infer.clonal.models() no rows to aggregate #51
Description
Dear Dang,
When I run the infer.clonal.models() for parents order, I meet a problem.
I reads the old issues about this question, but the method all not worked.
===============Error:==================
Sample 1: Pre1 <-- Pre1
Sample 2: Pre2 <-- Pre2
Sample 3: Post <-- Post
Using monoclonal model
Note: all VAFs were divided by 100 to convert from percentage to proportion.
Generating non-parametric boostrap samples...
Pre1 : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters: 6
Pre1 : 10 clonal architecture model(s) found
Pre2 : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters: 5
Pre2 : 22 clonal architecture model(s) found
Post : Enumerating clonal architectures...
Determining if cluster VAF is significantly positive...
Exluding clusters whose VAF < min.cluster.vaf=0.01
Non-positive VAF clusters: 3,5
Post : 7 clonal architecture model(s) found
Finding consensus models across samples...
Found 4 consensus model(s)
Generating consensus clonal evolution trees across samples...
Error in aggregate.data.frame(lhs, mf[-1L], FUN = FUN, ...): no rows to aggregate
Traceback:
- infer.clonal.models(variants = x, cluster.col.name = "cluster",
. vaf.col.names = vaf.col.names, sample.groups = sample.groups,
. sample.names = NULL, cancer.initiation.model = "monoclonal",
. subclonal.test = "bootstrap", subclonal.test.model = "non-parametric",
. num.boots = 1000, founding.cluster = 1, cluster.center = "median",
. ignore.clusters = NULL, clone.colors = clone.colors, min.cluster.vaf = 0.01,
. sum.p = 0.05, alpha = 0.05) - find.matched.models(vv, sample.names, sample.groups, merge.similar.samples = merge.similar.samples)
- merge.clone.trees(m, samples = samples, sample.groups, merge.similar.samples = merge.similar.samples)
- aggregate(sample.group ~ ., cgrp, paste, collapse = ",")
- aggregate.formula(sample.group ~ ., cgrp, paste, collapse = ",")
- aggregate.data.frame(lhs, mf[-1L], FUN = FUN, ...)
- stop("no rows to aggregate")
==============my Code==============
library(clonevol)
x=read.table("cluster2_rmNA.xls",head=T,sep="\t")
head(x)
vaf.col.names <- grep('.vaf', colnames(x), value=T)
vaf.col.names
sample.names <- gsub('.vaf', '', vaf.col.names)
sample.names
x[, sample.names] <- x[, vaf.col.names]
head(x)
x <- x[order(x$cluster),]
head(x)
sample.groups <- c('Pre1','Pre2','Post')
vaf.col.names = c('Pre1','Pre2','Post')
sample.names <- c('Pre1','Pre2','Post')
y = infer.clonal.models(variants = x,
cluster.col.name = 'cluster',
vaf.col.names = vaf.col.names,
sample.groups = sample.groups,
sample.names = NULL,
cancer.initiation.model='monoclonal',
subclonal.test = 'bootstrap',
subclonal.test.model = 'non-parametric',
num.boots = 1000,
founding.cluster = 1,
cluster.center = 'median',
ignore.clusters = NULL,
clone.colors = clone.colors,
min.cluster.vaf = 0.01,
sum.p = 0.05,
alpha = 0.05)
===============My data is ===================
cluster gene Pre1.vaf Pre2.vaf Post.vaf
2 gene2 34.58 22.54 12.83
2 gene3 41.73 26.79 21.9
2 gene4 37.86 23.11 12.95
2 gene5 34.81 18.35 19.65
3 gene6 37.85 8.27 0
3 gene7 34.65 14.29 0
2 gene8 44.05 18.94 24.19
6 gene9 0 9.92 7.66
6 gene10 0 10.24 2.31
3 gene11 35.05 6.97 0
3 gene12 34.39 7.72 0
2 gene13 36.14 25.83 18.31
4 gene14 22.07 20.34 21.56
6 gene15 0 14.92 24.37
3 gene16 26.83 5.36 0
6 gene17 0 17.16 16.67
2 gene18 33.33 28.08 18.75
5 gene19 8.33 0 0
6 gene20 0 4.49 3.83
2 gene21 30.65 25.89 19.33
6 gene22 0 7.51 0.6
2 gene23 34.1 29.75 28.25
2 gene24 35.44 28.1 30.36
2 gene25 35.89 27.5 36.92
6 gene26 0 8.25 0
1 gene27 46.03 33.33 24.39
2 gene28 40.8 30.04 28.43
6 gene29 0 8.11 12.24
3 gene30 41 6.97 0
3 gene31 33.33 4.68 0
4 gene32 15.29 21.09 11.89
1 gene33 70.76 45.48 21.32
1 gene34 85.47 71.12 61.57
6 gene35 0 9.49 1.89
2 gene36 35.43 27.6 16.94
6 gene37 0 13.77 0
6 gene38 0 9 0
3 gene39 34.5 8.44 0
2 gene40 34.17 17.65 20.97
3 gene41 33.82 10.22 0
3 gene42 34.69 8.5 1.16
2 gene43 39.9 29.17 24.16
2 gene44 33.98 23.36 22.12
6 gene45 0 11.54 0
4 gene46 15.76 18.67 15.79
2 gene47 23.78 26.29 22
1 gene48 46.12 35.94 28.08
3 gene49 19.26 6.4 0
4 gene50 16.32 10.42 12.12
3 gene51 34.12 5.95 0
6 gene52 0 10.93 2.5
3 gene53 36.43 6.37 0
2 gene54 32.97 22.14 20.41
6 gene55 0.31 7.48 14.06
2 gene56 34.72 32.35 17.65
2 gene57 36.22 19.23 16.87
3 gene58 41.3 10.34 0
5 gene59 14.89 0 0
2 gene60 35.61 26.32 24.19
1 gene61 41.29 32.45 45.61
6 gene62 0 0 14.29
2 gene63 42.17 29.88 14.02
3 gene64 33.63 8.21 0
2 gene65 36.54 25.36 16.99
2 gene66 35.2 20.47 15.2
3 gene67 38.18 7.99 1.6
3 gene68 34.43 9.51 0
2 gene69 34.29 24.83 20.21
3 gene70 32.56 5.43 0
3 gene71 37.25 8.12 0.49
2 gene72 43.55 25 31.54
2 gene73 29.86 23.54 26.18
2 gene74 32.31 24.28 9.05
2 gene75 36.61 27.3 27.32
2 gene76 31.39 27.27 29.72
1 gene77 51.15 37.96 39.85
6 gene78 0 16.87 0
6 gene79 0 7.51 0
3 gene80 30.71 11.7 0
2 gene81 36.31 18.33 19.51
4 gene82 9.04 10.98 12.76
2 gene83 33.54 32.58 27.11
2 gene84 36.23 21.19 20.31
2 gene85 30.81 18.72 22.73
2 gene86 32.11 23.4 36.8
2 gene87 32.89 28.57 29.09
3 gene88 44.35 9.55 0.72
3 gene89 36.88 8.16 0
3 gene90 38.64 7.69 0
1 gene91 45.83 26.32 46.42
2 gene92 33.79 24.89 13.97
2 gene93 36.19 21.76 32.14
2 gene94 33.48 26.43 24.5
2 gene95 34.8 26.18 20.66
2 gene96 31.69 25 21.38
2 gene97 36.59 29.48 23.19
2 gene98 38.48 33.42 22.55
3 gene99 32.67 11.76 0
2 gene100 29.1 27.65 10.95
2 gene101 40.4 24.88 29.94
2 gene102 35.43 24.12 27.78
2 gene103 40.34 31.47 33.82
3 gene104 43.43 3.03 0.27
6 gene105 0 7.45 3.17
2 gene106 36.24 30.67 15.53
2 gene107 43.43 25.7 14.38
2 gene108 38.15 26.7 15.89
2 gene109 37.05 30.28 29.21
2 gene110 40.19 25.24 17.2
2 gene111 35.35 23.1 19.02
6 gene112 0 7.23 23.08
2 gene113 32.42 22.02 18.64
2 gene114 28.33 24.4 20.98
2 gene115 34.13 18.9 18.15
3 gene116 39.09 8.56 0
2 gene117 39.01 25.07 19.05
3 gene118 40.4 7.55 0.4
2 gene119 34.57 23.68 38.15
6 gene120 0.49 12.32 4.2
2 gene121 30.67 24.16 13.27
2 gene122 32.26 24.66 25.27
2 gene123 34.65 23.43 14.09
6 gene124 0 11.6 8.06
2 gene125 31.78 20 19.87
2 gene126 30.08 29.67 25.17
2 gene127 41.04 22.64 16.44
2 gene128 38.18 25.4 15.96
5 gene129 14.46 0 0.36
2 gene130 33.68 26.42 13.47
2 gene131 38.17 33.26 26.13
2 gene132 33.33 21.47 22.73
2 gene133 44.14 27.06 25.71
2 gene134 32.74 19.82 24.82
2 gene135 35.04 25 20.85
2 gene136 34.3 25.29 20.42
2 gene137 39.11 25.14 18.69
2 gene138 32.62 24 12.9
6 gene139 0 6.22 13.62
6 gene140 0 0 11.59
2 gene141 32.83 22.29 12.14
3 gene142 34.19 5.92 0
2 gene143 32.2 28.49 21.1
5 gene144 11.14 0 0
3 gene145 29.3 6.63 0
5 gene146 12.46 0 0
2 gene147 30.91 28.74 19.11
6 gene148 0 8.15 2.33
2 gene149 30.63 29 17.8
2 gene150 32.9 28 25.74
2 gene151 30.66 36.36 28.8
2 gene152 35.71 19.88 23.45
2 gene153 32.89 27.85 20.28
3 gene154 29.32 9.33 0
2 gene155 35.18 23.47 26.24
2 gene156 34.1 27.74 32.72
1 gene157 54.76 33.7 25.95
1 gene158 57.6 46.41 27.19
1 gene159 59.24 29.26 33.13
1 gene160 35.92 40.91 24.37
3 gene161 59.69 12.26 0
2 gene162 37.06 26.97 20.74
2 gene163 30.32 26.44 27.23
3 gene164 38.52 6.48 0
2 gene165 37.62 31.05 26.42
2 gene166 30.37 26.27 15.71
1 gene167 66.06 34.07 24.77
1 gene168 53.57 42.53 23.68
4 gene169 22.12 13.22 20.16
2 gene170 27.64 20.14 23.89
2 gene171 27.52 25.11 23.6
2 gene172 37.46 25.76 12.07
3 gene173 32.23 8.13 0.29
2 gene174 35.44 23.14 15
3 gene175 38.57 8.92 3.95
6 gene176 0 6.28 0
2 gene177 33.97 23.34 16.22
2 gene178 36.44 26.36 19.25
3 gene179 35 9.45 0
2 gene180 37.5 26.05 24.92
2 gene181 32.18 25.3 7.83
4 gene182 13.16 14.22 22.57
2 gene183 33.2 27.81 23.36
6 gene184 0 8.38 19.26
2 gene185 31.82 25.53 21.7
2 gene186 36.63 30.74 35.42
2 gene187 35.03 21.61 12.25
3 gene188 31.74 15.85 0
6 gene189 0 9.17 0
6 gene190 0 11.41 0
2 gene191 38.49 32.36 28
2 gene192 34.01 30.83 32.88
3 gene193 36.67 5.45 0
2 gene194 29.19 18.59 6.09
Thanks,
Qiwei