diff_analysis without rarefying

[cmetadea]

Hi, I was wondering if its possible to do diff analysis without rarefying?

I ran this script

mpse %<>% mp_diff_analysis(.abundance = RelAbundanceBySample,
                           .group = cagA,
                           first.test.alpha = .05, strict = FALSE, cl.min = 2, force = TRUE,
                           second.test.alpha = .05)

and the result is this ,

Error in `dplyr::select()`:
! Can't subset columns that don't exist.
✖ Column `RelAbundanceBySample` doesn't exist.
Run `rlang::last_trace()` to see where the error occurred.

> 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] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] TreeSummarizedExperiment_2.6.0 Biostrings_2.66.0              XVector_0.38.0                
 [4] SingleCellExperiment_1.20.1    SummarizedExperiment_1.28.0    Biobase_2.58.0                
 [7] GenomicRanges_1.50.2           GenomeInfoDb_1.34.9            IRanges_2.32.0                
[10] S4Vectors_0.36.2               BiocGenerics_0.44.0            MatrixGenerics_1.10.0         
[13] matrixStats_1.1.0              treeio_1.22.0                  phyloseq_1.42.0               
[16] tidyselect_1.2.0               lubridate_1.9.3                forcats_1.0.0                 
[19] stringr_1.5.1                  dplyr_1.1.4                    purrr_1.0.2                   
[22] readr_2.1.4                    tibble_3.2.1                   tidyverse_2.0.0               
[25] tidytree_0.4.5                 tidyr_1.3.0                    ggplotify_0.1.2               
[28] ggrastr_1.0.2                  ggpubr_0.6.0                   ggrepel_0.9.4                 
[31] ggsci_3.0.0                    ggside_0.2.2                   ggsignif_0.6.4                
[34] ggstar_1.0.4                   ggtreeExtra_1.8.1              ggtree_3.6.2                  
[37] ggnewscale_0.4.9               ggalluvial_0.12.5              ggbeeswarm_0.7.2              
[40] ggbreak_0.1.2                  ggfun_0.1.3                    ggh4x_0.2.6                   
[43] gghalves_0.1.4                 ggplot2_3.4.4                  ANCOMBC_2.0.3                 
[46] MicrobiotaProcess_1.13.3      

Any idea?