ChiLiubio
commented
3 years ago Hi, @Irisescat
Thanks for your finding on the difference. I find that the input data have differences between R version and Huttenhower galaxy python version. For example, in the Galaxy_LEfSe/lefse_format.txt, the first row of taxa is kUnclassified|pOther|cOther|oOther|fOther|gOther|s__Other. In your scripts, after using tidy_taxonomy() and cal_abund(), the taxa abundance table can have no such ‘Other’ or ‘unclassified’, as those information are chaotic and may affect some following data analysis. So, there is a strict checking on the taxonomic info with tidy_taxonomy() to filter those classification. The input of lefse in microeco depend on the taxa_abund from cal_abund() function. Now, without the consistency of the input data and your microtable object dataset, I can not repeat the steps and make sure whether those difference actually come from the input data inconsistency or other points. Could you generate a ‘lefse_format.txt’ by saving the taxa_abund after using cal_abund() and try again in the Huttenhower galaxy ? If this issue still occur, please feel free to tell me and send me your microtable object dataset by using save function.
Chi
Irisescat
Hi, Thanks for the helpful package of microbiome study. When I tried to compare the features of three different groups (no subgroups) with LEfSe on the microeco and the Huttenhower galaxy server(using the all-against-all mode), using LDA > 3.0. I noticed the results are not similar, the features marked as different Group. I'm not sure if this is correct, and hope someone can help me to clarify the difference.
sessionInfo() `R version 4.1.0 (2021-05-18) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 21.04
Matrix products: default BLAS: /usr/local/lib/R/lib/libRblas.so LAPACK: /usr/local/lib/R/lib/libRlapack.so
locale: [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C [3] LC_TIME=C.UTF-8 LC_COLLATE=C.UTF-8 [5] LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 [7] LC_PAPER=C.UTF-8 LC_NAME=C.UTF-8 [9] LC_ADDRESS=C.UTF-8 LC_TELEPHONE=C.UTF-8 [11] LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C.UTF-8
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] magrittr_2.0.1 r2excel_1.0.0 xlsx_0.6.5 RColorBrewer_1.1-2 [5] tibble_3.1.4 pheatmap_1.0.12 ggtree_3.0.4 ggplot2_3.3.5 [9] file2meco_0.1.1 microeco_0.5.1 tidyr_1.1.3.9000 dplyr_1.0.7 [13] phyloseq_1.36.0
loaded via a namespace (and not attached): [1] Biobase_2.52.0 jsonlite_1.7.2 splines_4.1.0 [4] foreach_1.5.1 assertthat_0.2.1 stats4_4.1.0 [7] yulab.utils_0.0.2 xlsxjars_0.6.1 GenomeInfoDbData_1.2.6 [10] pillar_1.6.2 lattice_0.20-44 glue_1.4.2 [13] XVector_0.32.0 colorspace_2.0-2 ggfun_0.0.3 [16] Matrix_1.3-3 plyr_1.8.6 pkgconfig_2.0.3 [19] zlibbioc_1.38.0 purrr_0.3.4 patchwork_1.1.1 [22] tidytree_0.3.4 scales_1.1.1 ggplotify_0.1.0 [25] mgcv_1.8-35 generics_0.1.0 IRanges_2.26.0 [28] ellipsis_0.3.2 withr_2.4.2 BiocGenerics_0.38.0 [31] lazyeval_0.2.2 survival_3.2-11 crayon_1.4.1 [34] fansi_0.5.0 nlme_3.1-152 MASS_7.3-54 [37] vegan_2.5-7 tools_4.1.0 data.table_1.14.0 [40] lifecycle_1.0.0 stringr_1.4.0 aplot_0.1.0 [43] Rhdf5lib_1.14.2 S4Vectors_0.30.0 munsell_0.5.0 [46] cluster_2.1.2 Biostrings_2.60.2 ade4_1.7-17 [49] compiler_4.1.0 GenomeInfoDb_1.28.4 gridGraphics_0.5-1 [52] rlang_0.4.11 rhdf5_2.36.0 grid_4.1.0 [55] RCurl_1.98-1.4 iterators_1.0.13 rhdf5filters_1.4.0 [58] biomformat_1.20.0 rstudioapi_0.13 igraph_1.2.6 [61] bitops_1.0-7 gtable_0.3.0 codetools_0.2-18 [64] multtest_2.48.0 DBI_1.1.1 reshape2_1.4.4 [67] R6_2.5.1 utf8_1.2.2 treeio_1.16.2 [70] permute_0.9-5 ape_5.5 rJava_1.0-4 [73] stringi_1.7.4 parallel_4.1.0 Rcpp_1.0.7 [76] vctrs_0.3.8 tidyselect_1.1.1 `
LEfSe_Issue.zip