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Package
March 2, 2019
Type Package
Title Identify large-scale CNV events from single cell or bulk RNA-Seq data
Version 0.1.0
Author Akdes Serin Harmanci, Arif O. Harmanci
Maintainer Akdes Serin Harmanci 
Description Identification, visualization and integrative analysis of CNV events in multiscale resolution using single-cell or bulk RNA sequencing data
Encoding UTF-8
LazyData true
LinkingTo Rcpp
Depends Rcpp, signal, pheatmap, RColorBrewer, HMMcopy, IRanges, grid, GenomeGraphs, ggplot2, reshape, mclust, ggpubr, scales, gridExtra, igraph, intergraph, ggnetwork, philentropy, ape, biomaRt, limma, GO.db, org.Hs.eg.db, GOstats
RoxygenNote 6.1.1
Suggests knitr,
rmarkdown
VignetteBuilder knitr

R topics documented:
CaSpER-package . . . . . . . . . .
assignStates . . . . . . . . . . . . .
AverageReference . . . . . . . . . .
calcROC . . . . . . . . . . . . . . .
calculateLOHShiftsForEachSegment
casper . . . . . . . . . . . . . . . .
CenterSmooth . . . . . . . . . . . .
ControlNormalize . . . . . . . . . .
CreateCasperObject . . . . . . . . .
extractEvents . . . . . . . . . . . .
extractLargeScaleEvents . . . . . .
extractMUAndCooccurence . . . .
extractSegmentSummary . . . . . .
gene.matrix . . . . . . . . . . . . .
generateAnnotation . . . . . . . . .
generateEnrichmentSummary . . . .

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2

CaSpER-package
generateLargeScaleEvents . . . . . . . . . .
generateParam . . . . . . . . . . . . . . . . .
getDiffExprGenes . . . . . . . . . . . . . . .
goEnrichmentBP . . . . . . . . . . . . . . .
lohCallMedianFilter . . . . . . . . . . . . . .
lohCallMedianFilterByChr . . . . . . . . . .
mergeScalesAndGenerateFinalEventSummary
PerformMedianFilter . . . . . . . . . . . . .
PerformMedianFilterByChr . . . . . . . . . .
PerformSegmentationWithHMM . . . . . . .
plotBAFAllSamples . . . . . . . . . . . . . .
plotBAFInSeperatePages . . . . . . . . . . .
plotBAFOneSample . . . . . . . . . . . . . .
plotGEAllSamples . . . . . . . . . . . . . .
plotGEAndBAFOneSample . . . . . . . . . .
plotGEAndGT . . . . . . . . . . . . . . . . .
plotHeatmap . . . . . . . . . . . . . . . . . .
plotLargeScaleEvent . . . . . . . . . . . . .
plotLargeScaleEvent2 . . . . . . . . . . . . .
plotMUAndCooccurence . . . . . . . . . . .
plotSCellCNVTree . . . . . . . . . . . . . .
plotSingleCellLargeScaleEventHeatmap . . .
ProcessData . . . . . . . . . . . . . . . . . .
readBAFExtractOutput . . . . . . . . . . . .
runCaSpER . . . . . . . . . . . . . . . . . .
splitByOverlap . . . . . . . . . . . . . . . .

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Index

CaSpER-package

11
12
12
13
13
14
14
15
15
16
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17
18
18
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24

CaSpER: Identify large-scale CNV events from single cell or bulk
RNA-Seq data

Description
Identification, visualization and integrative analysis of CNV events in multiscale resolution using
single-cell or bulk RNA sequencing data

Details
The main functions you will need to use are CreateCasperObject() and runCaSpER(casper_object).
For additional details on running the analysis step by step, please refer to the example vignette.

assignStates

3

assignStates()

assignStates

Description
calculates baf shift threshold using gaussian mixture models and assigns deletion or amplification
to a segment when the HMM state is 1 or 5 without looking at the BAF signal. When the segment
state is 2 or 4, an accompanying BAF shift on the segment is required.
Usage
assignStates(object)
Arguments
object

casper object

Value
object

AverageReference

AverageReference()

Description
the mean the expression level for each gene across all the reference cells (samples) are computed.
Usage
AverageReference(data, ref_ids)
Arguments
object
Value
object

casper object

4

calculateLOHShiftsForEachSegment

calcROC

calcROC()

Description
Calculates tpr and fpr values using genotyping array as gold standard
Usage
calcROC(chrMat, chrMat2)
Arguments
chrMat

large scale event matrix generated using CaSpER

chrMat2

large scale event matrix generated using genotyping array

Value
accuracy measures

calculateLOHShiftsForEachSegment
calculateLOHShiftsForEachSegment()

Description
calculate the median value of the BAF shift signal on the segments
Usage
calculateLOHShiftsForEachSegment(object)
Arguments
object
Value
object

casper object

casper

casper

5

The CaSpER Class

Description
The CaSpER Class The casper object is required for performing CNV analysis on single-cell and
bulk RNA-Seq. It stores all information associated with the dataset, including data, smoothed data,
baf values, annotations, scale specific segments, scale specific large scale events etc.
Slots
raw.data raw project data
data lowly expressed genes are filtered from the data
loh original baf signal
median.filtered.data median filtered expression signal
loh.median.filtered.data median filtered baf signal
centered.data gene expression levels are centered around the mid-point. For each gene, the midpoint of expression level is computed among all the cells (or samples in bulk RNA-seq), then
the mid-point expression level is subtracted from the expression levels
center.smoothed.data cell centric expression centering is performed. For each cell (or sample),
we compute the mid-point of the expression level then we subtract the mid-point expression
from the expression levels of all the genes for the corresponding cel
control.normalized control normalization is performed by subtracting reference expression values from the tumor expression values.
control.normalized.visbound control normalized data is thresholded in order to perform better
visualization.
control.normalized.visbound.noiseRemoved noise is removed from control normalized and
thresholded data.
large.scale.cnv.events large scale CNV events identified by CaSpER
segments CNV segments identified by CaSpER

cytoband cytoband information downloaded from UCSC hg19: http://hgdownload.cse.ucsc.edu/goldenpath/hg19/databa
hg38:http://hgdownload.cse.ucsc.edu/goldenpath/hg38/database/cytoBand.txt.gz
annotation positions of each gene along each chromosome in the genome
annotation.filt lowly expressed genes are filtered from gene annotation data.frame
control.sample.ids vector containing the reference (normal) cell (sample) names
project.name project name
genomeVersion genomeVersion: hg19 or hg38
hmmparam initial hmm parameters estimated from data
plotorder cell (sample) ordering for heatmap plots
vis.bound threshold for control normalized data for better visualization
noise.thr noise threshold for better visualization
loh.name.mapping containing the cell (sample) name and the matching baf signal sample name
sequencing.type sequencing type: bulk or single-cell

6

ControlNormalize
cnv.scale maximum expression scale
loh.scale maximum baf scale
loh.shift.thr baf shift threshold estimated from baf signal using gaussian mixture models
window.length window length used for median filtering
length.iterations increase in window length at each scale iteration

CenterSmooth()

CenterSmooth

Description
Cell centric expression centering is performed. For each cell (or sample), we compute the mid-point
of the expression level then we subtract the mid-point expression from the expression levels of all
the genes for the corresponding cell
Usage
CenterSmooth(object)
Arguments
object

casper object

Value
object

ControlNormalize

ControlNormalize()

Description
The control normalization is performed by subtracting reference expression values from the tumor
expression values.
Usage
ControlNormalize(object, vis.bound, noise.thr)
Arguments
object
Value
object

casper object

CreateCasperObject

7

CreateCasperObject

CreateCasperObject

Description
Creation of a casper object.
Usage
CreateCasperObject(raw.data, annotation, control.sample.ids, cytoband,
loh.name.mapping, cnv.scale, loh.scale, method, loh,
project = "casperProject", sequencing.type, expr.cutoff = 4.5,
display.progress = TRUE, log.transformed = TRUE,
centered.threshold = 3, window.length = 50, length.iterations = 50,
vis.bound = 2, noise.thr = 0.3, genomeVersion = "hg19", ...)
Arguments
raw.data

the matrix of genes (rows) vs. cells (columns) containing the raw counts

data.frame containing positions of each gene along each chromosome in the
genome
control.sample.ids
vector containing the reference (normal) cell (sample) names
annotation

cytoband information downloaded from UCSC hg19: http://hgdownload.cse.ucsc.edu/goldenpath/hg1
hg38:http://hgdownload.cse.ucsc.edu/goldenpath/hg38/database/cytoBand.txt.gz
loh.name.mapping
contains the cell (sample) name and the matching baf signal sample name
cytoband

cnv.scale

maximum expression scale

loh.scale

maximum baf scale

method

analysis type: itereative or fixed (default: iterative)

loh
The original baf signal
sequencing.type
sequencing.type sequencing type: bulk or single-cell
expr.cutoff
expression cutoff for lowly expressed genes
log.transformed
indicates if the data log2 transformed or not. (default:TRUE)
centered.threshold
window.length window length used for median filtering (default: 50)
length.iterations
increase in window length at each scale iteration (default: 50)
vis.bound

threshold for control normalized data for better visualization (default: 2)

genomeVersion

genomeVersion: hg19 or hg38 (default: hg19)

Value
casper

8

extractLargeScaleEvents

extractEvents

extractEvents()

Description
formats large scale events as a matrix. Rows represent samples (cells) whereas columns represent chromosome arms (1: amplification, 0: neutral, -1: deletion) helper function for generateLargeScaleEvents()
Usage
extractEvents(segments, cytoband, type)
Arguments
cytoband

cytoband information downloaded from UCSC hg19: http://hgdownload.cse.ucsc.edu/goldenpath/hg1
hg38:http://hgdownload.cse.ucsc.edu/goldenpath/hg38/database/cytoBand.txt.gz

type

event type amp (amplification) or del (deletion)/

object

casper object

Value
combined large scale events in data.frame

extractLargeScaleEvents
extractLargeScaleEvents()

Description
generates coherent set of large scale CNV events using the pairwise comparison of all scales from
BAF and expression signals
Usage
extractLargeScaleEvents(final.objects, thr = 0.5)
Arguments
final.objects

casper object

thr

gamma threshold determining the least number of scales required to support

Value
final large scale event summary reported as a matrix

extractMUAndCooccurence

9

extractMUAndCooccurence
extractMUAndCooccurence()

Description
calculates significant mutually exclusive and co-occurent events
Usage
extractMUAndCooccurence(finalChrMat, loh, loh.name.mapping)
Arguments
finalChrMat

large scale event matrix generated using CaSpER

loh

original baf signal

loh.name.mapping
contains the cell (sample) name and the matching baf signal sample name
Value
list of mutually exclusive and co-occurent events

extractSegmentSummary extractSegmentSummary()

Description
generates coherent set of CNV segments using the pairwise comparison of all scales from BAF and
expression signals
Usage
extractSegmentSummary(final.objects)
Arguments
final.objects

list of casper object

Value
list of loss and gain segments identified in all scales

10

generateAnnotation

gene.matrix()

gene.matrix

Description
Gene level CNV events represented as matrix where rows represent samples and columns represent
samples
Usage
gene.matrix(segment, all.genes, all.samples, genes.ann)
Arguments
segment

CNV segments

all.genes

gene names

all.samples

samp names

genes.ann

gene symbols within each segments

Value
matrix of gene level CNV events

generateAnnotation

generateAnnotation()

Description
retrieves gene chromosomal locations from biomart
Usage
generateAnnotation(id_type = "ensembl_gene_id", genes, ishg19,
centromere)
Arguments
id_type

gene list identifier, ensembl_gene_id or hgnc_symbol

genes

list of genes

ishg19

boolean values determining the genome version

centromere

centromer regions

Value
list of mutually exclusive and co-occurent events

generateEnrichmentSummary

generateEnrichmentSummary
generateEnrichmentSummary()

Description
generate GO Term enrichment summary
Usage
generateEnrichmentSummary(results)
Arguments
results

output of getDiffExprGenes() function

Value
significantly enriched GO Terms

generateLargeScaleEvents
generateLargeScaleEvents()

Description
generates large scale CNV events
Usage
generateLargeScaleEvents(object)
Arguments
object
Value
object

casper object

11

12

getDiffExprGenes

generateParam

generateParam()

Description
Initial HMM parameters estimated from the data.
Usage
generateParam(object, cnv.scale = 3)
Arguments
object

casper object

cnv.scale

expression.scale for the expression signal

Value
object

getDiffExprGenes

getDiffExprGenes()

Description
get differentially expressed genes between samples having selected specified CNV events
Usage
getDiffExprGenes(final.objects, sampleName, chrs, event.type)
Arguments
final.objects

list of objects

sampleName

sample name

chrs

selected chromosomes

event.type

cnv event type

Value
differentially expressed genes

goEnrichmentBP

13

goEnrichmentBP()

goEnrichmentBP

Description
GO Term enrichment
Usage
goEnrichmentBP(genes, ontology, universe = character(0), pvalue = 0.05,
annotation = "org.Hs.eg.db", conditionalSearch = TRUE, genes2)
Arguments
genes

list of genes

ontology

ontology (BP, CC or MF)

universe

universe of genes

pvalue

pvalue cutoff

annotation

ontology annotation default:org.Hs.eg.db

Value
significantly enriched GO Terms

lohCallMedianFilter

lohCallMedianFilter()

Description
Reads BAFExtract output files
Usage
lohCallMedianFilter(object, loh.scale, n = 50, scale.iteration = 50)
Arguments
path

path for the folder that contains BAFExtract output files

Value
baf signal in data.frame format

14

mergeScalesAndGenerateFinalEventSummary

lohCallMedianFilterByChr
readBAFExtractOutput()

Description
Reads BAFExtract output files
Usage
lohCallMedianFilterByChr(object, loh.scale, n = 50,
scale.iteration = 50)
Arguments
path

path for the folder that contains BAFExtract output files

Value
baf signal in data.frame format

mergeScalesAndGenerateFinalEventSummary
mergeScalesAndGenerateFinalEventSummary()

Description
helper function for extractLargeScaleEvents()
Usage
mergeScalesAndGenerateFinalEventSummary(final.objects)
Arguments
final.objects
Value
list of objects

list of casper objects

PerformMedianFilter

15

PerformMedianFilter

PerformMedianFilter()

Description
Recusive iterative median filtering is applied to whole genome
Usage
PerformMedianFilter(object, window.length = 50, length.iterations = 50)
Arguments
object

casper object

window.length window length used for median filtering
length.iterations
increase in window length at each scale iteration
Value
object

PerformMedianFilterByChr
PerformMedianFilterByChr()

Description
Recusive iterative median filtering is applied for each chromosome
Usage
PerformMedianFilterByChr(object, window.length = 50,
length.iterations = 50)
Arguments
object

casper object

window.length window length used for median filtering
length.iterations
increase in window length at each scale iteration
Value
object

16

plotBAFAllSamples

PerformSegmentationWithHMM
PerformSegmentationWithHMM()

Description
HMM segmentation applied for each scale of expression signal
Usage
PerformSegmentationWithHMM(object, cnv.scale, removeCentromere = T,
cytoband)
Arguments
object

casper object

cnv.scale
expression signal scale number
removeCentromere
boolean values determining if centromere regions should be removed from the
analysis
cytoband

cytoband information downloaded from UCSC hg19: http://hgdownload.cse.ucsc.edu/goldenpath/hg1
hg38:http://hgdownload.cse.ucsc.edu/goldenpath/hg38/database/cytoBand.txt.gz

Value
object

plotBAFAllSamples

plotBAFAllSamples()

Description
Visualization of BAF shift signal for all samples together
Usage
plotBAFAllSamples(loh, fileName)
Arguments
loh

baf signal, user can either give smoothed baf signal or original baf signal as an
input.

fileName

fileName of the putput image

plotBAFInSeperatePages

17

plotBAFInSeperatePages
plotBAFInSeperatePages()

Description
Visualization of BAF deviation for each sample in separate pages
Usage
plotBAFInSeperatePages(loh, folderName)

Arguments
loh

baf signal, user can either give smoothed baf signal or original baf signal as an
input.

folderName

folder name for the output images

Value
object

plotBAFOneSample

plotBAFOneSample()

Description
Visualization of BAF shift signal in different scales for one sample
Usage
plotBAFOneSample(object, fileName)

Arguments
object

casper object

fileName

fileName of the output image

18

plotGEAndBAFOneSample

plotGEAllSamples

plotGEAllSamples()

Description
plot gene expression signal for each sample seperately
Usage
plotGEAllSamples(object, fileName = fileName, cnv.scale)
Arguments
object

casper object

fileName

fileName of the putput image

cnv.scale

expression.scale for the expression signal

plotGEAndBAFOneSample plotGEAndBAFOneSample()

Description
Gene expression and BAF signal for one sample in one plot
Usage
plotGEAndBAFOneSample(object, cnv.scale, loh.scale, sample, n = 50,
scale.iteration = 50)
Arguments
object

casper object

cnv.scale

expression.scale for the expression signal

sample

sample name

n

window length used for median filtering

length.iterations
increase in window length at each scale iteration

plotGEAndGT

plotGEAndGT

19

plotGEAndGT()

Description
Heatmap plot for large scale event calls identified by CaSpER and genotyping array.
Usage
plotGEAndGT(chrMat, genoMat, fileName)
Arguments
chrMat

large scale events identified from CaSpER represented as matrix. Rows indicates
samples (cells) whereas columns indicates chromosome arms

genoMat

large scale events identified from genotyping array represented as matrix. Rows
indicates samples (cells) whereas columns indicates chromosome arms

fileName

fileName of the putput image

plotHeatmap

plotHeatmap()

Description
Visualization of the genomewide gene expression signal plot at different smoothing scales
Usage
plotHeatmap(object, fileName, cnv.scale = 3, cluster_cols = F,
cluster_rows = T, show_rownames = T, only_soi = T)
Arguments
object

casper object

fileName

fileName of the putput image

cnv.scale

expression.scale for the expression signal

cluster_cols

boolean values determining if columns should be clustered

cluster_rows

boolean values determining if rows should be clustered

show_rownames

boolean values determining if rownames should be plotted

only_soi

boolean values determining if only samples of interest without control samples
should be plotted

20

plotMUAndCooccurence

plotLargeScaleEvent

plotLargeScaleEvent()

Description
Visualization of the large-scale CNV events among all the samples/cells
Usage
plotLargeScaleEvent(object, fileName)
Arguments
object

casper object

fileName

fileName of the output image

plotLargeScaleEvent2

plotLargeScaleEvent2()

Description
Visualization of the large-scale CNV events among all the samples/cells
Usage
plotLargeScaleEvent2(chrMat, fileName)
Arguments
chrMat

large scale events identified from CaSpER represented as matrix. Rows indicates
samples (cells) whereas columns indicates chromosome arms

fileName

fileName of the output image

plotMUAndCooccurence

plotMUAndCooccurence()

Description
Visualization of mutually exclusive and co-occuring events
Usage
plotMUAndCooccurence(results)
Arguments
results

output of extractMUAndCooccurence() function

plotSCellCNVTree

plotSCellCNVTree

21

plotSCellCNVTree()

Description
Pyhlogenetic tree-based clustering and visualization of the cells based on the CNV events from
single cell RNA-seq Data.
Usage
plotSCellCNVTree(finalChrMat, sampleName,
path = "C:\\Users\\aharmanci\\Downloads\\phylip-3.695\\phylip-3.695\\exe",
fileName)
Arguments
finalChrMat

large scale events identified from CaSpER represented as matrix. Rows indicates
samples (cells) whereas columns indicates chromosome arms

sampleName

sample name

path

path to the executable containing fitch. If path = NULL, the R will search
several commonly used directories for the correct executable file. More information about installing PHYLIP can be found on the PHYLIP webpage:
http://evolution.genetics.washington.edu/phylip.html.

plotSingleCellLargeScaleEventHeatmap
plotSingleCellLargeScaleEventHeatmap()

Description
Visualization of large scale event summary for selected samples and chromosomes
Usage
plotSingleCellLargeScaleEventHeatmap(finalChrMat, sampleName, chrs)
Arguments
finalChrMat

large scale events identified from CaSpER represented as matrix. Rows indicates
samples (cells) whereas columns indicates chromosome arms

sampleName

sample name

chrs

chromosome names

Value
object

22

readBAFExtractOutput

ProcessData()

ProcessData

Description
Processing expression signal. Step 1. Recursively iterative median filtering Step 2. Center Normalization Step 3. Control Normalization
Usage
ProcessData(object)

Arguments
object

casper object

Value
object

readBAFExtractOutput

readBAFExtractOutput()

Description
Reads BAFExtract output files
Usage
readBAFExtractOutput(path, sequencing.type = "bulk")

Arguments
path

path for the folder that contains BAFExtract output files

Value
baf signal in data.frame format

runCaSpER

runCaSpER

23

runCaSpER()

Description
Main casper function that performs a pairwise comparison of all scales from BAF and expression
signals to ensure a coherent set of CNV calls.
Usage
runCaSpER(object, removeCentromere = T, cytoband = object@cytoband,
method = "iterative")
Arguments
object
casper object
removeCentromere
boolean values determining if centromere regions should be removed from the
analysis
cytoband

cytoband information downloaded from UCSC hg19: http://hgdownload.cse.ucsc.edu/goldenpath/hg1
hg38:http://hgdownload.cse.ucsc.edu/goldenpath/hg38/database/cytoBand.txt.gz

method

iterative or fixed method. Fixed performs CNV calls on desired baf and expression scale whereas iterative performs pairwise comparison of all expression and
baf scale pairs. Iterative method is recommendend. (default: iterative)

Value
list of objects

splitByOverlap

splitByOverlap()

Description
helper function for segment summary. Acknowledgements to https://support.bioconductor.org/p/67118/
Usage
splitByOverlap(query, subject, column = "ENTREZID", ...)

Index
_PACKAGE (CaSpER-package), 2

plotMUAndCooccurence, 20
plotSCellCNVTree, 21
plotSingleCellLargeScaleEventHeatmap,
21
ProcessData, 22

assignStates, 3
AverageReference, 3
calcROC, 4
calculateLOHShiftsForEachSegment, 4
casper, 5
casper-class (casper), 5
CaSpER-package, 2
CenterSmooth, 6
ControlNormalize, 6
CreateCasperObject, 7

readBAFExtractOutput, 22
runCaSpER, 23
splitByOverlap, 23

extractEvents, 8
extractLargeScaleEvents, 8
extractMUAndCooccurence, 9
extractSegmentSummary, 9
gene.matrix, 10
generateAnnotation, 10
generateEnrichmentSummary, 11
generateLargeScaleEvents, 11
generateParam, 12
getDiffExprGenes, 12
goEnrichmentBP, 13
lohCallMedianFilter, 13
lohCallMedianFilterByChr, 14
mergeScalesAndGenerateFinalEventSummary,
14
PerformMedianFilter, 15
PerformMedianFilterByChr, 15
PerformSegmentationWithHMM, 16
plotBAFAllSamples, 16
plotBAFInSeperatePages, 17
plotBAFOneSample, 17
plotGEAllSamples, 18
plotGEAndBAFOneSample, 18
plotGEAndGT, 19
plotHeatmap, 19
plotLargeScaleEvent, 20
plotLargeScaleEvent2, 20
24



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