8 Merging CopyKit Objects

CopyKit objects can be merged into one object with the cbind() function.

Example:

# gain of chromosome 7 and deletion of chromosome 10
ck1 <- mock_bincounts(
  ncells = 50,
  ncells_diploid = 0,
  position_gain = 4900:5493,
  position_del = 6523:7056,
  genome = "hg38",
  resolution = "220kb"
)

## Running variance stabilization transformation: ft

## Smoothing outlier bins.

## Running segmentation algorithm: CBS for genome hg38

## Merging levels.

## Done.

# adding an identifier to colData
colData(ck1)$info <- 'object1'

# gain of chromosome 7 and deletion of chromosome 10
# additional gain of chromosome 1
ck2 <- mock_bincounts(
  ncells = 50,
  ncells_diploid = 0,
  position_gain = c(1:906, 4900:5493),
  position_del = 6523:7056,
  genome = "hg38",
  resolution = "220kb"
)

## Running variance stabilization transformation: ft

## Smoothing outlier bins.

## Running segmentation algorithm: CBS for genome hg38

## Merging levels.

## Done.

# adding an identifier to colData
colData(ck2)$info <- 'object2'

# merging objects
merged_copykit <- cbind(ck1, ck2)

Following merge a standard CopyKit analysis can be applied

# UMAP and clustering
merged_copykit <- runUmap(merged_copykit)

## Using assay: logr

## Embedding data with UMAP. Using seed 17

## Warning in .check_reddim_names(x, value, withDimnames): non-NULL 'rownames(value)' should be the same as
## 'colnames(x)' for 'reducedDim<-'. This will be an
## error in the next release of Bioconductor.

## Access reduced dimensions slot with: reducedDim(scCNA, 'umap').

## Done.

merged_copykit <- findSuggestedK(merged_copykit)

## Calculating jaccard similarity for k range: 10

## 

## Suggested k = 10 with median jaccard similarity of: 1

merged_copykit <- findClusters(merged_copykit)

## Using suggested k_subclones = 10

## Finding clusters, using method: hdbscan

## Found 2 subclones.

## Done.

plotUmap(merged_copykit, label = 'subclones')

## Plotting Umap.

## Coloring by: subclones.

# plotting
plotHeatmap(merged_copykit, label = 'info', order_cells = 'hclust')

## Ordering cells by:hclust

## No distance matrix detected in the scCNA object.

## Calculating distance matrix with metric: euclidean

## Using 1 cores.

## Access distance matrix with copykit::distMat()

## Done.

## Plotting Heatmap.