Closed rajeha closed 2 years ago
@rajeha Hi, for Wang approach (publish on Plos genetics), we only generate signatures for prostate cancer, if you are not studying this, I think you should explore the meaning of signature by your own. Maybe you can refer to https://xsliulab.github.io/PC_CNA_signature/.
If you want to explore copy number signature based on current discovery, you may want to use "S" (for method described in Steele et al. 2019).
. This year, they publish many reference signatures on Nature. After you extract signature with method "S", you can obtain similarity with specifying sig_db = "CNS_TCGA"
, or you can obtain the reference signatures from COSMIC.
The prostate reference signature is attached here
@ShixiangWang thank you for the quick response. Using method S allowed me to get similarities to the CNS_USARC set, but not the CNS_TCGA set (same error as above). Any suggestions?
> sim <- get_sig_similarity(cn_sig, sig_db = 'CNS_TCGA')
Error in get_sig_similarity(cn_sig, sig_db = "CNS_TCGA") :
The following components cannot be found in reference!
0:homdel:0-100Kb 0:homdel:100Kb-1Mb 0:homdel:>1Mb 1:LOH:0-100Kb 1:LOH:100Kb-1Mb 1:LOH:1Mb-10Mb 1:LOH:10Mb-40Mb 1:LOH:>40Mb 2:LOH:0-100Kb 2:LOH:100Kb-1Mb 2:LOH:1Mb-10Mb 3-4:LOH:0-100Kb 3-4:LOH:100Kb-1Mb 3-4:LOH:>40Mb 5-8:LOH:0-100Kb 5-8:LOH:100Kb-1Mb 5-8:LOH:1Mb-10Mb 9+:LOH:0-100Kb 9+:LOH:100Kb-1Mb 9+:LOH:1Mb-10Mb 2:het:0-100Kb 2:het:100Kb-1Mb 2:het:1Mb-10Mb 2:het:10Mb-40Mb 2:het:>40Mb 3-4:het:0-100Kb 3-4:het:100Kb-1Mb 3-4:het:1Mb-10Mb 3-4:het:10Mb-40Mb 3-4:het:>40Mb 5-8:het:0-100Kb 5-8:het:100Kb-1Mb 5-8:het:1Mb-10Mb 5-8:het:10Mb-40Mb 5-8:het:>40Mb 9+:het:0-100Kb 9+:het:100Kb-1Mb 9+:het:1Mb-10Mb 9+:het:10Mb-40Mb 9+:het:>40Mb
> sim <- get_sig_similarity(cn_sig, sig_db = 'CNS_USARC')
-Comparing against COSMIC signatures
------------------------------------
--Found Sig1 most similar to USARC_CNS4
Aetiology: See https://doi.org/10.1016/j.ccell.2019.02.002 [similarity: 0.637]
--Found Sig2 most similar to USARC_CNS4
Aetiology: See https://doi.org/10.1016/j.ccell.2019.02.002 [similarity: 0.79]
--Found Sig3 most similar to USARC_CNS4
Aetiology: See https://doi.org/10.1016/j.ccell.2019.02.002 [similarity: 0.344]
------------------------------------
Return result invisiblely.
@rajeha The "S" approach generates two matrices with different catalogs, for "USARC", it's 40 catalogs (designed in a cancer cell paper); while for "TCGA", it's 48 catalogs (designed in a nature paper). For getting similarity to CNS_TCGA, you have to extract signature with 48-catalog matrix.
A full example is given below:
load(system.file("extdata", "toy_segTab.RData",
package = "sigminer", mustWork = TRUE
))
cn <- read_copynumber(segTabs,
seg_cols = c("chromosome", "start", "end", "segVal"),
genome_build = "hg19", complement = FALSE
)
cn
set.seed(1234)
segTabs$minor_cn <- sample(c(0, 1), size = nrow(segTabs), replace = TRUE)
cn <- read_copynumber(segTabs,
seg_cols = c("chromosome", "start", "end", "segVal"),
genome_measure = "wg", complement = TRUE, add_loh = TRUE
)
# Use tally method "S" (Steele et al.)
tally_s <- sig_tally(cn, method = "S")
cn_sig = sig_extract(tally_s$all_matrices$CN_48, n_sig = 2)
get_sig_similarity(cn_sig, sig_db = "CNS_TCGA")
I am closing as it it completed. Feel free to reopen it if you have further questions.
Yes that was super helpful, thanks!
Thank you for this great package! I have been following the documentation and performed CNV signature extraction using the Wang method. I am wondering if there is a method to make sense of extracted CNV signatures? I am able to visualize the CNV signatures but would like to get similarity estimates as is possible for single and dinucleotide polymorphisms (which works fine). I have tried getting similarity estimates like so:
sim <- get_sig_similarity(cn_sig2, sig_db = "CNS_USARC")
Here's what some of my signatures look like:
Would appreciate any guidance. Thanks!