Copy Number Variations (CNV)

1. Prerequisites

None.

2. Introduction

InferCNV is used to identify large-scale chromosomal copy number alterations in single cell RNA-Seq data including gains or deletions of chromosomes or large segments of such. Several publications have leveraged this technique. (Patel et al., 2014, Tirosh et al., 2016, Venteicher et al., 2017 )

Note: We are currently encountering problems with the function infercnv:::plot_cnv(). It does not prevent the copy number variation analysis. Sometimes, however, the heatmap is not plotted. Checkout your R console for hints in how far the function did not work properly. We are working on a solution for that. If you encounter warnings raised from runCnvAnalysis() let us know.

# load package
library(tidyverse)
library(SPATA2)
library(SPATAData)

object_t269 <- downloadSpataObject(sample_name = "269_T")

# load histolgoy segmentations
data("spatial_segmentations")

object_t269 <- 
  addFeatures(
    object = object_t269, 
    feature_df = spatial_segmentations[["269_T"]], 
    overwrite = TRUE
  )

# only histology
plotSurface(object, = object_t269, pt_alpha = 0)

# histological grouping
plotSurface(object = object_t269, color_by = "histology")
Fig.1 The example sample raw and colored by histological classification.Fig.1 The example sample raw and colored by histological classification.

Fig.1 The example sample raw and colored by histological classification.

SPATA2 implements the package infercnv published and maintained by Broadinstitute and allows to integrate this technique in your workflow of analyzing spatial trancsriptomic data derived from malignancies.

3. Running CNV-Analysis

As with all other functions prefixed with run*() the function runCnvAnalysis() is a wrapper around all necessary functions needed to conduct copy-number-variation-analysis. The results needed for subsequent analysis steps are stored in the specified spata2 object (slot: @cnv). The infercnv-object is stored in the folder specified in the argument directory_cnv_folder.

object_t269 <-
  runCnvAnalysis(
    object = object_t269,
    directory_cnv_folder = "data-gbm269/cnv-results", # example directory
    cnv_prefix = "Chr"
    )

If your desired set up deviates from the default you can reach any function of the inferncnv pipeline by entering it’s name as an argument and specify it’s input as a list of arguments with which you want it to be called.

Copy number variation analysis requires reference data. This includes a count matrix from healthy tissue, an annotation file as well as a data.frame that contains information about the chromosome positions. We provide reference data in the list SPATA2::cnv_ref.

names(cnv_ref)
## [1] "annotation" "mtr"        "regions"
summary(cnv_ref)
##            Length   Class      Mode   
## annotation        1 data.frame list   
## mtr        71067022 -none-     numeric
## regions           4 data.frame list

runCnvAnalysis() defaults to the content of this list. The documentation of runCnvAnalysis() contains a detailed description of the requirements each reference input must meet in order for the function to work.

4. CNV-Results

The results are stored in a list inside the spata2 object. This list can be obtained via getCnvResults().

cnv_results <- getCnvResults(object_t269)

names(cnv_results)
## [1] "prefix"      "cnv_df"      "cnv_mtr"     "gene_pos_df" "regions_df"

5. Visualization

5.1 Heatmap

The gains and losses of chromosomal segments can be displayed via plotCnvHeatmap().

plotCnvHeatmap(object = object_t269)
Fig.2 Basic CNV-Heatmap.

Fig.2 Basic CNV-Heatmap.

If you want to visualize the results across certain groups to identify subclones make use of the across-argument.

plotCnvHeatmap(object = object_t269, across = "histology", clrp = "npg")
Fig.3 CNV-Heatmap with meta data displayes chromosomal alterations across histological classification.

Fig.3 CNV-Heatmap with meta data displayes chromosomal alterations across histological classification.

5.2 Lineplot

An additional option to visualize the results provides plotCnvLineplot().

plotCnvLineplot(
  object = object_t269,
  across = "histology", 
  n_bins_bcsp = 1000,
  n_bins_genes = 1000,
  nrow = 3
)
Fig.4 CNV-Lineplot displays chromosomal changes across histological classification.

Fig.4 CNV-Lineplot displays chromosomal changes across histological classification.

5.3 Surface

The numeric values by which the copy number variations of each chromosome are represented are immediately transferred to the spata2 object’s feature data and are thus accessible for all functions that work with numeric variables. The character string specified in argument cnv_prefix combined with the chromosomes number determines the name by which you can refer to these feature variables.

# cnv feature names
getCnvFeatureNames(object = object_t269)
##  [1] "Chr1"  "Chr2"  "Chr3"  "Chr4"  "Chr5"  "Chr6"  "Chr7"  "Chr8"  "Chr9" 
## [10] "Chr10" "Chr11" "Chr12" "Chr13" "Chr14" "Chr15" "Chr16" "Chr17" "Chr18"
## [19] "Chr19" "Chr20" "Chr21" "Chr22"
# are part of all feature names
getFeatureNames(object = object_t269)
##            numeric            integer             factor            numeric 
##   "nCount_Spatial" "nFeature_Spatial"  "seurat_clusters"             "Chr0" 
##            numeric            numeric            numeric            numeric 
##             "Chr1"            "Chr10"            "Chr11"            "Chr12" 
##            numeric            numeric            numeric            numeric 
##            "Chr13"            "Chr14"            "Chr15"            "Chr16" 
##            numeric            numeric            numeric            numeric 
##            "Chr17"            "Chr18"            "Chr19"             "Chr2" 
##            numeric            numeric            numeric            numeric 
##            "Chr20"            "Chr21"            "Chr22"            "Chr23" 
##            numeric            numeric            numeric            numeric 
##             "Chr3"             "Chr4"             "Chr5"             "Chr6" 
##            numeric            numeric            numeric             factor 
##             "Chr7"             "Chr8"             "Chr9"        "histology"

Use plotSurface() to visualize chromosomal alterations directly on the histology.

plotSurface(
  object = object_t269, 
  color_by = "Chr7", 
  pt_clrsp = "Reds"
)

plotSurface(
  object = object_t269, 
  color_by = "Chr10", 
  pt_clrsp = "Oslo" 
)
Fig.5 Surface plots display chromosomal alterations directory on the histology.Fig.5 Surface plots display chromosomal alterations directory on the histology.

Fig.5 Surface plots display chromosomal alterations directory on the histology.

5.4 Gradients

As showcased in our corresponding vignette about spatial trajectories gradients of numeric variables can be displayed. Chromosomal alterations are numeric variables.

object_t269 <- 
    setTrajectory(
      object = object_t269, 
      trajectory = spatial_trajectories[["269_T"]][["horizontal_mid"]], 
      overwrite = TRUE
    )

plotSpatialTrajectories(
  object = object_t269, 
  ids = "horizontal_mid", 
  color_by = "Chr7", 
  pt_clrsp = "Reds 3"
)

plotSpatialTrajectories(
  object = object_t269, 
  ids = "horizontal_mid", 
  color_by = "Chr10", 
  pt_clrsp = "Oslo"
)

plotTrajectoryLineplot(
  object = object_t269, 
  id = "horizontal_mid", 
  variables = "Chr7", 
  line_color = "red"
)

plotTrajectoryLineplot(
  object = object_t269, 
  id = "horizontal_mid", 
  variables = "Chr10", 
  line_color = "blue", 
  x_nth = 4
)
Fig.6 Gradients of Chromosomal alterations along a trajectory in space.Fig.6 Gradients of Chromosomal alterations along a trajectory in space.Fig.6 Gradients of Chromosomal alterations along a trajectory in space.Fig.6 Gradients of Chromosomal alterations along a trajectory in space.

Fig.6 Gradients of Chromosomal alterations along a trajectory in space.

(Note that trajectory lineplots always rescale variables to 0-1 or to low-high.)