cluster.counts #25
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Hi @amanda-fitz, I am not sure if I understand your question completely, but: (1) clonevol doesn't perform clustering. It takes the clustering from pyclone and reconstruct the concensus clonal evolution tree and estimates the clonal admixture for individual samples. (2) clonevol can use/estimate both median/mean CCF. There is a parameter called |
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Hi thanks for your reply and explanations.
My question is actually very simple but perhaps I didn't explain well.
I would like a numerical output for the variant cluster plot. So from the example below
[X]
I would like the Cluster number (i.e. cluster number assigned by ClonEvol, on here 1,2,3, etc, which I understand comes from pyclone cluster just assigned a new ID) and for each cluster, the median CCF by sample type. My script generates a 'cluster.counts' data file but it contains only a single median CCF output and the pyclone cluster ID. I imagine it would be straightforward to obtain a data file given this data is used to make the cluster plot?
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…________________________________
From: Ha X. Dang <notifications@github.com>
Sent: 31 October 2018 18:49
To: hdng/clonevol
Cc: Amanda Fitzpatrick; Mention
Subject: Re: [hdng/clonevol] cluster.counts (#25)
Hi @amanda-fitz<https://github.com/amanda-fitz>,
I am not sure if I understand your question completely, but:
(1) clonevol doesn't perform clustering. It takes the clustering from pyclone and reconstruct the concensus clonal evolution tree and estimates the clonal admixture for individual samples.
(2) clonevol can use/estimate both median/mean CCF. There is a parameter called cluster.center in infer.clonal.models function that takes either a string "mean" or "median".
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hi @amanda-fitz @hdng best. here are the resultls of the two sample : (2) the cluster results of sample_two : |
Hi there, can anyone help?
I have two things I'd like to add to my cluster.counts data file.
cluster number assigned by ClonEvol (1,2,3,4 etc) to data table cluster.counts - currently my cluster.counts data table only has the PyClone cluster ID.
Median CCF per sample (currently just median.ccf for each cluster)
[LM002_cluster.counts.xlsx]
Attached example of my cluster.counts file
(https://github.com/hdng/clonevol/files/2533239/LM002_cluster.counts.xlsx)
Here is my script:
pyclone.directory <- '/Users/amandafitzpatrick/Library/Mobile Documents/com
appleCloudDocs/DOCUMENTS/E57 exome sequencing/2018-08-30_results_ascat_pyclone/pyclone'output.directory <- '/Users/amandafitzpatrick/Library/Mobile Documents/com
appleCloudDocs/DOCUMENTS/E57 exome sequencing/2018-08-30_results_ascat_pyclone'sample.sheet.file <- 'sample_annotation.txt'
min.mutation.count <- 30
cancer.genes <- scan('/Users/amandafitzpatrick/Library/Mobile Documents/com
appleCloudDocs/DOCUMENTS/E57 exome sequencing/2018-08-30_results/Exome Sequencing/COMBINED list Stratton plus Caldas.txt', what = character())patient.id <- 'LM002'
loci.file <- file.path(pyclone.directory, patient.id, 'output', 'tables', 'annotated_loci.tsv')
loci <- read.table(loci.file, header = TRUE, sep = '\t', stringsAsFactors = FALSE)
sample.sheet <- read.table(sample.sheet.file, header = TRUE, sep = '\t', stringsAsFactors = FALSE)
clonevol.data <- loci %>%
mutate(
vaf = 100*cellular_prevalence/2,
is.driver = symbol %in% cancer.genes & 'exonic' == func & 'synonymous_SNV' != exonic_func
) %>%
select(mutation_id, cluster_id, sample_id, vaf, symbol, is.driver) %>%
spread(sample_id, vaf);
n.samples <- length( unique(loci$sample_id) )
if( 1 == n.samples ) stop('Need more than one sample for ClonEvol!')
cluster.counts <- loci %>%
group_by(cluster_id) %>%
summarize(
count = n()/n.samples,
min.ccf = min(cellular_prevalence),
median.ccf = median(cellular_prevalence),
mean.ccf = mean(cellular_prevalence)
) %>%
ungroup() %>%
filter(count >= min.mutation.count) %>%
arrange(-median.ccf)
recode.values <- 1:nrow(cluster.counts)
names(recode.values) <- as.character(cluster.counts$cluster_id)
clonevol.data <- clonevol.data %>%
select(-mutation_id) %>%
filter(cluster_id %in% cluster.counts$cluster_id) %>%
mutate(cluster = recode.values[ as.character(cluster_id) ] )
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