Is there a minimum number of samples required for testBetaDiversity?

[emi-panzetta]

Hi, I appreciate your help. I ran testBetaDiversity in a subset of samples of a phyloseq object successfully (75 samples), then I try to do the same in a smaller subset of the samples (6 samples, 3 in each group) and I get this error:

Is there a minimum number of samples required for this function?

sample_data(ps) X1 <- diag(nrow(sample_data(ps))) colnames(X1) <- rownames(X1) <- as.character(1:ncol(X1))

divnetgenus <- divnet(W = ps, X = X1, variance = "none")

sample_data(ps)$myvariable <- as.factor (sample_data(ps)$myvariable)

set.seed(2) bc_g_test <- testBetaDiversity(dv = divnetgenus, h0 = "bray-curtis", n_boot = 10000, sample_specimen_matrix = divnetgenus$X, groups = sample_data(ps)$myvariable)

Error in apply(dv$fitted_z[samples, ], 2, median) : dim(X) must have a positive length

sessioninfo R version 4.2.2 (2022-10-31) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Ventura 13.1

Matrix products: default LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib

locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages: [1] dada2_1.26.0 Rcpp_1.0.11 doSNOW_1.0.20
[4] snow_0.4-4 doParallel_1.0.17 iterators_1.0.14
[7] foreach_1.5.2 writexl_1.4.2 DivNet_0.4.0
[10] breakaway_4.8.4 magrittr_2.0.3 gridExtra_2.3
[13] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[16] purrr_1.0.2 readr_2.1.4 tidyr_1.3.0
[19] tibble_3.2.1 tidyverse_2.0.0 ggtext_0.1.2
[22] speedyseq_0.5.3.9018 microViz_0.10.6 phyloseq_1.42.0
[25] scales_1.2.1 ggplot2_3.4.4 car_3.1-2
[28] carData_3.0-5 lme4_1.1-34 Matrix_1.5-4.1
[31] dplyr_1.1.3 readxl_1.4.3

loaded via a namespace (and not attached): [1] plyr_1.8.8 igraph_1.5.1
[3] splines_4.2.2 BiocParallel_1.32.6
[5] GenomeInfoDb_1.34.9 digest_0.6.33
[7] ca_0.71.1 htmltools_0.5.6.1
[9] fansi_1.0.5 cluster_2.1.4
[11] tzdb_0.4.0 Biostrings_2.66.0
[13] RcppParallel_5.1.7 matrixStats_1.0.0
[15] vroom_1.6.4 timechange_0.2.0
[17] jpeg_0.1-10 colorspace_2.1-0
[19] xfun_0.40 crayon_1.5.2
[21] RCurl_1.98-1.12 jsonlite_1.8.7
[23] survival_3.5-7 ape_5.7-1
[25] glue_1.6.2 registry_0.5-1
[27] gtable_0.3.4 zlibbioc_1.44.0
[29] XVector_0.38.0 DelayedArray_0.24.0
[31] Rhdf5lib_1.20.0 BiocGenerics_0.44.0
[33] abind_1.4-5 DBI_1.1.3
[35] gridtext_0.1.5 bit_4.0.5
[37] stats4_4.2.2 RColorBrewer_1.1-3
[39] pkgconfig_2.0.3 farver_2.1.1
[41] deldir_1.0-9 utf8_1.2.4
[43] tidyselect_1.2.0 labeling_0.4.3
[45] rlang_1.1.1 reshape2_1.4.4
[47] munsell_0.5.0 cellranger_1.1.0
[49] tools_4.2.2 cli_3.6.1
[51] generics_0.1.3 ade4_1.7-22
[53] evaluate_0.22 biomformat_1.26.0
[55] fastmap_1.1.1 yaml_2.3.7
[57] bit64_4.0.5 knitr_1.45
[59] nlme_3.1-163 mvnfast_0.2.8
[61] xml2_1.3.5 compiler_4.2.2
[63] rstudioapi_0.15.0 png_0.1-8
[65] stringi_1.7.12 lattice_0.21-8
[67] commonmark_1.9.0 nloptr_2.0.3
[69] markdown_1.8 vegan_2.6-4
[71] microbiome_1.20.0 permute_0.9-7
[73] multtest_2.54.0 vctrs_0.6.4
[75] pillar_1.9.0 lifecycle_1.0.3
[77] rhdf5filters_1.10.1 BiocManager_1.30.22
[79] data.table_1.14.8 bitops_1.0-7
[81] seriation_1.4.2 patchwork_1.1.3
[83] GenomicRanges_1.50.2 R6_2.5.1
[85] latticeExtra_0.6-30 hwriter_1.3.2.1
[87] TSP_1.2-4 ShortRead_1.56.1
[89] IRanges_2.32.0 codetools_0.2-19
[91] boot_1.3-28.1 MASS_7.3-60
[93] rhdf5_2.42.1 SummarizedExperiment_1.28.0 [95] withr_2.5.2 GenomicAlignments_1.34.1
[97] Rsamtools_2.14.0 S4Vectors_0.36.2
[99] GenomeInfoDbData_1.2.9 mgcv_1.9-0
[101] hms_1.1.3 grid_4.2.2
[103] minqa_1.2.5 rmarkdown_2.25
[105] MatrixGenerics_1.10.0 Rtsne_0.16
[107] Biobase_2.58.0 interp_1.1-4