Error in STEP 15: computing tumor subclusters via leiden,,heatmap.cnv(obs_data, Rowv = obs_dendrogram, Colv = FALSE, cluster.by.row = TRUE, : 'RowIndividualColors' must be a character vector of length nrow(x)

[qiuhuijiajia]

Hello InferCNV Team,

I've encountered this issue in Step 15, when I was running the following code:

infercnv_obj = infercnv::run(infercnv_obj,
cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
out_dir="gse155446_1225",
no_prelim_plot = T,
cluster_by_groups=TRUE,
denoise=TRUE,
HMM=F,
min_cells_per_gene = 10,
num_threads=10,
leiden_resolution = 0.05,
plot_steps=F,
write_expr_matrix = T)
Error : heatmap.cnv(obs_data, Rowv = obs_dendrogram, Colv = FALSE, cluster.by.row = TRUE, :
'RowIndividualColors' must be a character vector of length nrow(x)
Warning:
1: In asMethod(object) :
sparse->dense coercion: allocating vector of size 2.4 GiB
2: In asMethod(object) :
sparse->dense coercion: allocating vector of size 2.4 GiB
3: In asMethod(object) :
sparse->dense coercion: allocating vector of size 2.4 GiB
4: In cbind(row_groupings, get_group_color_palette()(length(table(hcl_obs_annotations_groups)))[hcl_obs_annotations_groups]) :
number of rows of result is not a multiple of vector length (arg 2)
5: In cbind(split_groups, row_groupings[, 1], hcl_obs_annotations_groups, :
number of rows of result is not a multiple of vector length (arg 3)

I am trying to determine if this issue stems from my sample data or if it's something that can be addressed technically. Here are the specifics of my analysis setup for your reference:

packageVersion("infercnv")
[1] ‘1.22.0’

My sessionInfo as follow:
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0

locale:
[1] LC_CTYPE=zh_CN.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=zh_CN.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=zh_CN.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] future_1.34.0 SeuratObject_4.1.4 Seurat_4.4.0 infercnv_1.22.0
[5] qs_0.27.2

loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.4.2
[3] later_1.4.1 bitops_1.0-9
[5] tibble_3.2.1 polyclip_1.10-7
[7] lifecycle_1.0.4 fastcluster_1.2.6
[9] edgeR_4.4.0 doParallel_1.0.17
[11] globals_0.16.3 lattice_0.22-5
[13] MASS_7.3-61 magrittr_2.0.3
[15] limma_3.62.1 plotly_4.10.4
[17] httpuv_1.6.15 sctransform_0.4.1
[19] spam_2.11-0 sp_2.1-4
[21] spatstat.sparse_3.1-0 reticulate_1.40.0
[23] cowplot_1.1.3 pbapply_1.7-2
[25] DBI_1.2.3 RColorBrewer_1.1-3
[27] multcomp_1.4-26 abind_1.4-8
[29] zlibbioc_1.52.0 Rtsne_0.17
[31] GenomicRanges_1.58.0 purrr_1.0.2
[33] BiocGenerics_0.52.0 TH.data_1.1-2
[35] sandwich_3.1-1 GenomeInfoDbData_1.2.13
[37] IRanges_2.40.0 S4Vectors_0.44.0
[39] ggrepel_0.9.6 irlba_2.3.5.1
[41] listenv_0.9.1 spatstat.utils_3.1-1
[43] goftest_1.2-3 spatstat.random_3.3-2
[45] fitdistrplus_1.2-1 parallelly_1.39.0
[47] leiden_0.4.3.1 codetools_0.2-19
[49] coin_1.4-3 DelayedArray_0.32.0
[51] RApiSerialize_0.1.4 tidyselect_1.2.1
[53] futile.logger_1.4.3 UCSC.utils_1.2.0
[55] farver_2.1.2 rjags_4-16
[57] matrixStats_1.4.1 stats4_4.4.2
[59] spatstat.explore_3.3-3 jsonlite_1.8.9
[61] progressr_0.15.1 ggridges_0.5.6
[63] survival_3.7-0 iterators_1.0.14
[65] foreach_1.5.2 tools_4.4.2
[67] ica_1.0-3 Rcpp_1.0.13-1
[69] glue_1.8.0 gridExtra_2.3
[71] SparseArray_1.6.0 MatrixGenerics_1.18.0
[73] GenomeInfoDb_1.42.0 dplyr_1.1.4
[75] withr_3.0.2 formatR_1.14
[77] BiocManager_1.30.25 fastmap_1.2.0
[79] fansi_1.0.6 caTools_1.18.3
[81] digest_0.6.37 parallelDist_0.2.6
[83] R6_2.5.1 mime_0.12
[85] colorspace_2.1-1 scattermore_1.2
[87] gtools_3.9.5 tensor_1.5
[89] spatstat.data_3.1-4 utf8_1.2.4
[91] tidyr_1.3.1 generics_0.1.3
[93] data.table_1.16.2 httr_1.4.7
[95] htmlwidgets_1.6.4 S4Arrays_1.6.0
[97] uwot_0.2.2 pkgconfig_2.0.3
[99] gtable_0.3.6 modeltools_0.2-23
[101] lmtest_0.9-40 SingleCellExperiment_1.28.1
[103] XVector_0.46.0 htmltools_0.5.8.1
[105] dotCall64_1.2 scales_1.3.0
[107] Biobase_2.66.0 png_0.1-8
[109] phyclust_0.1-34 spatstat.univar_3.1-1
[111] lambda.r_1.2.4 rstudioapi_0.17.1
[113] reshape2_1.4.4 coda_0.19-4.1
[115] nlme_3.1-166 zoo_1.8-12
[117] stringr_1.5.1 KernSmooth_2.23-24
[119] parallel_4.4.2 miniUI_0.1.1.1
[121] libcoin_1.0-10 pillar_1.9.0
[123] grid_4.4.2 vctrs_0.6.5
[125] gplots_3.2.0 RANN_2.6.2
[127] promises_1.3.2 stringfish_0.16.0
[129] xtable_1.8-4 cluster_2.1.8
[131] locfit_1.5-9.10 mvtnorm_1.3-2
[133] cli_3.6.3 compiler_4.4.2
[135] futile.options_1.0.1 rlang_1.1.4
[137] crayon_1.5.3 future.apply_1.11.3
[139] argparse_2.2.4 plyr_1.8.9
[141] stringi_1.8.4 viridisLite_0.4.2
[143] deldir_2.0-4 munsell_0.5.1
[145] lazyeval_0.2.2 spatstat.geom_3.3-4
[147] Matrix_1.7-1 patchwork_1.3.0
[149] ggplot2_3.5.1 statmod_1.5.0
[151] shiny_1.9.1 SummarizedExperiment_1.36.0
[153] ROCR_1.0-11 igraph_2.1.1
[155] RcppParallel_5.1.9 ape_5.8

output12252.txt

[qiuhuijiajia]

7: (function ()
traceback(2))()
6: stop("'RowIndividualColors' must be a character vector of length nrow(x)")
5: heatmap.cnv(obs_data, Rowv = obs_dendrogram, Colv = FALSE, cluster.by.row = TRUE,
cluster.by.col = FALSE, main = cnv_title, ylab = cnv_obs_title,
margin.for.labCol = 2, xlab = "Genomic Region", key = TRUE,
labCol = contig_labels, cexCol = contig_lab_size, cexAt = c(1,
contig_seps), notecol = "black", density.info = "histogram",
denscol = "blue", trace = "none", dendrogram = "row", cexRow = 0.8,
breaks = breaksList, gene_position_breaks = gene_position_breaks,
scale = "none", x.center = x.center, color.FUN = col_pal,
if.plot = !testing, sepList = contigSepList, sep.color = c("black",
"black"), sep.lty = 1, sep.lwd = 1, RowIndividualColors = row_groupings,
annotations_legend = annotations_legend, grouping_key_coln = grouping_key_coln,
ColIndividualColors = contig_colors, key.title = "Distribution of Expression",
key.xlab = "Modified Expression", key.ylab = "Count", force_lmat = layout_lmat,
force_lwid = layout_lwid, force_lhei = layout_lhei, useRaster = useRaster)
4: .plot_cnv_observations(infercnv_obj = infercnv_obj, obs_data = obs_data,
file_base_name = out_dir, do_plot = !is.na(output_format),
write_expr_matrix = write_expr_matrix, write_phylo = write_phylo,
output_filename_prefix = output_filename, cluster_contig = ref_contig,
contigs = contigs, contig_colors = ct.colors[contigs], contig_labels = contig_labels,
contig_names = contig_names, col_pal = custom_pal, contig_seps = col_sep,
num_obs_groups = k_obs_groups, obs_annotations_groups = obs_annotations_groups,
obs_annotations_names = obs_annotations_names, grouping_key_coln = grouping_key_coln[1],
cluster_by_groups = cluster_by_groups, cnv_title = title,
cnv_obs_title = obs_title, contig_lab_size = contig_cex,
breaksList = breaksList_t, gene_position_breaks = gene_position_breaks,
x.center = x.center, hclust_method = hclust_method, layout_lmat = force_layout[["lmat"]],
layout_lhei = force_layout[["lhei"]], layout_lwid = force_layout[["lwid"]],
useRaster = useRaster)
3: plot_cnv(subcluster_obj, cluster_by_groups = TRUE, output_filename = output_filename,
out_dir = out_dir, write_expr_matrix = FALSE)
2: plot_subclusters(infercnv_obj, out_dir = out_dir, output_filename = "infercnv_subclusters")
1: infercnv::run(infercnv_obj, cutoff = 0.1, out_dir = "gse155446_1225",
no_prelim_plot = T, cluster_by_groups = TRUE, denoise = TRUE,
HMM = F, min_cells_per_gene = 10, num_threads = 10, leiden_resolution = 0.05,
plot_steps = F, write_expr_matrix = T)

[qiuhuijiajia]

here is my output log
output12252.txt