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hdng / clonevol

Inferring and visualizing clonal evolution in multi-sample cancer sequencing
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Error in infer.clonal.models function #19

Open vale86ita opened 6 years ago

vale86ita commented 6 years ago

Hello,

I get the following error when running infer.clonal.models(). Can you please help me fix this?

Finding consensus models across samples... Found 1 consensus model(s) Generating consensus clonal evolution trees across samples... Error in aggregate.data.frame(mf[1L], mf[-1L], FUN = FUN, ...) : no rows to aggregate

Here a sample of the data: cluster gene Ascite11.vaf Spheres.vaf Xenograft_p2.vaf Xenograft_p3.vaf 1 1 GPR4 19.80 40.00 43.93 44.74 2 1 KRTAP24-1 33.33 22.58 41.28 48.48 3 1 MLANA 23.26 87.50 81.48 100.00 4 1 PDP2 37.50 27.27 87.23 100.00 5 1 PGAP1 0.00 44.44 58.82 64.29 6 1 SPIRE2 26.15 48.68 76.14 100.00 Xenograft_p5.vaf Xenograft_p6.vaf Ascite11 Spheres Xenograft_p2 Xenograft_p3 1 49.53 42.86 19.80 40.00 43.93 44.74 2 41.61 64.18 33.33 22.58 41.28 48.48 3 100.00 100.00 23.26 87.50 81.48 100.00 4 98.86 100.00 37.50 27.27 87.23 100.00 5 24.00 62.50 0.00 44.44 58.82 64.29 6 97.20 97.06 26.15 48.68 76.14 100.00 Xenograft_p5 Xenograft_p6 1 49.53 42.86 2 41.61 64.18 3 100.00 100.00 4 98.86 100.00 5 24.00 62.50 6 97.20 97.06

Here the code of infer.clonal.models I used: y = infer.clonal.models(variants = data_asc11, cluster.col.name = 'cluster', vaf.col.names = vaf.col.names, sample.groups = sample.groups, cancer.initiation.model='monoclonal', subclonal.test = 'bootstrap', subclonal.test.model = 'non-parametric', num.boots = 1000, founding.cluster = 1, cluster.center = 'mean', ignore.clusters = NULL, clone.colors = clone.colors, min.cluster.vaf = 0.01, sum.p = 0.05, alpha = 0.05)

Thank you for your help! Valentina

hdng commented 6 years ago

Could you send complete code and input file so I can reproduce the exact error?

vale86ita commented 6 years ago

Hi,

here in attachment the code (_pipeline.txt) and the data (_data.txt).

Thank you for your help and support!

Valentina

2018-03-21 22:29 GMT+01:00 Ha X. Dang notifications@github.com:

Could you send complete code and input file so I can reproduce the exact error?

— You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub https://github.com/hdng/clonevol/issues/19#issuecomment-375102273, or mute the thread https://github.com/notifications/unsubscribe-auth/Aj4BbiEQrBnYXZdLFZtYtSjhB8PKkeOtks5tgsZJgaJpZM4SzTCS .

data_a11 = read.table('A11_clonal_evolution_data.txt', header = T, sep = "\t")

pdf('box_a11.pdf', width = 3, height = 3, useDingbats = FALSE, title='') pp <- plot.variant.clusters(data_a11, 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 = 20, violin = FALSE, box = FALSE, jitter = TRUE, jitter.shape = 1, jitter.color = clone.colors, jitter.size = 3, 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()

plot.pairwise(data_a11, col.names = vaf.col.names, out.prefix = 'variants.pairwise.plot', colors = clone.colors)

pdf('flow_a11.pdf', width=3, height=3, useDingbats=FALSE, title='') plot.cluster.flow(data_a11, vaf.col.names = vaf.col.names, sample.names = c('A11', 'SP', 'Xp2', 'Xp3', 'Xp5', 'Xp6'), colors = clone.colors) dev.off()

y = infer.clonal.models(variants = data_a11, cluster.col.name = 'cluster', vaf.col.names = vaf.col.names, sample.groups = sample.groups, cancer.initiation.model='monoclonal', subclonal.test = 'bootstrap', subclonal.test.model = 'non-parametric', num.boots = 1000, founding.cluster = 1, cluster.center = 'mean', ignore.clusters = NULL, clone.colors = clone.colors, min.cluster.vaf = 0.01, sum.p = 0.05, alpha = 0.05)

y = infer.clonal.models(variants = data_a11, cluster.col.name = 'cluster', vaf.col.names = vaf.col.names, sample.groups = sample.groups, cancer.initiation.model='polyclonal', subclonal.test = 'bootstrap', subclonal.test.model = 'non-parametric', num.boots = 1000, founding.cluster = 1, cluster.center = 'mean', ignore.clusters = NULL, clone.colors = clone.colors, min.cluster.vaf = 0.01, sum.p = 0.05, alpha = 0.05)

cluster gene A11.vaf SP.vaf Xp2.vaf Xp3.vaf Xp5.vaf Xp6.vaf A11 SP Xp2 Xp3 Xp5 Xp6 1 GPR4 19.80 40.00 43.93 44.74 49.53 42.86 19.80 40.00 43.93 44.74 49.53 42.86 1 KRTAP24-1 33.33 22.58 41.28 48.48 41.61 64.18 33.33 22.58 41.28 48.48 41.61 64.18 1 MLANA 23.26 87.50 81.48 100.00 100.00 100.00 23.26 87.50 81.48 100.00 100.00 100.00 1 PDP2 37.50 27.27 87.23 100.00 98.86 100.00 37.50 27.27 87.23 100.00 98.86 100.00 1 PGAP1 0.00 44.44 58.82 64.29 24.00 62.50 0.00 44.44 58.82 64.29 24.00 62.50 1 SPIRE2 26.15 48.68 76.14 100.00 97.20 97.06 26.15 48.68 76.14 100.00 97.20 97.06 1 ZNF860 25.71 19.05 67.69 100.00 100.00 100.00 25.71 19.05 67.69 100.00 100.00 100.00 1 DNAH14 41.51 52.27 53.16 56.06 53.93 66.67 41.51 52.27 53.16 56.06 53.93 66.67 1 HEATR2 41.67 31.71 65.32 80.46 69.08 67.65 41.67 31.71 65.32 80.46 69.08 67.65 1 NDUFA7 39.19 57.14 64.44 100.00 100.00 100.00 39.19 57.14 64.44 100.00 100.00 100.00 1 NDUFV1 18.60 24.14 43.48 46.97 30.57 24.00 18.60 24.14 43.48 46.97 30.57 24.00 1 NRG2 30.30 53.49 56.14 59.55 53.21 56.14 30.30 53.49 56.14 59.55 53.21 56.14 1 PTPRF 23.27 51.04 42.11 55.15 49.11 51.52 23.27 51.04 42.11 55.15 49.11 51.52 1 RELN 22.16 40.00 38.53 49.53 48.27 50.72 22.16 40.00 38.53 49.53 48.27 50.72 1 TFR2 24.23 49.57 43.18 54.90 51.90 50.00 24.23 49.57 43.18 54.90 51.90 50.00 1 ZNF658 44.44 36.36 50.00 51.72 63.46 62.50 44.44 36.36 50.00 51.72 63.46 62.50 1 CEP89 20.25 40.00 51.28 49.40 40.77 45.00 20.25 40.00 51.28 49.40 40.77 45.00 1 DCAF4 36.13 50.51 84.82 100.00 100.00 98.00 36.13 50.51 84.82 100.00 100.00 98.00 1 DZIP1 19.12 34.78 33.33 62.50 51.91 43.24 19.12 34.78 33.33 62.50 51.91 43.24 1 EVA1B 22.25 42.59 44.56 49.77 52.22 47.27 22.25 42.59 44.56 49.77 52.22 47.27 1 HACL1 21.69 13.95 47.62 51.25 52.03 35.71 21.69 13.95 47.62 51.25 52.03 35.71 1 KBTBD7 96.30 64.00 100.00 100.00 100.00 100.00 96.30 64.00 100.00 100.00 100.00 100.00 1 LRRK2 28.07 39.62 43.64 49.37 46.21 39.22 28.07 39.62 43.64 49.37 46.21 39.22 1 LTBP2 29.37 30.77 69.90 100.00 100.00 100.00 29.37 30.77 69.90 100.00 100.00 100.00 1 MUC6 12.93 13.85 26.13 25.93 26.29 34.12 12.93 13.85 26.13 25.93 26.29 34.12 1 TRPM4 18.55 44.29 54.95 45.71 51.13 54.67 18.55 44.29 54.95 45.71 51.13 54.67 1 ZNF862 12.20 50.91 42.14 53.09 51.49 54.24 12.20 50.91 42.14 53.09 51.49 54.24 1 CEMIP 31.58 31.03 75.31 100.00 100.00 100.00 31.58 31.03 75.31 100.00 100.00 100.00 1 CHSY1 27.27 24.49 83.61 100.00 98.63 100.00 27.27 24.49 83.61 100.00 98.63 100.00 1 FAM196B 19.44 15.79 44.87 57.14 47.83 53.33 19.44 15.79 44.87 57.14 47.83 53.33 2 DPCR1 0.00 16.67 17.14 0.00 0.00 0.00 0.00 16.67 17.14 0.00 0.00 0.00 2 DPCR1 0.00 50.00 69.23 0.00 0.00 0.00 0.00 50.00 69.23 0.00 0.00 0.00 2 GSC 0.00 35.29 0.00 0.00 0.00 0.00 0.00 35.29 0.00 0.00 0.00 0.00 2 LURAP1 23.29 63.64 51.69 59.26 47.88 60.00 23.29 63.64 51.69 59.26 47.88 60.00 2 SGTB 0.00 30.00 0.00 0.00 0.00 0.00 0.00 30.00 0.00 0.00 0.00 0.00 2 SIGLEC11 0.00 20.00 0.00 0.00 0.00 0.00 0.00 20.00 0.00 0.00 0.00 0.00 2 SYTL2 18.35 47.62 47.93 40.71 34.81 26.56 18.35 47.62 47.93 40.71 34.81 26.56 2 TBC1D22A 0.00 44.74 0.00 0.00 0.00 0.00 0.00 44.74 0.00 0.00 0.00 0.00 2 ZNF587 0.00 55.17 0.00 0.00 0.00 0.00 0.00 55.17 0.00 0.00 0.00 0.00 2 ACOXL 25.36 53.04 51.15 51.23 48.40 40.91 25.36 53.04 51.15 51.23 48.40 40.91 2 ALDH18A1 14.29 15.38 15.25 0.00 0.00 0.00 14.29 15.38 15.25 0.00 0.00 0.00 2 ASNS 20.49 60.78 55.15 50.00 48.03 45.45 20.49 60.78 55.15 50.00 48.03 45.45 2 FASN 30.00 55.56 47.46 36.11 42.34 36.59 30.00 55.56 47.46 36.11 42.34 36.59 2 FSCB 0.00 100.00 100.00 90.00 0.00 100.00 0.00 100.00 100.00 90.00 0.00 100.00 2 FSCB 0.00 30.00 0.00 0.00 0.00 0.00 0.00 30.00 0.00 0.00 0.00 0.00 2 PRKAG2 0.00 13.79 0.00 0.00 0.00 0.00 0.00 13.79 0.00 0.00 0.00 0.00 2 SNAPC4 0.00 38.71 0.00 0.00 0.00 0.00 0.00 38.71 0.00 0.00 0.00 0.00 2 TCF20 0.00 40.00 0.00 0.00 0.00 0.00 0.00 40.00 0.00 0.00 0.00 0.00 2 TNFRSF25 0.00 14.29 0.00 0.00 0.00 0.00 0.00 14.29 0.00 0.00 0.00 0.00 2 CNDP2 0.00 42.86 0.00 0.00 0.00 0.00 0.00 42.86 0.00 0.00 0.00 0.00 2 IQSEC3 0.00 22.22 0.00 0.00 0.00 0.00 0.00 22.22 0.00 0.00 0.00 0.00 2 MAP4 0.00 18.03 17.53 23.81 0.00 0.00 0.00 18.03 17.53 23.81 0.00 0.00 2 SIDT2 0.00 23.08 0.00 0.00 0.00 0.00 0.00 23.08 0.00 0.00 0.00 0.00 2 TPCN1 0.00 9.80 0.00 0.00 0.00 0.00 0.00 9.80 0.00 0.00 0.00 0.00 2 HCN4 0.00 34.09 0.00 0.00 0.00 0.00 0.00 34.09 0.00 0.00 0.00 0.00 3 BAI3 21.13 52.78 41.94 50.00 53.44 40.48 21.13 52.78 41.94 50.00 53.44 40.48 3 EXOC3L2 0.00 12.90 0.00 0.00 14.52 0.00 0.00 12.90 0.00 0.00 14.52 0.00 3 MUC16 0.00 57.50 18.87 21.30 23.31 15.38 0.00 57.50 18.87 21.30 23.31 15.38 3 RSPH1 26.26 47.41 38.87 44.44 51.46 46.85 26.26 47.41 38.87 44.44 51.46 46.85 3 ZNF208 0.00 30.00 16.13 0.00 23.08 0.00 0.00 30.00 16.13 0.00 23.08 0.00 3 ZNF493 0.00 30.77 0.00 35.00 25.45 15.15 0.00 30.77 0.00 35.00 25.45 15.15 3 ZNF493 14.29 30.77 0.00 34.15 25.00 14.71 14.29 30.77 0.00 34.15 25.00 14.71 3 ZNF493 0.00 25.00 0.00 26.67 20.63 0.00 0.00 25.00 0.00 26.67 20.63 0.00 3 ARVCF 0.00 9.30 0.00 0.00 0.58 0.00 0.00 9.30 0.00 0.00 0.58 0.00 3 CASKIN1 13.95 75.56 61.54 65.38 61.84 66.67 13.95 75.56 61.54 65.38 61.84 66.67 3 FAM69B 0.00 21.43 0.00 0.00 0.00 0.00 0.00 21.43 0.00 0.00 0.00 0.00 3 LEMD3 24.06 59.68 41.24 61.11 56.96 48.28 24.06 59.68 41.24 61.11 56.96 48.28 3 OBSCN 23.63 58.08 42.62 53.08 45.92 50.27 23.63 58.08 42.62 53.08 45.92 50.27 3 PAF1 0.00 50.00 29.09 48.15 34.25 52.38 0.00 50.00 29.09 48.15 34.25 52.38 3 SLC6A15 25.00 66.67 31.58 42.11 44.44 70.00 25.00 66.67 31.58 42.11 44.44 70.00 3 TGDS 29.73 67.50 45.95 44.58 46.30 41.30 29.73 67.50 45.95 44.58 46.30 41.30 3 ANKZF1 0.00 13.64 0.00 0.00 0.00 0.00 0.00 13.64 0.00 0.00 0.00 0.00 3 C17orf59 0.00 49.32 0.00 0.00 0.00 0.00 0.00 49.32 0.00 0.00 0.00 0.00 3 MYH9 0.00 37.78 0.00 0.00 0.00 0.00 0.00 37.78 0.00 0.00 0.00 0.00 3 PCDHA10 22.31 51.82 42.34 51.08 52.84 43.21 22.31 51.82 42.34 51.08 52.84 43.21 3 TAB2 0.00 15.00 0.00 0.00 0.00 0.00 0.00 15.00 0.00 0.00 0.00 0.00 3 TYW1 0.00 75.00 36.36 62.50 53.33 0.00 0.00 75.00 36.36 62.50 53.33 0.00 3 ZNF469 0.00 48.57 0.00 0.00 0.00 0.00 0.00 48.57 0.00 0.00 0.00 0.00 3 FN1 13.21 88.46 36.54 45.00 57.97 34.62 13.21 88.46 36.54 45.00 57.97 34.62 3 NEFM 0.00 40.00 0.00 0.00 0.00 0.00 0.00 40.00 0.00 0.00 0.00 0.00 3 PPP1R15B 26.01 54.78 34.84 48.02 49.88 48.25 26.01 54.78 34.84 48.02 49.88 48.25 3 PRG4 0.00 15.79 0.00 0.00 0.00 0.00 0.00 15.79 0.00 0.00 0.00 0.00 3 SIGLEC1 20.60 54.63 41.84 40.89 49.38 49.26 20.60 54.63 41.84 40.89 49.38 49.26 3 TMCC3 23.03 60.53 41.11 48.95 49.43 46.15 23.03 60.53 41.11 48.95 49.43 46.15 4 DPCR1 21.05 20.00 0.00 0.00 18.52 20.00 21.05 20.00 0.00 0.00 18.52 20.00 4 STRA6 0.00 40.00 0.00 0.00 0.00 0.00 0.00 40.00 0.00 0.00 0.00 0.00 4 CDH5 0.00 47.71 0.00 0.00 0.00 0.00 0.00 47.71 0.00 0.00 0.00 0.00 4 DNM2 21.74 44.83 15.79 24.58 18.86 21.62 21.74 44.83 15.79 24.58 18.86 21.62 4 HSPA2 0.00 44.44 0.00 0.00 0.00 0.00 0.00 44.44 0.00 0.00 0.00 0.00 4 TET2 18.75 46.67 0.00 0.00 0.00 0.00 18.75 46.67 0.00 0.00 0.00 0.00 4 TPX2 0.00 12.50 0.00 0.00 0.00 0.00 0.00 12.50 0.00 0.00 0.00 0.00 4 U2SURP 0.00 17.50 0.00 0.00 0.00 0.00 0.00 17.50 0.00 0.00 0.00 0.00 4 ANKRD54 0.00 15.79 0.00 0.00 0.00 0.00 0.00 15.79 0.00 0.00 0.00 0.00 4 ARHGEF10L 0.00 15.71 0.00 0.00 0.00 0.00 0.00 15.71 0.00 0.00 0.00 0.00 4 MAMDC4 0.00 15.00 0.00 0.00 0.00 0.00 0.00 15.00 0.00 0.00 0.00 0.00 4 ST3GAL5 46.67 57.14 50.00 70.59 45.45 0.00 46.67 57.14 50.00 70.59 45.45 0.00 4 TSNAXIP1 0.00 31.25 0.00 0.00 0.00 0.00 0.00 31.25 0.00 0.00 0.00 0.00 4 DYSF 0.00 14.29 0.00 0.00 0.00 0.00 0.00 14.29 0.00 0.00 0.00 0.00 4 OR52N4 0.00 34.09 0.00 0.00 0.00 0.00 0.00 34.09 0.00 0.00 0.00 0.00 4 SGMS2 34.21 46.67 0.00 0.00 0.00 0.00 34.21 46.67 0.00 0.00 0.00 0.00

hdng commented 6 years ago

I couldn't reproduce the exact error you reported, however, there were a few things missing in your code that produced a different error (see how sample.groups is passed). I consider this a bug of clonevol and will push fix. Here is the result and the working version of the code that produced the result:

image

library(clonevol)

data_a11 = read.table('A11_clonal_evolution_data.txt', header = T, sep = "\t")
vaf.col.names = grep('.vaf', colnames(data_a11), value=T)
clone.colors = NULL

pdf('box_a11.pdf', width = 7, height = 7, useDingbats = FALSE, title='')
pp <- plot.variant.clusters(data_a11,
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 = 20,
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()

plot.pairwise(data_a11, col.names = vaf.col.names,
out.prefix = 'variants.pairwise.plot',
colors = clone.colors)

pdf('flow_a11.pdf', width=3, height=3, useDingbats=FALSE, title='')
plot.cluster.flow(data_a11, vaf.col.names = vaf.col.names,
sample.names = c('A11', 'SP', 'Xp2', 'Xp3', 'Xp5', 'Xp6'),
colors = clone.colors)
dev.off()

sample.groups = rep('grp1', length(vaf.col.names))
names(sample.groups) = vaf.col.names

y = infer.clonal.models(variants = data_a11,
cluster.col.name = 'cluster',
vaf.col.names = vaf.col.names,
sample.groups = sample.groups,
cancer.initiation.model='monoclonal',
subclonal.test = 'bootstrap',
subclonal.test.model = 'non-parametric',
num.boots = 1000,
founding.cluster = 1,
cluster.center = 'mean',
ignore.clusters = NULL,
clone.colors = clone.colors,
min.cluster.vaf = 0.01,
sum.p = 0.05,
alpha = 0.05)

y = convert.consensus.tree.clone.to.branch(y)

# plot variant clusters, bell plots, cell populations, and trees
plot.clonal.models(y,
                   # box plot parameters
                   box.plot = TRUE,
                   fancy.boxplot = TRUE,
                   fancy.variant.boxplot.highlight = 'is.driver',
                   fancy.variant.boxplot.highlight.shape = 21,
                   fancy.variant.boxplot.highlight.fill.color = 'red',
                   fancy.variant.boxplot.highlight.color = 'black',
                   fancy.variant.boxplot.highlight.note.col.name = 'gene',
                   fancy.variant.boxplot.highlight.note.color = 'blue',
                   fancy.variant.boxplot.highlight.note.size = 2,
                   fancy.variant.boxplot.jitter.alpha = 1,
                   fancy.variant.boxplot.jitter.center.color = 'grey50',
                   fancy.variant.boxplot.base_size = 12,
                   fancy.variant.boxplot.plot.margin = 1,
                   fancy.variant.boxplot.vaf.suffix = '.VAF',
                   # bell plot parameters
                   clone.shape = 'bell',
                   bell.event = TRUE,
                   bell.event.label.color = 'blue',
                   bell.event.label.angle = 60,
                   clone.time.step.scale = 1,
                   bell.curve.step = 2,
                   # node-based consensus tree parameters
                   merged.tree.plot = TRUE,
                   tree.node.label.split.character = NULL,
                   tree.node.shape = 'circle',
                   tree.node.size = 30,
                   tree.node.text.size = 0.5,
                   merged.tree.node.size.scale = 1.25,
                   merged.tree.node.text.size.scale = 2.5,
                   merged.tree.cell.frac.ci = FALSE,
                   # branch-based consensus tree parameters
                   merged.tree.clone.as.branch = TRUE,
                   mtcab.event.sep.char = ',',
                   mtcab.branch.text.size = 1,
                   mtcab.branch.width = 0.75,
                   mtcab.node.size = 3,
                   mtcab.node.label.size = 1,
                   mtcab.node.text.size = 1.5,
                   # cellular population parameters
                   cell.plot = TRUE,
                   num.cells = 100,
                   cell.border.size = 0.25,
                   cell.border.color = 'black',
                   clone.grouping = 'horizontal',
                   #meta-parameters
                   scale.monoclonal.cell.frac = TRUE,
                   show.score = FALSE,
                   cell.frac.ci = TRUE,
                   disable.cell.frac = FALSE,
                   # output figure parameters
                   out.dir = 'output',
                   out.format = 'pdf',
                   overwrite.output = TRUE,
                   width = 11,
                   height = 7,
                   # vector of width scales for each panel from left to right
                   panel.widths = c(3,4,2,4,2))
vale86ita commented 6 years ago

Thank you very much for your help,

I understand that I missed part of the code!

Best, Valentina

2018-03-22 15:40 GMT+01:00 Ha X. Dang notifications@github.com:

I couldn't reproduce the exact error you reported, however, there was a few things missing in your code that produced a different error (see how sample.groups is passed). I consider this a bug of clonevol and will push fix. Here is the result and the working version of the code that produced the result:

[image: image] https://user-images.githubusercontent.com/8001943/37777190-f976cb3e-2db4-11e8-9669-947ebeed732b.png

library(clonevol)

data_a11 = read.table('A11_clonal_evolution_data.txt', header = T, sep = "\t") vaf.col.names = grep('.vaf', colnames(data_a11), value=T) clone.colors = NULL

pdf('box_a11.pdf', width = 7, height = 7, useDingbats = FALSE, title='') pp <- plot.variant.clusters(data_a11,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 = 20, 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()

plot.pairwise(data_a11, col.names = vaf.col.names, out.prefix = 'variants.pairwise.plot', colors = clone.colors)

pdf('flow_a11.pdf', width=3, height=3, useDingbats=FALSE, title='') plot.cluster.flow(data_a11, vaf.col.names = vaf.col.names, sample.names = c('A11', 'SP', 'Xp2', 'Xp3', 'Xp5', 'Xp6'), colors = clone.colors) dev.off()

sample.groups = rep('grp1', length(vaf.col.names)) names(sample.groups) = vaf.col.names

y = infer.clonal.models(variants = data_a11,cluster.col.name = 'cluster', vaf.col.names = vaf.col.names, sample.groups = sample.groups, cancer.initiation.model='monoclonal', subclonal.test = 'bootstrap', subclonal.test.model = 'non-parametric', num.boots = 1000, founding.cluster = 1, cluster.center = 'mean', ignore.clusters = NULL, clone.colors = clone.colors, min.cluster.vaf = 0.01, sum.p = 0.05, alpha = 0.05)

y = convert.consensus.tree.clone.to.branch(y)

plot variant clusters, bell plots, cell populations, and trees

plot.clonal.models(y,

box plot parameters

               box.plot = TRUE,
               fancy.boxplot = TRUE,
               fancy.variant.boxplot.highlight = 'is.driver',
               fancy.variant.boxplot.highlight.shape = 21,
               fancy.variant.boxplot.highlight.fill.color = 'red',
               fancy.variant.boxplot.highlight.color = 'black',
               fancy.variant.boxplot.highlight.note.col.name = 'gene',
               fancy.variant.boxplot.highlight.note.color = 'blue',
               fancy.variant.boxplot.highlight.note.size = 2,
               fancy.variant.boxplot.jitter.alpha = 1,
               fancy.variant.boxplot.jitter.center.color = 'grey50',
               fancy.variant.boxplot.base_size = 12,
               fancy.variant.boxplot.plot.margin = 1,
               fancy.variant.boxplot.vaf.suffix = '.VAF',
               # bell plot parameters
               clone.shape = 'bell',
               bell.event = TRUE,
               bell.event.label.color = 'blue',
               bell.event.label.angle = 60,
               clone.time.step.scale = 1,
               bell.curve.step = 2,
               # node-based consensus tree parameters
               merged.tree.plot = TRUE,
               tree.node.label.split.character = NULL,
               tree.node.shape = 'circle',
               tree.node.size = 30,
               tree.node.text.size = 0.5,
               merged.tree.node.size.scale = 1.25,
               merged.tree.node.text.size.scale = 2.5,
               merged.tree.cell.frac.ci = FALSE,
               # branch-based consensus tree parameters
               merged.tree.clone.as.branch = TRUE,
               mtcab.event.sep.char = ',',
               mtcab.branch.text.size = 1,
               mtcab.branch.width = 0.75,
               mtcab.node.size = 3,
               mtcab.node.label.size = 1,
               mtcab.node.text.size = 1.5,
               # cellular population parameters
               cell.plot = TRUE,
               num.cells = 100,
               cell.border.size = 0.25,
               cell.border.color = 'black',
               clone.grouping = 'horizontal',
               #meta-parameters
               scale.monoclonal.cell.frac = TRUE,
               show.score = FALSE,
               cell.frac.ci = TRUE,
               disable.cell.frac = FALSE,
               # output figure parameters
               out.dir = 'output',
               out.format = 'pdf',
               overwrite.output = TRUE,
               width = 11,
               height = 7,
               # vector of width scales for each panel from left to right
               panel.widths = c(3,4,2,4,2))

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