Closed tomasellimichelle closed 4 years ago
Hey, I am running into the same problem, no consensus trees across samples. I am wondering of you could find a solution for that? Thanks
Good morning,
Yes, after many tries I founded about you must have a homogeneous raw data, try to filter your variants.
Thanks, Michelle.
El 29 nov 2022, a las 3:47, Shaghayegh Soudi (PhD) @.**@.>> escribió:
Hey, I am running into the same problem, no consensus trees across samples. I am wondering of you could find a solution for that? Thanks
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Thank you for your kind message, can you please just explain a little more, What type of filtering you are referring to?
Dear Ha X. Dang,
I am analysing four different spatial samples from cancer. I could reach to run clonevo until the function called infer.clonal.models and could not find any consensus models across samples... Could you try to help me to find what I made wrong or what could I do to obtain the proper models?
---- Below you will find the code used ----
I ran Sciclone and I obtained a file with the following header --> chr st sci_EPC3205_01.ref sci_EPC3205_01.var sci_EPC3205_01.vaf sci_EPC3205_01.cn sci_EPC3205_01.cleancn sci_EPC3205_01.depth sci_EPC3205_02.ref sci_EPC3205_02.var sci_EPC3205_02.vaf sci_EPC3205_02.cn sci_EPC3205_02.cleancn sci_EPC3205_02.depth sci_EPC3205_03.ref sci_EPC3205_03.var sci_EPC3205_03.vaf sci_EPC3205_03.cn sci_EPC3205_03.cleancn sci_EPC3205_03.depth sci_EPC3205_04.ref sci_EPC3205_04.var sci_EPC3205_04.vaf sci_EPC3205_04.cn sci_EPC3205_04.cleancn sci_EPC3205_04.depth adequateDepth cluster cluster.prob.1 cluster.prob.2 cluster.prob.3 cluster.prob.4 cluster.prob.5 cluster.prob.6 cluster.prob.7 cluster.prob.8 cluster.prob.9
library(clonevol)
library(devtools)
sample1 <- "EPC3205_01"
sample2 <-"EPC3205_02"
sample3 <- "EPC3205_03"
sample4 <- "EPC3205_04"
iinformation <- "EPC3205_minlen20" --> output file from sciclone (clusters)
patient <- "EPC3205"
numcolorsqtt <- 9
v1 <- read.delim2(file=iinformation, header=TRUE, sep="\t", dec=".", stringsAsFactors = FALSE) -- Save the file as a dataframe for a easy treat, delete NA's ff<- v1 delete.na <- function(DF, n=0) { DF[rowSums(is.na(DF)) <= n,] } ffy <- delete.na(ff) x<-ffy
vaf.col.names <- grep('.vaf', colnames(x), value=T)
sample.names <- gsub('.vaf', '', vaf.col.names)
x[, sample.names] <- x[, vaf.col.names]
vaf.col.names <- sample.names
sample.groups <- c(sample1, sample2, sample3 , sample4)
names(sample.groups) <- vaf.col.names
x <- x[order(x$cluster),]
library("colorspace")
clone.colors <- sequential_hcl(numcolorsqtt,palette = "Blue-Yellow")
pdf(paste0(patient,'_cluster_minlength_20.pdf'), useDingbats = FALSE, title='cluster_minlength_20')
pp <- plot.variant.clusters(x, cluster.col.name = 'cluster', show.cluster.size = FALSE, cluster.size.text.color = 'blue', vaf.col.names = vaf.col.names, vaf.limits = 70, sample.title.size = 8, violin = FALSE, box = T, jitter = TRUE, jitter.shape = 1, jitter.color = clone.colors, jitter.size = 1, jitter.alpha = 1, jitter.center.method = 'median', jitter.center.size = 1, jitter.center.color = 'darkgray', jitter.center.display.value = 'none', highlight.shape = 21, highlight.color = 'blue', highlight.fill.color = 'green', highlight.note.col.name = 'gene', highlight.note.size = 2, order.by.total.vaf = FALSE)
dev.off()
-------------------- console output --------------------
Warning messages:
1:
fun.y
is deprecated. Usefun
instead.2:
fun.y
is deprecated. Usefun
instead.3:
fun.y
is deprecated. Usefun
instead.4:
fun.y
is deprecated. Usefun
instead.5: Removed 15 rows containing non-finite values (stat_summary).
6: Removed 15 rows containing non-finite values (stat_boxplot).
7: Removed 15 rows containing missing values (geom_point).
8: Removed 1 rows containing non-finite values (stat_summary).
9: Removed 1 rows containing non-finite values (stat_boxplot).
10: Removed 1 rows containing missing values (geom_point).
plot.pairwise(x, col.names = vaf.col.names, out.prefix = paste0(patient,'_variants_minlength20.plot'), colors = clone.colors)
-------------------- console output --------------------
Warning messages:
1: Removed 15 rows containing missing values (geom_point).
2: Removed 15 rows containing missing values (geom_point).
3: Removed 16 rows containing missing values (geom_point).
4: Removed 1 rows containing missing values (geom_point).
5: Removed 1 rows containing missing values (geom_point).
pdf(paste0(patient,'_flow_minlength_20.pdf'),width=10, height=5, useDingbats=FALSE, title='flow_minlength_20.pdf')
plot.cluster.flow(x, vaf.col.names = vaf.col.names, sample.names = c(sample1, sample2, sample3 , sample4), colors = clone.colors)
dev.off()
y = infer.clonal.models(variants = x, cluster.col.name = 'cluster', vaf.col.names = vaf.col.names, sample.groups = sample.groups, vaf.in.percent = TRUE, cancer.initiation.model='monoclonal', subclonal.test = 'bootstrap', subclonal.test.model = 'non-parametric', num.boots = 1000, founding.cluster = 1,cluster.center = 'mean', ignore.clusters = NULL,merge.similar.samples = TRUE, clone.colors = clone.colors, min.cluster.vaf = 0.01,seeding.aware.tree.pruning = FALSE, sum.p = 0.05,alpha = 0.05)
-------------------- console output --------------------
Sample 1: sci_EPC3205_01 <-- sci_EPC3205_01
Sample 2: sci_EPC3205_02 <-- sci_EPC3205_02
Sample 3: sci_EPC3205_03 <-- sci_EPC3205_03
Sample 4: sci_EPC3205_04 <-- sci_EPC3205_04
Using monoclonal model
Note: all VAFs were divided by 100 to convert from percentage to proportion. Generating non-parametric boostrap samples... sci_EPC3205_01 : Enumerating clonal architectures... Determining if cluster VAF is significantly positive... Exluding clusters whose VAF < min.cluster.vaf=0.01 Non-positive VAF clusters: 5,9 sci_EPC3205_01 : 48 clonal architecture model(s) found
sci_EPC3205_02 : Enumerating clonal architectures... Determining if cluster VAF is significantly positive... Exluding clusters whose VAF < min.cluster.vaf=0.01 Non-positive VAF clusters: 2,7 sci_EPC3205_02 : 48 clonal architecture model(s) found
sci_EPC3205_03 : Enumerating clonal architectures... Determining if cluster VAF is significantly positive... Exluding clusters whose VAF < min.cluster.vaf=0.01 Non-positive VAF clusters: 3,6 sci_EPC3205_03 : 44 clonal architecture model(s) found
sci_EPC3205_04 : Enumerating clonal architectures... Determining if cluster VAF is significantly positive... Exluding clusters whose VAF < min.cluster.vaf=0.01 Non-positive VAF clusters: 4,7,9 sci_EPC3205_04 : 84 clonal architecture model(s) found
Finding consensus models across samples... Found 0 consensus model(s) Found 0 consensus model(s) Scoring models... Pruning consensus clonal evolution trees.... Seeding aware pruning is: off Number of unique pruned consensus trees: 0
Also, I realized that the following plots have gene names, I have to input this data during the sciclone procedure, or there is a way for adds the information now? Moreover, the gene names are a list of driver genes detected or a list of all variants that are located within genes?
Really thanks for your time and help, Michelle.