monkeyBai96
commented
2 years ago How do you solve the problem?
Open Aaron-sqw opened 2 years ago
monkeyBai96
commented
2 years ago How do you solve the problem?
ChiaraCaprioli
commented
1 year ago I encountered the same error and solved it by naming sample.groups, as also explained in the vignette
names(sample.groups) <- vaf.col.names
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:
==============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