculated). We will remove them in a couple steps and transpose the matrix, making the columns the bins and the samples the rows.
 ```{r aml_plots, eval=T,echo=T}

doc/additonal_examples.html

@@ -389,7 +389,8 @@ <h4 class="date">7/9/2019</h4>
 print(target_files_aml)</code></pre>
 <pre class="r"><code>sample_aggregated_segvals_aml&lt;-formSampleMatrixFromRawGDCData(tcga_files = target_files_aml,format = &quot;TARGET&quot;)
 saveRDS(sample_aggregated_segvals_aml,&quot;aml_sample_matched_input_matrix.rds&quot;)</code></pre>
-<p>Now that we’ve created and saved the AML matrix, let’s visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene(. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations(reference: <a href="http://www.bloodjournal.org/blood/article/130/Supplement%201/3800/71708/Recurrent-Copy-Number-Variants-Are-Highly?searchresult=1" class="uri">http://www.bloodjournal.org/blood/article/130/Supplement%201/3800/71708/Recurrent-Copy-Number-Variants-Are-Highly?searchresult=1</a>).</p>
+<p>Now that we’ve created and saved the AML matrix, let’s visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations.</p>
+<p>Reference: Tarlock et al.; Recurrent Copy Number Variants Are Highly Prevalent in Acute Myeloid Leukemia. Blood 2017; 130 (Supplement 1): 3800.).</p>
 <p>Some of the bins are invariant and correlation requires that the standard deviation be a value other than zero (otherwise correlation cannot be calculated). We will remove them in a couple steps and transpose the matrix, making the columns the bins and the samples the rows.</p>
 <pre class="r"><code>sample_aggregated_segvals_aml&lt;-readRDS(&quot;aml_sample_matched_input_matrix.rds&quot;)
 invariant_bins&lt;-which((sample_aggregated_segvals_aml[stringr::str_detect(rownames(sample_aggregated_segvals_aml),&quot;chr7&quot;),] %&gt;% t() %&gt;% as.data.frame() %&gt;% sapply(sd))==0)

vignettes/additonal_examples.Rmd

@@ -53,7 +53,10 @@ sample_aggregated_segvals_aml<-formSampleMatrixFromRawGDCData(tcga_files = targe
 saveRDS(sample_aggregated_segvals_aml,"aml_sample_matched_input_matrix.rds")
 ```
 
-Now that we've created and saved the AML matrix, let's visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene(. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations(reference: http://www.bloodjournal.org/blood/article/130/Supplement%201/3800/71708/Recurrent-Copy-Number-Variants-Are-Highly?searchresult=1).
+Now that we've created and saved the AML matrix, let's visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations.
+
+Reference: Tarlock et al.; Recurrent Copy Number Variants Are Highly Prevalent
+in Acute Myeloid Leukemia. Blood 2017; 130 (Supplement 1): 3800.).
 
 Some of the bins are invariant and correlation requires that the standard deviation be a value other than zero (otherwise correlation cannot be calculated). We will remove them in a couple steps and transpose the matrix, making the columns the bins and the samples the rows.
 ```{r aml_plots, eval=T,echo=T}

vignettes/additonal_examples.html

@@ -389,7 +389,8 @@ <h4 class="date">7/9/2019</h4>
 print(target_files_aml)</code></pre>
 <pre class="r"><code>sample_aggregated_segvals_aml&lt;-formSampleMatrixFromRawGDCData(tcga_files = target_files_aml,format = &quot;TARGET&quot;)
 saveRDS(sample_aggregated_segvals_aml,&quot;aml_sample_matched_input_matrix.rds&quot;)</code></pre>
-<p>Now that we’ve created and saved the AML matrix, let’s visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene(. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations(reference: <a href="http://www.bloodjournal.org/blood/article/130/Supplement%201/3800/71708/Recurrent-Copy-Number-Variants-Are-Highly?searchresult=1" class="uri">http://www.bloodjournal.org/blood/article/130/Supplement%201/3800/71708/Recurrent-Copy-Number-Variants-Are-Highly?searchresult=1</a>).</p>
+<p>Now that we’ve created and saved the AML matrix, let’s visualize it with a quick correlation map of a single chromosome, chromosome 7, the location of the BRAF gene and nearby EZH2 gene. The BRAF gene (chr7:140419127-140624564) is a locus for recurrent copy number aberrations.</p>
+<p>Reference: Tarlock et al.; Recurrent Copy Number Variants Are Highly Prevalent in Acute Myeloid Leukemia. Blood 2017; 130 (Supplement 1): 3800.).</p>
 <p>Some of the bins are invariant and correlation requires that the standard deviation be a value other than zero (otherwise correlation cannot be calculated). We will remove them in a couple steps and transpose the matrix, making the columns the bins and the samples the rows.</p>
 <pre class="r"><code>sample_aggregated_segvals_aml&lt;-readRDS(&quot;aml_sample_matched_input_matrix.rds&quot;)
 invariant_bins&lt;-which((sample_aggregated_segvals_aml[stringr::str_detect(rownames(sample_aggregated_segvals_aml),&quot;chr7&quot;),] %&gt;% t() %&gt;% as.data.frame() %&gt;% sapply(sd))==0)