Merge pull request #54 from broadinstitute/devel

Devel

Former-commit-id: b7c3cf5
Former-commit-id: e2177ca

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: infercnv
 Type: Package
 Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data
-Version: 0.8
+Version: 0.8.1
 Date: 2017-05-25
 Authors@R: c( person("Timothy", "Tickle", email = "[email protected]", role = c("aut", "cre")), person("Itay", "Tirosh", email = "[email protected]", role = "aut"), person("Christophe", "Georgescu", email = "[email protected]", role = "aut"), person("Maxwell", "Brown", email = "[email protected]", role = "aut"), person("Brian", "Haas", email = "[email protected]", role = "aut")) 
 Author: Timothy Tickle [aut, cre], Itay Tirosh [aut], Christophe Georgescu [aut], Maxwell Brown [aut], Brian Haas [aut]

R/inferCNV.R

@@ -377,3 +377,23 @@ validate_infercnv_obj <- function(infercnv_obj) {
 
 }
 
+
+get_cell_name_by_grouping <- function(infercnv_obj) {
+
+ cell_name_groupings = list()
+
+ groupings = c(infercnv_obj@reference_grouped_cell_indices, infercnv_obj@observation_grouped_cell_indices)
+
+ for (group_name in names(groupings)) {
+
+ cell_names = colnames(infercnv_obj@expr.data[, groupings[[ group_name ]] ] )
+
+ cell_name_groupings[[ group_name ]] = cell_names
+
+ }
+
+ return(cell_name_groupings)
+}
+
+
+

R/inferCNV_heatmap.R

@@ -145,7 +145,8 @@ plot_cnv <- function(infercnv_obj,
 
  # Row separation based on reference
  ref_idx <- unlist(infercnv_obj@reference_grouped_cell_indices)
-
+ ref_idx = ref_idx[order(ref_idx)]
+
  # Column seperation by contig and label axes with only one instance of name
  contig_tbl <- table(contigs)[unique_contigs]
  col_sep <- cumsum(contig_tbl)
@@ -176,13 +177,14 @@ plot_cnv <- function(infercnv_obj,
  counter <- counter + 1
  }
  # restrict to just the obs indices
- obs_annotations_groups <- obs_annotations_groups[ unlist(obs_index_groupings) ]
-
 
+ obs_annotations_groups <- obs_annotations_groups[-ref_idx]
+
  grouping_key_coln[1] <- floor(123/(max(nchar(obs_annotations_names)) + 4)) ## 123 is the max width in number of characters, 4 is the space taken by the color box itself and the spacing around it
  if (grouping_key_coln[1] < 1) {
  grouping_key_coln[1] <- 1
  }
+
  name_ref_groups = names(infercnv_obj@reference_grouped_cell_indices)
  grouping_key_coln[2] <- floor(123/(max(nchar(name_ref_groups)) + 4)) ## 123 is the max width in number of characters, 4 is the space taken by the color box itself and the spacing around it
  if (grouping_key_coln[2] < 1) {
@@ -195,7 +197,7 @@ plot_cnv <- function(infercnv_obj,
  # Calculate how much bigger the output needs to be to accodomate for the grouping key
  grouping_key_height <- c((grouping_key_rown[2] + 2) * 0.175, (grouping_key_rown[1] + 3) * 0.175)
 
-  # Rows observations, Columns CHR
+ # Rows observations, Columns CHR
  if (! is.na(output_format)) {
  if (output_format == "pdf") {
  pdf(paste(out_dir, paste(output_filename, ".pdf", sep=""), sep="/"),
@@ -219,7 +221,7 @@ plot_cnv <- function(infercnv_obj,
  ## They are more informative anyway
  obs_data <- infercnv_obj@expr.data
  if (!is.null(ref_idx)){
- obs_data <- plot_data[, -1 * ref_idx, drop=FALSE]
+ obs_data <- plot_data[, -ref_idx, drop=FALSE]
  if (ncol(obs_data) == 1) {
  # hack for dealing with single entries
  plot_data <- cbind(obs_data, obs_data)
@@ -244,7 +246,7 @@ plot_cnv <- function(infercnv_obj,
  current_grp_idx <- current_grp_idx + 1
  }
  ref_groups <- updated_ref_groups
-
+ 
  nb_breaks <- 16
  breaksList_t <-
  seq(min(min(obs_data_t, na.rm=TRUE), min(ref_data_t, na.rm=TRUE)),

example/example.Rmd

@@ -292,8 +292,10 @@ plot_cnv(infercnv_obj,
 
 ```{r}
 knitr::include_graphics("infercnv.finalized_view.png")
+```
+
 And that's it. You can experiment with each step to fine-tune your data exploration. See the documentation for uploading the resulting data matrix into the Next Generation Clustered Heatmap Viewer for more interactive exploration of the infercnv-processed data:
 <https://github.com/broadinstitute/inferCNV/wiki/Next-Generation-Clustered-Heat-Map>
 
 
-```
+

scripts/inferCNV_utils.R

@@ -1,9 +1,9 @@
 
 library(tidyverse)
-
+library(futile.logger)
 
 # plot expression density by chromosome for each observation group, reference groups are shown as single 'normal' group.
-plot_density_by_chr <- function(infercnv_obj, pdf_filename=NULL, exclude_range=NULL, chrs=NULL) {
+plot_density_by_chr <- function(infercnv_obj, pdf_filename=NULL, exclude_range=NULL, include_range = NULL, chrs=NULL) {
 
  ref_group_cell_indices = infercnv:::get_reference_grouped_cell_indices(infercnv_obj)
 
@@ -15,6 +15,9 @@ plot_density_by_chr <- function(infercnv_obj, pdf_filename=NULL, exclude_range=N
  if (! is.null(pdf_filename)) {
  pdf(pdf_filename)
  }
+
+
+ chr_expr_vals = list()
 
  for (chr in chrs) {
 
@@ -40,16 +43,25 @@ plot_density_by_chr <- function(infercnv_obj, pdf_filename=NULL, exclude_range=N
  excl_range_right = exclude_range[2]
 
  df = df %>% filter(vals < excl_range_left | vals > excl_range_right)
+ } else if (! is.null(include_range)) {
+ include_range_left = include_range[1]
+ include_range_right = include_range[2]
+
+ df = df %>% filter(vals >= include_range_left & vals <= include_range_right)
  }
-
+  
  p = df %>% ggplot(aes(vals, fill=class)) + geom_density(alpha=0.3) + scale_y_continuous(trans='log10', limits=c(1,NA)) + ggtitle(chr)
  plot(p)
+
+ chr_expr_vals[[ chr ]] = df
 
  }
 
  if (! is.null(pdf_filename)) {
  dev.off()
  }
+
+ return(chr_expr_vals)
 
 }
 
@@ -87,6 +99,8 @@ plot_dist_counts_expr_genes_by_chr <- function(infercnv_obj, pdf_filename=NULL,
  if (! is.null(pdf_filename)) {
  pdf(pdf_filename)
  }
+
+ gene_counts_dfs = list()
 
  for (chr in chrs) {
  gene_idx = which(infercnv_obj@gene_order$chr == chr)
@@ -104,11 +118,15 @@ plot_dist_counts_expr_genes_by_chr <- function(infercnv_obj, pdf_filename=NULL,
  }
  p = df %>% ggplot(aes(gene_counts, fill=class)) + geom_density(alpha=0.3) + ggtitle(chr)
  plot(p)
+
+ gene_counts_dfs[[ chr ]] = df
  }
 
  if (! is.null(pdf_filename)) {
  dev.off()
  }
+
+ return(gene_counts_dfs)
 }
 
 
@@ -133,7 +151,9 @@ compare_gene_expr_means_by_group_pair <- function(infercnv_obj, groupA, groupB,
  groupA.gene_mean = rowMeans(groupA.expr.data)
  groupB.gene_mean = rowMeans(groupB.expr.data)
 
- plot(groupA.gene_mean, groupB.gene_mean)
+ #plot(groupA.gene_mean, groupB.gene_mean)
+ smoothScatter(groupA.gene_mean, groupB.gene_mean)
+ abline(a=0, b=1, col='magenta')
 
  df=data.frame(groupA=groupA.gene_mean, groupB=groupB.gene_mean)