Merge pull request #31 from CostaLab/devel

Fixed loading bug after updating to R4.4.

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: sigurd
 Type: Package
 Title: Single cell Genotyping Using RNA Data
-Version: 0.2.51
+Version: 0.2.52
 Authors@R: c(
   person(given = "Martin",
          family = "Grasshoff",
 	 role = c("aut", "cre"),
 	 email = "[email protected]"),
-  person(given = "Ivan Costa",
-         family = "Gesteira",
+  person(given = "Ivan",
+         family = "Costa",
          role = c("aut"),
          email = "[email protected]",
          comment = c(ORCID = "0000-0003-2890-8697")))
@@ -26,31 +26,32 @@ Imports:
     data.table,
     dplyr,
     DropletUtils,
-    fastmatch,
     GenomeInfoDb,
-    ggplot2,
     grid,
     Matrix,
-    MatrixGenerics,
-    magrittr,
     methods,
     parallel,
-    rcompanion,
     S4Vectors,
     Seurat,
     SummarizedExperiment,
     scales,
     tibble,
-    tidyr,
-    tidyverse,
-    VariantAnnotation
-RoxygenNote: 7.2.3
+    VariantAnnotation,
+    vegan
+RoxygenNote: 7.3.2
 Suggests:
     GenomicRanges,
+    fastmatch,
+    ggplot2,
     IRanges,
     knitr,
+    magrittr,
+    MatrixGenerics,
+    rcompanion,
     rmarkdown,
     SeuratObject,
-    testthat (>= 3.0.0)
+    testthat (>= 3.0.0),
+    tidyr,
+    tidyverse
 VignetteBuilder: knitr
 Config/testthat/edition: 3

R/CalculateStrandCorrelation.R

@@ -20,7 +20,8 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){
   dt <- merge(data.table::data.table(summary(reads_A_fw)), 
               data.table::data.table(summary(reads_A_rev)), 
               by.x = c("i", "j"), by.y = c("i", "j"), 
-              all = TRUE)[x.x >0 | x.y >0]
+              all = TRUE)
+  dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,]
   dt <- data.table::data.table(variant = variants_A[dt[[1]]],
                                cell_id = dt[[2]],
 		                           fw = dt[[3]], rev = dt[[4]])
@@ -34,14 +35,11 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){
   rownames(reads_C_rev) <- paste0(chromosome_prefix, "_", 1:nrow(reads_C_rev), "_", ref_allele, "_C")
   reads_C_fw  <- reads_C_fw[ref_allele != "C",]
   reads_C_rev <- reads_C_rev[ref_allele != "C",]
-#  dt <- merge(data.table::data.table(summary(reads_C_fw)),
-#              data.table::data.table(summary(reads_C_rev)),
-#              by.x = c("i", "j"), by.y = c("i", "j"),
-#              all = TRUE)[x.x > 0 | x.y > 0]
   dt <- merge(data.table::data.table(summary(reads_C_fw)),
               data.table::data.table(summary(reads_C_rev)),
               by.x = c("i", "j"), by.y = c("i", "j"),
-              all = TRUE)[!!dplyr::sym("x.x") > 0 | !!dplyr::sym("x.y") > 0]
+              all = TRUE)
+  dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,]
   dt <- data.table::data.table(variant = variants_C[dt[[1]]],
                                cell_id = dt[[2]],
                                fw = dt[[3]], rev = dt[[4]])
@@ -58,7 +56,8 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){
   dt <- merge(data.table::data.table(summary(reads_G_fw)),
               data.table::data.table(summary(reads_G_rev)),
               by.x = c("i", "j"), by.y = c("i", "j"),
-              all = TRUE)[x.x >0 | x.y >0]
+              all = TRUE)
+  dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,]
   dt <- data.table::data.table(variant = variants_G[dt[[1]]],
                                          cell_id = dt[[2]],
                                          fw = dt[[3]], rev = dt[[4]])
@@ -75,7 +74,8 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){
   dt <- merge(data.table::data.table(summary(reads_T_fw)),
               data.table::data.table(summary(reads_T_rev)),
               by.x = c("i", "j"), by.y = c("i", "j"),
-              all = TRUE)[x.x >0 | x.y >0]
+              all = TRUE)
+  dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,]
   dt <- data.table::data.table(variant = variants_T[dt[[1]]],
                                cell_id = dt[[2]],
                                fw = dt[[3]], rev = dt[[4]])

R/LoadingMAEGATK_typewise.R

@@ -150,11 +150,6 @@ LoadingMAEGATK_typewise <- function(samples_file, samples_path = NULL, patient,
     coverage_depth_per_cell           <- gsub("_._.$", "", coverage_depth_per_cell)
     coverage_depth_per_cell           <- !duplicated(coverage_depth_per_cell)
     coverage_depth_per_cell           <- coverage[coverage_depth_per_cell,]
-    cell_ids                          <- colnames(coverage_depth_per_cell)
-    variant_names                     <- rownames(coverage_depth_per_cell)
-    coverage_depth_per_cell           <- suppressWarnings(matrix(coverage, nrow = length(variant_names), ncol = length(cell_ids)))
-    colnames(coverage_depth_per_cell) <- cell_ids
-    rownames(coverage_depth_per_cell) <- variant_names
     coverage_depth_per_variant        <- rowMeans(coverage)
     coverage_depth_per_cell           <- colMeans(coverage_depth_per_cell)
     meta_data_col                     <- data.frame(Cell = colnames(consensus), Patient = patient, Sample = substr(x = colnames(consensus), start = 1, stop = nchar(colnames(consensus))-(cellbarcode_length+1)), AverageCoverage = coverage_depth_per_cell)

README.md

@@ -15,13 +15,14 @@ We employ a imputation approach that utilizes the correlation between mitochondr
 You can install sigurd using the following code. The vignette requires data that is currently not published, but is provided as a reference.
 
 ```{r}
+
 install.packages("devtools")
 devtools::install_github("https://github.com/CostaLab/sigurd.git", build_vignettes = FALSE)
 require(sigurd)
 
 ```
 
-# SiGURD 
+# SIGURD 
 
 We have provided a small example data set for SiGURD. It consists of chromosome 9 and MT for one MPN sample.
 The mutation data was obtained from the Sanger Institute Catalogue Of Somatic Mutations In Cancer web site, http://cancer.sanger.ac.uk/cosmic Bamford et al (2004) The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer, 91,355-358.
@@ -32,7 +33,7 @@ The mutation data was obtained from the Sanger Institute Catalogue Of Somatic Mu
 
 ```
 
-# Current Features v0.2.51
+# Current Features v0.2.52
 
 - Loading data from VarTrix and MAEGATK.
 - Transforming the data to be compatible for joint analysis.