# General FAQs This document is a collection of frequently asked questions (FAQs) and their solutions regarding the use of the MicrobiomeStat package in R. These FAQs are based on common issues and inquiries encountered by users in the past. For additional questions or if you encounter new issues, please feel free to visit the GitHub issues section of the MicrobiomeStat repository at , where you can post your questions and receive support from the community and the package developers. ## I'm having an issue converting a phyloseq object to a MicrobiomeStat object using `mStat_convert_phyloseq_to_data_obj()` Despite installing the latest version of MicrobiomeStat from GitHub, I'm still getting the error "could not find function 'mStat\_convert\_phyloseq\_to\_data\_obj'". Any suggestions? **Solution:** It appears there may have been an issue with the installation. After downloading and installing, pay attention to the version numbers. The current CRAN version is 1.1, while the GitHub version is 1.1.3. If the installation of the GitHub version is unsuccessful, this issue may arise. Ensure you have installed all necessary dependencies before attempting to install from GitHub: ```r packages_to_install <- c( "rlang", "tibble", "ggplot2", "matrixStats", "lmerTest", "foreach", "modeest", "dplyr", "pheatmap", "tidyr", "ggh4x", "GUniFrac", "stringr", "rmarkdown", "knitr", "pander", "tinytex", "vegan", "scales", "ape", "ggrepel", "parallel", "ggprism", "aplot", "yaml", "biomformat", "Biostrings" ) install.packages(packages_to_install) ``` Then install MicrobiomeStat: `devtools::install_github("cafferychen777/MicrobiomeStat")` If issues persist, check for error messages during installation. ## Accessing Bray-Curtis distances from `generate_beta_ordination_pair` I successfully plotted Bray-Curtis distance over time, but cannot access the calculated distances. Will this option be included in the future? **Solution:** The distances are accessible in the object generated by `generate_beta_ordination_pair`: ```r plot <- generate_beta_ordination_pair(data.obj = your_data_object, ...) bray_curtis_data <- plot$BC$data ``` We continuously improve MicrobiomeStat and appreciate suggestions for new features. ## Difference between `generate_beta_ordination_pair` and `generate_beta_change_spaghettiplot_long` I can get BC distance data with the former but not the latter when `dist.name = c("BC")` is included. Are they equivalent? **Solution:** No, the two methods differ: * `generate_beta_ordination_pair` uses PC1 and PC2 from multidimensional scaling (MDS) * `generate_beta_change_spaghettiplot_long` calculates distances relative to original points To get BC distances from `generate_beta_change_spaghettiplot_long`: ```r a <- generate_beta_change_spaghettiplot_long(data.obj = your_data_object, dist.name = c("BC"), ...) a$BC$layers[[1]]$data ``` ## Error with `generate_beta_test_single()` When running the tutorial, I got: `Error in colnames<-(*tmp*, value = colnames(lhs)) : attempt to set 'colnames' on an object with less than two dimensions`. Any idea what's happening? **Solution:** This could be due to conflicts/issues with your setup. Try: 1. Reinstall MicrobiomeStat package 2. Reinstall GUniFrac package 3. Ensure latest versions of all dependencies If issues persist, provide more details on your system and error messages. ## Error with `generate_alpha_trend_test_long` I get `Error in vec_data() : ! 'x' must be a vector, not a object` when running this function. Time variable is numeric, any idea what's wrong? **Solution:** The `feature.tab`, `meta.dat`, and `feature.ann` components may not be in expected R matrix/data.frame format. Convert them: ```r ms_rare$feature.tab <- as.data.frame(ms_rare$feature.tab) %>% as.matrix() ms_rare$meta.dat <- as.matrix(ms_rare$meta.dat) %>% as.data.frame() ms_rare$feature.ann <- ms_rare$feature.ann %>% as.data.frame() %>% as.matrix() ``` This should ensure the components are in the correct format. Reach out if issues persist. ## Error normalizing data with GMPR method I get `Error in GMPR(otu_tab) : could not find function "GMPR"` when trying to normalize using the GMPR method. How to resolve? **Solution:** This error occurs when the required GUniFrac package is not loaded. Load it first: ```r library(GUniFrac) ``` Then you should be able to normalize using GMPR without the error. ## Missing timepoints in `generate_taxa_areaplot_long` visualization I'm missing some timepoints and `subject.var` values when visualizing taxa changes across timepoints. My timepoints are 0, 1, 2, 3, 5, 6, 7, 8, 10, 12, 18, 24 (not all present in each sample). Suggestions? **Solution:** Try pairing `mStat_subset_data` with `generate_taxa_areaplot_long` in a loop to generate plots for each subject: ```r data(subset_T2D.obj) unique.subject.id <- subset_T2D.obj$meta.dat$subject_id plot.list <- lapply(unique.subject.id, function(subject.id){ sample.ids <- rownames(subset_T2D.obj$meta.dat[subset_T2D.obj$meta.dat$subject_id == subject.id, ]) sub_subset_T2D.obj <- mStat_subset_data(subset_T2D.obj, sample.ids) generate_taxa_areaplot_long(sub_subset_T2D.obj, subject.var = "subject_id", time.var = "visit_number_num", feature.level = c("Genus"), feature.dat.type = "count", file.ann = subject.id) }) ``` Modify `group.var` as needed to group data by specific variables. ## Reason for `feature.mt.method` option in `generate_taxa_volcano_single()` The `linda()` function already accounts for FDR, but `generate_taxa_volcano_single()` has a `feature.mt.method` option to use "none" or "fdr". Why add another FDR layer? **Solution:** FDR adjustment is crucial for high-dimensional data to control multiple testing issues, which `linda()` inherently does. The `feature.mt.method` parameter allows users to choose between adjusted p-values (via FDR) or raw p-values for visualization in the volcano plot. Using raw p-values is for aesthetic reasons to improve plot clarity and interpretability. While FDR adjustment is integral to MicrobiomeStat's statistical procedures, `feature.mt.method` offers flexibility in visualization without compromising statistical rigor. ## Issue with Corrupted Lazy-Load Database for MicrobiomeStat Package **Problem:** When running functions from the MicrobiomeStat package, the following error is encountered: ``` Warning: restarting interrupted promise evaluation Warning: internal error -3 in R_decompress1 Error in FUN(X[[i]], ...) : The lazy-load database '/R/x86_64-pc-linux-gnu-library/4.2/MicrobiomeStat/R/MicrobiomeStat.rdb' is corrupted. ``` **Solution:** 1. Restart the R session: This often resolves issues related to the lazy-load database. 2. Reinstall the MicrobiomeStat package: If restarting the R session does not work, try reinstalling the package using: ```r devtools::install_github("cafferychen777/MicrobiomeStat") ``` If these steps do not resolve the issue, please provide more detailed information for further troubleshooting. ## Windows Installation Error: "System command 'Rcmd.exe' failed" **Problem:** Users encountering "System command 'Rcmd.exe' failed" error or issues with file path lengths during Windows installation. **Solution:** Several approaches can resolve this issue: 1. Using pak Package (Recommended): ```r if (!requireNamespace("pak", quietly = TRUE)) { install.packages("pak") } pak::pkg_install("cafferychen777/MicrobiomeStat", dependencies = TRUE) ``` 2. Using Shorter Temporary Directory Path: ```r # Set temporary directory to a shorter path dir.create("C:/Rtemp", showWarnings = FALSE) Sys.setenv(TMPDIR = "C:/Rtemp") # Install the package devtools::install_github("cafferychen777/MicrobiomeStat", build_vignettes = FALSE) ``` 3. Modify Windows Path Length Limit: * Open PowerShell as Administrator * Run: `Set-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1` * Restart computer If issues persist, check: ```r # Check temporary directory Sys.getenv("TMPDIR") # Check working directory getwd() ``` Ensure latest Rtools is installed and try running R/RStudio as administrator.