Install devtools from CRAN
install.packages('devtools')Set this to allow dependencies that throw warnings to be installed.
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS = TRUE)Install dependent packages and latest Bioconductor (if you haven't already)
source('https://bioconductor.org/biocLite.R')
biocLite('GenomicRanges')Install mskilab R dependencies (gUtils)
devtools::install_github('mskilab/gUtils')Install dryclean
devtools::install_github('mskilab/dryclean')(after installing R package) Add dryclean directory to PATH and test the executable
$ export PATH=${PATH}:$(Rscript -e 'cat(paste0(installed.packages()["dryclean", "LibPath"], "/dryclean/extdata/"))')
$ drcln -h ## to see the help message
dryclean is a robust principal component analysis (rPCA) based method. dryclean uses a panel of normal (PON) samples to learn the landscape of both biological and technical noise in read depth data. dryclean then uses this landscape significantly reduce noise and artifacts in the signal for tumor samples. The input to the algorithm is a GenomicsRanges object containing read depth. You can either use read count from your favorite tools (there are many fast tools out there, for example: megadepth). Using uncorrected read count as input for dryclean works well from our experience, but if you wish, you can use the GC amd mappability corrected read depth data from fragCounter that can be found at: https://github.com/mskilab/fragCounter . The only requirement for the input is centering the data. This can be achieved by dividing each bin by global mean signal of the sample .i.e. mean-normalization. This is an essential pre-processing step.
For creating PON the following factors are needed:
- Tumor and normal sample mean-normalized read count outputs should be stored in two different directories
- A data.table with two columns: a. "sample" column contains the sample name you will use to index the sample b. "normal_cov" is a column with paths to the normal samples to be used
Following is an example of such a table
normal_table_example = readRDS("~/git/dryclean/inst/extdata/normal_table.rds")
normal_table_example| sample | normal_cov |
|---|---|
| samp1 | ~/git/dryclean/inst/extdata/samp1.rds |
| samp2 | ~/git/dryclean/inst/extdata/samp2.rds |
| samp3 | ~/git/dryclean/inst/extdata/samp3.rds |
There are three ways to make the PON:
- Using all normal samples availabble. PON can be made with all available normal samples. In this case set use.all = TRUE
detergent = prepare_detergent(normal.table.path = "~/git/dryclean/inst/extdata/normal_table.rds", path.to.save = "~/git/dryclean/inst/extdata/", num.cores = 1, use.all = TRUE)- Using random subset of normal samples availabble.
detergent = prepare_detergent(normal.table.path = "~/git/dryclean/inst/extdata/normal_table.rds", path.to.save = "~/git/dryclean/inst/extdata/", num.cores = 1, use.all = FALSE, choose.randomly = TRUE)- Cluster based approach. In order to keep the size of PON as small as possible but maximize the information in the PON. This is acheived by clustering the genomic background of normal samples and selecting normal samples from each cluster. Hierarchical clustering is used on L matrix after decomposing a small genomic region of all normal samples.
detergent = prepare_detergent(normal.table.path = "~/git/dryclean/inst/extdata/normal_table.rds", path.to.save = "~/git/dryclean/inst/extdata/", num.cores = 1, use.all = FALSE, choose.by.clustering = TRUE)names(detergent)
head(detergent$L)- 'L'
- 'S'
- 'k'
- 'U.hat'
- 'V.hat'
- 'sigma.hat'
| 0.1186491 | 0.1293625 | 0.1181923 |
| 0.3428964 | 0.3738583 | 0.3415761 |
| 0.1189428 | 0.1296828 | 0.1184849 |
| 0.1230901 | 0.1342045 | 0.1226161 |
| 0.1094483 | 0.1193309 | 0.1090269 |
| 0.1233053 | 0.1344392 | 0.1228305 |
The detergent is a list with the following elements:
- L: This is the L low ranked matrix of all the PONs calculated by batch robust PCA mathod
- S: This is the S sparse matrix of all the PONs calculated by batch robust PCA mathod
- k: This is estimated rank of a matrix where coverage values from each normal sample forms a column
- U.hat: svd decompsed left sigular matrix of L required for online implentation of rPCA
- V.hat: svd decompsed right sigular matrix of L required for online implentation of rPCA
- sigma.hat: svd decompsed first k sigular values of L required for online implentation of rPCA
Since a PON is used for decomposing the tumor samples, a method is required identify and remove germline events. This is achieved by looking at all normal samples as a population and infer the markers that have a copynumber events at a given frequency, set by user.
In order to do so, normal samples are treated as tumors and all copy number changes in the normal samples are extracted using thr PON created. Here is an example:
decomp.1 = start_wash_cycle(cov = sample.1, detergent.pon.path = "~/git/dryclean/inst/extdata/", whole_genome = TRUE, chr = NA, germline.filter = FALSE)
Once all normal samples are decomposed, the data.table is updated to reflect that:
normal_table_example = readRDS("~/git/dryclean/inst/extdata/normal_table.rds")
normal_table_example| sample | normal_cov | decomposed_cov |
|---|---|---|
| samp1 | ~/git/dryclean/inst/extdata/samp1.rds | ~/git/dryclean/inst/extdata/decomp1.rd |
| samp2 | ~/git/dryclean/inst/extdata/samp2.rds | ~/git/dryclean/inst/extdata/decomp2.rd |
| samp3 | ~/git/dryclean/inst/extdata/samp3.rds | ~/git/dryclean/inst/extdata/decomp3.rd |
With this table we can run the following:
grm = identify_germline(normal.table.path = "~/git/dryclean/inst/extdata/normal_table.rds", path.to.save = "~/git/dryclean/inst/extdata/", signal.thresh=0.5, pct.thresh=0.98)
Now we are ready for tumor decomposition
Following is a dummy example. The data diretory has a dummy coverage gRanges object which requires "reads.corrected" field
coverage_file = readRDS("~/git/dryclean/data/dummy_coverage.rds")
coverage_fileGRanges object with 50 ranges and 1 metadata column:
seqnames ranges strand | reads.corrected
<Rle> <IRanges> <Rle> | <numeric>
[1] 22 1-3 * | 2.86974197952077
[2] 22 3-5 * | 2.22116750082932
[3] 22 5-7 * | 3.57646101620048
[4] 22 7-9 * | 3.28955231001601
[5] 22 9-11 * | 0.0134209531825036
... ... ... ... . ...
[46] 22 91-93 * | 1.89621421624906
[47] 22 93-95 * | 4.1652654542122
[48] 22 95-97 * | 1.22946820687503
[49] 22 97-99 * | 2.79558349982835
[50] 22 99-101 * | 1.8819149560295
-------
seqinfo: 1 sequence from an unspecified genome; no seqlengthsIn order to run dryclean, simply invoke the following function
cov_out = start_wash_cycle(cov = coverage_file, detergent.pon.path = "~/git/dryclean/inst/extdata", whole_genome = TRUE, chr = NA, germline.filter = TRUE, germline.file = "~/git/dryclean/inst/extdata/germline.markers.rds")
head(cov_out)
GRanges object with 6 ranges and 7 metadata columns:
seqnames ranges strand | background.log foreground.log
<Rle> <IRanges> <Rle> | <numeric> <numeric>
[1] 22 1-3 * | 0.0319473954249811 0.667269772358936
[2] 22 3-5 * | 0.285896847736708 0
[3] 22 5-7 * | -0.0356340182110771 0.886416246387707
[4] 22 7-9 * | 0.306494769047133 0.780626154020659
[5] 22 9-11 * | -0.134142034550078 -4.39454085149961
[6] 22 11-13 * | 0.568947906575528 0.713624255729269
input.read.counts median.chr foreground background
<numeric> <numeric> <numeric> <numeric>
[1] 2.86974197952077 2.5809692934854 1.94890908484404 1.03246319158914
[2] 2.22116750082932 2.5809692934854 1 1.33095515713431
[3] 3.57646101620048 2.5809692934854 2.42641836548012 0.964993398874529
[4] 3.28955231001601 2.5809692934854 2.18283863086114 1.35865436040174
[5] 0.0134209531825036 2.5809692934854 0.0123445470046525 0.874465851418496
[6] 3.07809003512375 2.5809692934854 2.04137633774307 1.766407647601
log.reads
<numeric>
[1] 1.05422212312404
[2] 0.798032958921047
[3] 1.27437376852101
[4] 1.19075147953513
[5] -4.31093812294871
[6] 1.12430928616619
-------
seqinfo: 1 sequence from an unspecified genome
The output has following metadata fields:
- background.log: This is the L low ranked vector after decomposition and represent the background noise separated by dryclean in the log space
- foreground.log: The S vector with the inferred copy number signal separated by dryclean, that forms foreground, in the log space
- reads: Raw read counts
- gc: GC score
- map: mappability score
- input.read.counts: This is the mean-normalized count input in linear space
- median.chr: median chromosome signal
- blacklisted: if off target marker list is available and used
- foreground: Foreground signal, that forms SCNAs (S vector) in read count/ratio space
- background: This is the L low ranked vector after decomposition and represent the background noise separated by dryclean in read count/ratio space
- log.reads: log of the mean-normalized count
- germline.blk: germline marker based on the inferred germline function
./drcln -i inst/extdata/samp1.rds -p inst/extdata/detergent.50.rds
Rprofile Loading
Rprofile Finished Loading
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(Let's dryclean the genomes!)
Loading PON a.k.a detergent from path provided
Let's begin, this is whole exome/genome
Initializing
Using the detergent provided to start washing
lambdas calculated
Here begins the wash cycle
calculating A and B
calculating v and s
Updating subspace
Combining matrices with gRanges
Giddy Up!
All the options and usage is as follows
./drcln -hRprofile Loading
Rprofile Finished Loading
Usage: ./drcln [options]
Options:
-i INPUT, --input=INPUT
Path to cov.rds file, mean-normalized count for sample under consideration
-p PON, --pon=PON
Path to Panel Of Normal (PON) on which batch rPCA have been run. Within the file should be L, S matrices, estimated rank for burnin samples and svd decomposition matrices for the same
-m NAME, --name=NAME
Sample / Individual name
-b BLACKLIST, --blacklist=BLACKLIST
blacklisted makers
-w WHOLEGENOME, --wholeGenome=WHOLEGENOME
If TRUE then it will process all chromosomes and parallelize it
-C CHROMOSOME, --chromosome=CHROMOSOME
If wholeGenome is FALSE, specify the chromosome to process
-g GERMLINE.FILTER, --germline.filter=GERMLINE.FILTER
If PON based germline filter is to be used for removing some common germline events, If set to TRUE, give path to germline annotated file
-f GERMLINE.FILE, --germline.file=GERMLINE.FILE
Path to file annotated with germline calls, if germline.filter == TRUE
-c CORES, --cores=CORES
How many cores to use
-o OUTDIR, --outdir=OUTDIR
output directory
-k COLLAPSE, --collapse=COLLAPSE
collapse 200bp fragCounter to 1kb, deprecatec
-h, --help
Show this help message and exit
The Panel of Normal samples (PON) of 395 TCGA WGS normal samples was created using hierarchical clustering approach described above and filtered for CNPs.
The file is 17G in size.
WGS 1 kb PON: https://mskilab.s3.amazonaws.com/hg19/WGS/detergent.rds
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