Copy number analysis
Anand Mayakonda
2023-04-25
Note: This vignette was not evaluated.
1 Introduction
maftools provides a set of functions to facilitate copy number analysis using ASCAT for tumor-normal or tumor-only WGS datasets. Although there exists ascatNgs, it requires the installation of Perl and C modules to fetch the read counts across the markers. maftools bypass these requirements entirely within R with the C code baked in. However, maftools only generates the required read counts, BAF, and logR files. Downstream analyses have to be done with ASCAT.
ASCAT is not available on CRAN or Bioconductor and needs to be installed from GitHub
If you use maftools functions for CNV analysis, please cite the ASCAT publication
| Van Loo P, Nordgard SH, Lingjærde OC, et al. Allele-specific copy number analysis of tumors. Proc Natl Acad Sci U S A. 2010;107(39):16910-16915. doi:10.1073/pnas.1009843107 |
2 Step-1: Get nucleotide counts for genetic markers
Below command will generate two tsv files tumor_nucleotide_counts.tsv and normal_nucleotide_counts.tsv that can be used for downstream analysis. Note that the function will process ~900K SNPs from Affymetrix Genome-Wide Human SNP 6.0 Array. The process can be sped up linearly by increasing nthreads which will launch each chromosome on a separate thread. Currently hg19 and hg38 are supported.
3 Step-2: Prepare input files for ASCAT with prepAscat()
3.1 Tumor-Normal pair
Below command takes tumor_nucleotide_counts.tsv and normal_nucleotide_counts.tsv files, filter SNPs with low coverage (default <15), estimate BAF, logR, and generates the input files for ASCAT.
library(ASCAT)
ascat.bc = maftools::prepAscat(t_counts = "tumor_nucleotide_counts.tsv",
n_counts = "normal_nucleotide_counts.tsv",
sample_name = "tumor")
# Library sizes:
# Tumor: 1830168947
# Normal: 1321201848
# Library size difference: 1.385
# ------
# Counts file: tumor_nucleotide_counts.tsv
# Markers: 932148
# Removed 2982 duplicated loci
# Markers > 15: 928607
# ------
# Counts file: normal_nucleotide_counts.tsv
# Markers: 932148
# Removed 2982 duplicated loci
# Markers > 15: 928311
# ------
# Final number SNPs: 928107
# Generated following files:
# tumor_nucleotide_counts.tumour.BAF.txt
# tumor_nucleotide_counts.tumour.logR.txt
# tumor_nucleotide_counts.normal.BAF.txt
# tumor_nucleotide_counts.normal.logR.txt
# ------Generated BAF and logR files can be processed with ASCAT functions. The below code chunk shows minimal usage with ASCAT. See here for further workflow examples.
ascat.bc = ASCAT::ascat.loadData(
Tumor_LogR_file = "tumor_nucleotide_counts.tumour.logR.txt",
Tumor_BAF_file = "tumor_nucleotide_counts.tumour.BAF.txt",
Germline_LogR_file = "tumor_nucleotide_counts.normal.logR.txt",
Germline_BAF_file = "tumor_nucleotide_counts.normal.BAF.txt",
chrs = c(1:22, "X", "Y"),
sexchromosomes = c("X", "Y")
)
ASCAT::ascat.plotRawData(ASCATobj = ascat.bc, img.prefix = "tumor")
ascat.bc = ASCAT::ascat.aspcf(ascat.bc)
ASCAT::ascat.plotSegmentedData(ascat.bc)
ascat.output = ASCAT::ascat.runAscat(ascat.bc) 3.2 Tumor only
In tumor-only mode, read counts are normalized for median depth of coverage across autosomes.
ascat.bc = maftools::prepAscat_t(t_counts = "tumor_nucleotide_counts.tsv", sample_name = "tumor_only")
# Library sizes:
# Tumor: 1830168947
# Counts file: tumor_nucleotide_counts.tsv
# Markers: 932148
# Removed 2982 duplicated loci
# Markers > 15: 928607
# Median depth of coverage (autosomes): 76
# ------
# Generated following files:
# tumor_only.tumour.BAF.txt
# tumor_only.tumour.logR.txt
# ------The output logR and BAF files can be processed with ASCAT without matched normal data protocol:
ascat.bc = ASCAT::ascat.loadData(
Tumor_LogR_file = "tumor_only.tumour.logR.txt",
Tumor_BAF_file = "tumor_only.tumour.BAF.txt",
chrs = c(1:22, "X", "Y"),
sexchromosomes = c("X", "Y")
)
ASCAT::ascat.plotRawData(ASCATobj = ascat.bc, img.prefix = "tumor_only")
ascat.gg = ASCAT::ascat.predictGermlineGenotypes(ascat.bc)
ascat.bc = ASCAT::ascat.aspcf(ascat.bc, ascat.gg=ascat.gg)
ASCAT::ascat.plotSegmentedData(ascat.bc)
ascat.output = ASCAT::ascat.runAscat(ascat.bc) 3.3 CBS segmentation
Alternatively, tumor logR files generated by prepAscat()/prepAscat_t() can be processed with segmentLogR() function that performs circular binary segmentation and returns the DNAcopy object.
maftools::segmentLogR(tumor_logR = "tumor.tumour.logR.txt", sample_name = "tumor")
# Analyzing: tumor
# current chromosome: 1
# current chromosome: 2
# current chromosome: 3
# current chromosome: 4
# current chromosome: 5
# current chromosome: 6
# current chromosome: 7
# current chromosome: 8
# current chromosome: 9
# current chromosome: 10
# current chromosome: 11
# current chromosome: 12
# current chromosome: 13
# current chromosome: 14
# current chromosome: 15
# current chromosome: 16
# current chromosome: 17
# current chromosome: 18
# current chromosome: 19
# current chromosome: 20
# current chromosome: 21
# current chromosome: 22
# current chromosome: MT
# current chromosome: X
# current chromosome: Y
# Segments are written to: tumor_only.tumour_cbs.seg
# Segments are plotted to: tumor_only.tumour_cbs.png4 Processing Mosdepth output
Mosdepth offers the fastest way to estimate coverage metrics from WGS bam files. Output generated by mosdepth can be processed with maftools function plotMosdepth and plotMosdepth_t for CNV analysis by performing segmentation and plotting.
Below mosdepth command generates tumor.regions.bed.gz and normal.regions.bed.gz that contains depth of coverage across the genome in fixed windows.
mosdepth -n -b 5000 tumor tumor.bam
mosdepth -n -b 5000 normal normal.bamThe output {prefix}.regions.bed.gz can be imported and analyzed with maftools in tumor/normal or tumor only mode.
If you use the functions for CNV analysis, please cite the mosdepth publication
| Pedersen BS, Quinlan AR. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics. 2018;34(5):867-868. doi:10.1093/bioinformatics/btx699 |
4.1 Tumor normal pair
plotMosdepth(
t_bed = "tumor.regions.bed.gz",
n_bed = "normal.regions.bed.gz",
segment = TRUE,
sample_name = "tumor"
)
# Coverage ratio T/N: 1.821
# Running CBS segmentation:
# Analyzing: tumor01
# current chromosome: 1
# current chromosome: 2
# current chromosome: 3
# current chromosome: 4
# current chromosome: 5
# current chromosome: 6
# current chromosome: 7
# current chromosome: 8
# current chromosome: 9
# current chromosome: 10
# current chromosome: 11
# current chromosome: 12
# current chromosome: 13
# current chromosome: 14
# current chromosome: 15
# current chromosome: 16
# current chromosome: 17
# current chromosome: 18
# current chromosome: 19
# current chromosome: 20
# current chromosome: 21
# current chromosome: 22
# current chromosome: X
# current chromosome: Y
# Segments are written to: tumor01_cbs.seg
# Plotting
5 Session Info
sessionInfo()
#> R version 4.3.0 RC (2023-04-13 r84269)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] maftools_2.16.0
#>
#> loaded via a namespace (and not attached):
#> [1] digest_0.6.31 RColorBrewer_1.1-3 R6_2.5.1 fastmap_1.1.1
#> [5] Matrix_1.5-4 xfun_0.39 lattice_0.21-8 splines_4.3.0
#> [9] cachem_1.0.7 knitr_1.42 htmltools_0.5.5 rmarkdown_2.21
#> [13] cli_3.6.1 grid_4.3.0 sass_0.4.5 data.table_1.14.8
#> [17] jquerylib_0.1.4 compiler_4.3.0 tools_4.3.0 evaluate_0.20
#> [21] bslib_0.4.2 survival_3.5-5 yaml_2.3.7 DNAcopy_1.74.0
#> [25] rlang_1.1.0 jsonlite_1.8.4