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Encounter errors in diff_analysis #40

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Turbinenoli opened this issue Jan 13, 2022 · 6 comments
Closed

Encounter errors in diff_analysis #40

Turbinenoli opened this issue Jan 13, 2022 · 6 comments

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@Turbinenoli
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Turbinenoli commented Jan 13, 2022 •
edited
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Hello all

First of all, I really appreciate this package and so far I like it a lot, thank you for your work. It's my first try to get an issue solved via the github community and I will try my best to make it reproducible. It think my issue is similar to this closed issue here -> #Error in seq_len(ncol(taxdf)) : argument must be coercible to non-negative integer #13

My issue: I'd like to make a diff_analysis and later process it to ggdiffclade but fail to produce the object "deres <- diff_analysis(......). The code works just fine when using the provided example data. However, my command stops with following error message:

deres_dud <- diff_analysis(obj = lime_dud, classgroup = "plot", #this is my ps object and relevant factors

  •                    mlfun = "lda",

  •                    filtermod = "pvalue",

  •                    firstcomfun = "kruskal_test",

  •                    firstalpha = 0.05,

  •                    strictmod = TRUE,

  •                    secondcomfun = "wilcox_test",

  •                    subclmin = 3,

  •                    subclwilc = TRUE,

  •                    secondalpha = 0.01,

  •                    lda=3)

Error in seq_len(ncol(taxdf)) :
argument must be coercible to non-negative integer
In addition: Warning message:
In seq_len(ncol(taxdf)) : first element used of 'length.out' argument

My assumption is, that the code can not handle certain structures within my data, more precisely the tax_table within my phyloseq object, as unassigned taxa get the entry e.g "g__" instead of "g__Gaiella".

My ps object consists of an otu_table(), tax_table() and sample_data() and looks like this:

lime_dud
phyloseq-class experiment-level object
otu_table() OTU Table: [ 28139 taxa and 27 samples ]
sample_data() Sample Data: [ 27 samples by 5 sample variables ]
tax_table() Taxonomy Table: [ 28139 taxa by 6 taxonomic ranks ]

head(otu_table(lime_dud))[,1:5]
OTU Table: [5 taxa and 6 samples]
taxa are columns
ASV1 ASV2 ASV3 ASV4 ASV5
D1-1 351 133 300 272 139
D1-2 361 205 339 215 179
D1-3 414 236 418 268 245
D1-4 385 210 441 272 295
D1-5 324 165 355 242 196
D1-6 359 179 378 293 185

head(tax_table(lime_dud))[1:5,]
Taxonomy Table: [5 taxa by 6 taxonomic ranks]:
Kingdom Phylum Class Order Family Genus
ASV1 "k__Bacteria" "p__Proteobacteria" "c__Alphaproteobacteria" "o__Rhizobiales" "f__Xanthobacteraceae" "g__"
ASV2 "k__Bacteria" "p__Proteobacteria" "c__Alphaproteobacteria" "o__Rhizobiales" "f__Methyloligellaceae" "g__"
ASV3 "k__Bacteria" "p__Proteobacteria" "c__Alphaproteobacteria" "o__Rhizobiales" "f__Methyloligellaceae" "g__"
ASV4 "k__Bacteria" "p__Nitrospirota" "c__Nitrospiria" "o__Nitrospirales" "f__Nitrospiraceae" "g__Nitrospira"
ASV5 "k__Bacteria" "p__Proteobacteria" "c__Alphaproteobacteria" "o__Rhizobiales" "f__Xanthobacteraceae" "g__Bradyrhizobium"

head(sample_data(lime_dud))[1:6,1:5]
Sample Data: [6 samples by 5 sample variables]:
sample_ID location plot run ph
D1-1 D1BCS1 Düdingen BCS 1 6.0
D1-2 D1BCS2 Düdingen BCS 1 5.9
D1-3 D1BCS3 Düdingen BCS 1 5.8
D1-4 D1BBD1 Düdingen BBD 1 5.9
D1-5 D1BBD2 Düdingen BBD 1 5.9
D1-6 D1BBD3 Düdingen BBD 1 5.9

I tried several work-arounds such as converting my ps object to MPSE via the as.MPSE() command but keep getting the same error message:

mpse_dud <- lime_dud %>% as.MPSE()
Error in seq_len(ncol(taxdf)) :
argument must be coercible to non-negative integer
In addition: Warning message:
In seq_len(ncol(taxdf)) : first element used of 'length.out' argument

Any help is certainly appreciated, if the example lacks sufficient information to reproduce or understand my issue I am glad to provide further infos. Thanks in advance and best regards
@xiangpin
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The example does lack sufficient information, I cannot reproduce your issue. But I can build an MPSE class with the provided information (See the following). Would you mind sending me the dataset by email? I will not distribute this data without your permission. My email is xshuangbin@163.com

> library(MicrobiotaProcess) 
> otutab <- read.table("./otu_tab.txt", header = T, row.names = 1)
> otutab
     ASV1 ASV2 ASV3 ASV4 ASV5
D1-1  351  133  300  272  139
D1-2  361  205  339  215  179
D1-3  414  236  418  268  245
D1-4  385  210  441  272  295
D1-5  324  165  355  242  196
D1-6  359  179  378  293  185
> taxatab <- read.table("./taxa_tab.txt", header = T, row.names = 1) 
> taxatab
         Kingdom            Phylum                  Class            Order
ASV1 k__Bacteria p__Proteobacteria c__Alphaproteobacteria   o__Rhizobiales
ASV2 k__Bacteria p__Proteobacteria c__Alphaproteobacteria   o__Rhizobiales
ASV3 k__Bacteria p__Proteobacteria c__Alphaproteobacteria   o__Rhizobiales
ASV4 k__Bacteria   p__Nitrospirota         c__Nitrospiria o__Nitrospirales
ASV5 k__Bacteria p__Proteobacteria c__Alphaproteobacteria   o__Rhizobiales
                    Family             Genus
ASV1  f__Xanthobacteraceae               g__
ASV2 f__Methyloligellaceae               g__
ASV3 f__Methyloligellaceae               g__
ASV4     f__Nitrospiraceae     g__Nitrospira
ASV5  f__Xanthobacteraceae g__Bradyrhizobium
> sampleda <- read.table("./sample_tab.txt", header = T, row.names = 1) 
> sampled
     sample_ID location plot run  ph
D1-1    D1BCS1 Düdingen  BCS   1 6.0
D1-2    D1BCS2 Düdingen  BCS   1 5.9
D1-3    D1BCS3 Düdingen  BCS   1 5.8
D1-4    D1BBD1 Düdingen  BBD   1 5.9
D1-5    D1BBD2 Düdingen  BBD   1 5.9
D1-6    D1BBD3 Düdingen  BBD   1 5.9
> mpse <- MPSE(assays=list(Abundance=t(otutab)), colData=sampleda)
> mpse
# A MPSE-tibble (MPSE object) abstraction: 30 × 8
# OTU=5 | Samples=6 | Assays=Abundance | Taxanomy=NULL
   OTU   Sample Abundance sample_ID location plot    run    ph
   <chr> <chr>      <int> <chr>     <chr>    <chr> <int> <dbl>
 1 ASV1  D1-1         351 D1BCS1    Düdingen BCS       1   6
 2 ASV1  D1-2         361 D1BCS2    Düdingen BCS       1   5.9
 3 ASV1  D1-3         414 D1BCS3    Düdingen BCS       1   5.8
 4 ASV2  D1-1         133 D1BCS1    Düdingen BCS       1   6
 5 ASV2  D1-2         205 D1BCS2    Düdingen BCS       1   5.9
 6 ASV2  D1-3         236 D1BCS3    Düdingen BCS       1   5.8
 7 ASV3  D1-1         300 D1BCS1    Düdingen BCS       1   6
 8 ASV3  D1-2         339 D1BCS2    Düdingen BCS       1   5.9
 9 ASV3  D1-3         418 D1BCS3    Düdingen BCS       1   5.8
10 ASV4  D1-1         272 D1BCS1    Düdingen BCS       1   6
# … with 20 more rows
> taxonomy(mpse) <- taxatab
> mpse
# A MPSE-tibble (MPSE object) abstraction: 30 × 14
# OTU=5 | Samples=6 | Assays=Abundance | Taxanomy=Kingdom, Phylum, Class, Order, Family, Genus
   OTU   Sample Abundance sample_ID location plot    run    ph Kingdom  Phylum
   <chr> <chr>      <int> <chr>     <chr>    <chr> <int> <dbl> <chr>    <chr>
 1 ASV1  D1-1         351 D1BCS1    Düdingen BCS       1   6   k__Bact… p__Prot…
 2 ASV1  D1-2         361 D1BCS2    Düdingen BCS       1   5.9 k__Bact… p__Prot…
 3 ASV1  D1-3         414 D1BCS3    Düdingen BCS       1   5.8 k__Bact… p__Prot…
 4 ASV2  D1-1         133 D1BCS1    Düdingen BCS       1   6   k__Bact… p__Prot…
 5 ASV2  D1-2         205 D1BCS2    Düdingen BCS       1   5.9 k__Bact… p__Prot…
 6 ASV2  D1-3         236 D1BCS3    Düdingen BCS       1   5.8 k__Bact… p__Prot…
 7 ASV3  D1-1         300 D1BCS1    Düdingen BCS       1   6   k__Bact… p__Prot…
 8 ASV3  D1-2         339 D1BCS2    Düdingen BCS       1   5.9 k__Bact… p__Prot…
 9 ASV3  D1-3         418 D1BCS3    Düdingen BCS       1   5.8 k__Bact… p__Prot…
10 ASV4  D1-1         272 D1BCS1    Düdingen BCS       1   6   k__Bact… p__Nitr…
# … with 20 more rows, and 4 more variables: Class <chr>, Order <chr>,
#   Family <chr>, Genus <chr>

@xiangpin
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By the way, the following is my R session information. And the version of MicrobiotaProcess is the newest (1.7.5)

> sessioninfo::session_info()
─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.1.1 (2021-08-10)
 os       Ubuntu 18.04.4 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Asia/Shanghai
 date     2022-01-13

─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
 package              * version    date       lib source
 ape                    5.6-1      2022-01-07 [1] CRAN (R 4.1.1)
 aplot                  0.1.2      2022-01-10 [1] CRAN (R 4.1.1)
 assertthat             0.2.1      2019-03-21 [1] CRAN (R 4.1.1)
 Biobase                2.54.0     2021-10-26 [1] Bioconductor
 BiocGenerics           0.40.0     2021-10-26 [1] Bioconductor
 BiocManager            1.30.16    2021-06-15 [1] CRAN (R 4.1.1)
 Biostrings             2.62.0     2021-10-26 [1] Bioconductor
 bitops                 1.0-7      2021-04-24 [1] CRAN (R 4.1.1)
 cachem                 1.0.6      2021-08-19 [1] CRAN (R 4.1.1)
 cli                    3.1.0      2021-10-27 [1] CRAN (R 4.1.1)
 cluster                2.1.2      2021-04-17 [1] CRAN (R 4.1.1)
 codetools              0.2-18     2020-11-04 [1] CRAN (R 4.1.1)
 coin                   1.4-2      2021-10-08 [1] CRAN (R 4.1.1)
 colorspace             2.0-2      2021-06-24 [1] CRAN (R 4.1.1)
 conflicted           * 1.0.4      2019-06-21 [1] CRAN (R 4.1.1)
 crayon                 1.4.2      2021-10-29 [1] CRAN (R 4.1.1)
 DBI                    1.1.2      2021-12-20 [1] CRAN (R 4.1.1)
 DelayedArray           0.20.0     2021-10-26 [1] Bioconductor
 dplyr                  1.0.7      2021-06-18 [1] CRAN (R 4.1.1)
 ellipsis               0.3.2      2021-04-29 [1] CRAN (R 4.1.1)
 fansi                  1.0.0      2022-01-10 [1] CRAN (R 4.1.1)
 fastmap                1.1.0      2021-01-25 [1] CRAN (R 4.1.1)
 foreach                1.5.1      2020-10-15 [1] CRAN (R 4.1.1)
 generics               0.1.1      2021-10-25 [1] CRAN (R 4.1.1)
 GenomeInfoDb           1.30.0     2021-10-26 [1] Bioconductor
 GenomeInfoDbData       1.2.7      2021-10-29 [1] Bioconductor
 GenomicRanges          1.46.0     2021-10-26 [1] Bioconductor
 ggfun                  0.0.4      2021-09-17 [1] CRAN (R 4.1.1)
 ggnewscale             0.4.5      2021-01-11 [1] CRAN (R 4.1.1)
 ggplot2                3.3.5      2021-06-25 [1] CRAN (R 4.1.1)
 ggplotify              0.1.0      2021-09-02 [1] CRAN (R 4.1.1)
 ggrepel                0.9.1      2021-01-15 [1] CRAN (R 4.1.1)
 ggsignif               0.6.3      2021-09-09 [1] CRAN (R 4.1.1)
 ggstar                 1.0.3      2021-12-03 [1] CRAN (R 4.1.1)
 ggtree                 3.3.1      2021-12-31 [1] Bioconductor
 ggtreeExtra            1.5.1      2021-11-24 [1] Bioconductor
 glue                   1.6.0      2021-12-17 [1] CRAN (R 4.1.1)
 gridExtra              2.3        2017-09-09 [1] CRAN (R 4.1.1)
 gridGraphics           0.5-1      2020-12-13 [1] CRAN (R 4.1.1)
 gtable                 0.3.0      2019-03-25 [1] CRAN (R 4.1.1)
 IRanges                2.28.0     2021-10-26 [1] Bioconductor
 iterators              1.0.13     2020-10-15 [1] CRAN (R 4.1.1)
 jsonlite               1.7.2      2020-12-09 [1] CRAN (R 4.1.1)
 lattice                0.20-45    2021-09-22 [1] CRAN (R 4.1.1)
 lazyeval               0.2.2      2019-03-15 [1] CRAN (R 4.1.1)
 libcoin                1.0-9      2021-09-27 [1] CRAN (R 4.1.1)
 lifecycle              1.0.1      2021-09-24 [1] CRAN (R 4.1.1)
 magrittr             * 2.0.1      2020-11-17 [1] CRAN (R 4.1.1)
 MASS                   7.3-54     2021-05-03 [1] CRAN (R 4.1.1)
 Matrix                 1.3-4      2021-06-01 [1] CRAN (R 4.1.1)
 MatrixGenerics         1.6.0      2021-10-26 [1] Bioconductor
 matrixStats            0.61.0     2021-09-17 [1] CRAN (R 4.1.1)
 mgcv                   1.8-38     2021-10-06 [1] CRAN (R 4.1.1)
 MicrobiotaProcess    * 1.7.5      2021-12-31 [1] Bioconductor
 modeltools             0.2-23     2020-03-05 [1] CRAN (R 4.1.1)
 multcomp               1.4-17     2021-04-29 [1] CRAN (R 4.1.1)
 munsell                0.5.0      2018-06-12 [1] CRAN (R 4.1.1)
 mvtnorm                1.1-3      2021-10-08 [1] CRAN (R 4.1.1)
 nlme                   3.1-153    2021-09-07 [1] CRAN (R 4.1.1)
 patchwork              1.1.1      2020-12-17 [1] CRAN (R 4.1.1)
 permute                0.9-5      2019-03-12 [1] CRAN (R 4.1.1)
 pillar                 1.6.4      2021-10-18 [1] CRAN (R 4.1.1)
 pkgconfig              2.0.3      2019-09-22 [1] CRAN (R 4.1.1)
 purrr                  0.3.4      2020-04-17 [1] CRAN (R 4.1.1)
 R6                     2.5.1      2021-08-19 [1] CRAN (R 4.1.1)
 Rcpp                   1.0.7      2021-07-07 [1] CRAN (R 4.1.1)
 RCurl                  1.98-1.5   2021-09-17 [1] CRAN (R 4.1.1)
 rlang                  0.4.12     2021-10-18 [1] CRAN (R 4.1.1)
 rvcheck              * 0.2.0      2021-09-14 [1] CRAN (R 4.1.1)
 S4Vectors              0.32.0     2021-10-26 [1] Bioconductor
 sandwich               3.0-1      2021-05-18 [1] CRAN (R 4.1.1)
 scales                 1.1.1      2020-05-11 [1] CRAN (R 4.1.1)
 sessioninfo            1.1.1      2018-11-05 [1] CRAN (R 4.1.1)
 SummarizedExperiment   1.24.0     2021-10-26 [1] Bioconductor
 survival               3.2-13     2021-08-24 [1] CRAN (R 4.1.1)
 TH.data                1.1-0      2021-09-27 [1] CRAN (R 4.1.1)
 tibble                 3.1.6      2021-11-07 [1] CRAN (R 4.1.1)
 tidyr                  1.1.4.9000 2022-01-12 [1] Github (tidyverse/tidyr@3abfa55)
 tidyselect             1.1.1      2021-04-30 [1] CRAN (R 4.1.1)
 tidytree               0.3.7      2022-01-10 [1] CRAN (R 4.1.1)
 treeio                 1.18.0     2021-10-26 [1] Bioconductor
 utf8                   1.2.2      2021-07-24 [1] CRAN (R 4.1.1)
 vctrs                  0.3.8      2021-04-29 [1] CRAN (R 4.1.1)
 vegan                  2.5-7      2020-11-28 [1] CRAN (R 4.1.1)
 wget                 * 0.0.1      2021-12-06 [1] local
 withr                  2.4.3      2021-11-30 [1] CRAN (R 4.1.1)
 XVector                0.34.0     2021-10-26 [1] Bioconductor
 yulab.utils            0.0.4      2021-10-09 [1] CRAN (R 4.1.1)
 zlibbioc               1.40.0     2021-10-26 [1] Bioconductor
 zoo                    1.8-9      2021-03-09 [1] CRAN (R 4.1.1)

[1] /mnt/d/UbuntuApps/R/4.1.1/lib/R/library

@Turbinenoli
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Hey, thank you for your fast answer and your suggested approach.

I will just quickly double check your R session information with mine to make sure I used the right libraries. Furthermore I try to reproduce your suggestion above to convert it to MPSE.

In case it should fail again, I will send you my metadata and the code used to produce the ps object "lime" and it's location based subset "lime_dud" via e-mail. I appreciate your willingness to help a lot!

Cheers and I keep you posted

@Turbinenoli
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Turbinenoli commented Jan 13, 2022 •
edited
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Hello again

I encounter the same error on the command:> taxonomy(mpse) <- taxatab and you should have received a mail with my metadata. The packages check out so far, but still here is my session info:

 sessioninfo::session_info()
- Session info ---------------------------------------------------------------------------------------------------------------------------------------
 setting  value
 version  R version 4.1.1 (2021-08-10)
 os       Windows 10 x64 (build 19043)
 system   x86_64, mingw32
 ui       RStudio
 language (EN)
 collate  German_Switzerland.1252
 ctype    German_Switzerland.1252
 tz       Europe/Berlin
 date     2022-01-13
 rstudio  2021.09.0+351 Ghost Orchid (desktop)
 pandoc   2.14.0.3 @ C:/Program Files/RStudio/bin/pandoc/ (via rmarkdown)

- Packages -------------------------------------------------------------------------------------------------------------------------------------------
 ! package              * version    date (UTC) lib source
   abind                  1.4-5      2016-07-21 [1] CRAN (R 4.1.1)
   ade4                   1.7-18     2021-09-16 [1] CRAN (R 4.1.1)
   ape                    5.5        2021-04-25 [1] CRAN (R 4.1.1)
   aplot                  0.1.2      2022-01-10 [1] CRAN (R 4.1.1)
   assertthat             0.2.1      2019-03-21 [1] CRAN (R 4.1.1)
   backports              1.4.1      2021-12-13 [1] CRAN (R 4.1.2)
   Biobase                2.52.0     2021-05-19 [1] Bioconductor
   BiocGenerics           0.40.0     2021-10-26 [1] Bioconductor
   BiocManager            1.30.16    2021-06-15 [1] CRAN (R 4.1.1)
   biomformat             1.20.0     2021-05-19 [1] Bioconductor
   Biostrings             2.60.2     2021-08-05 [1] Bioconductor
   bitops                 1.0-7      2021-04-24 [1] CRAN (R 4.1.1)
   broom                  0.7.11     2022-01-03 [1] CRAN (R 4.1.2)
   car                    3.0-12     2021-11-06 [1] CRAN (R 4.1.2)
   carData                3.0-5      2022-01-06 [1] CRAN (R 4.1.2)
   caret                  6.0-90     2021-10-09 [1] CRAN (R 4.1.1)
   cellranger             1.1.0      2016-07-27 [1] CRAN (R 4.1.1)
   class                  7.3-19     2021-05-03 [2] CRAN (R 4.1.1)
   cli                    3.1.0      2021-10-27 [1] CRAN (R 4.1.1)
   cluster                2.1.2      2021-04-17 [2] CRAN (R 4.1.1)
   codetools              0.2-18     2020-11-04 [2] CRAN (R 4.1.1)
   coin                 * 1.4-2      2021-10-08 [1] CRAN (R 4.1.2)
   colorspace             2.0-2      2021-06-24 [1] CRAN (R 4.1.1)
   conquer                1.2.1      2021-11-01 [1] CRAN (R 4.1.2)
   crayon                 1.4.2      2021-10-29 [1] CRAN (R 4.1.1)
   data.table             1.14.2     2021-09-27 [1] CRAN (R 4.1.1)
   DBI                    1.1.2      2021-12-20 [1] CRAN (R 4.1.2)
   dbplyr                 2.1.1      2021-04-06 [1] CRAN (R 4.1.1)
   DelayedArray           0.18.0     2021-05-19 [1] Bioconductor
   digest                 0.6.29     2021-12-01 [1] CRAN (R 4.1.2)
   dplyr                * 1.0.7      2021-06-18 [1] CRAN (R 4.1.1)
   ellipsis               0.3.2      2021-04-29 [1] CRAN (R 4.1.1)
   evaluate               0.14       2019-05-28 [1] CRAN (R 4.1.1)
   extrafont              0.17       2014-12-08 [1] CRAN (R 4.1.1)
   extrafontdb            1.0        2012-06-11 [1] CRAN (R 4.1.1)
   fansi                  0.5.0      2021-05-25 [1] CRAN (R 4.1.1)
   farver                 2.1.0      2021-02-28 [1] CRAN (R 4.1.1)
   fastmap                1.1.0      2021-01-25 [1] CRAN (R 4.1.1)
   forcats              * 0.5.1      2021-01-27 [1] CRAN (R 4.1.1)
   foreach                1.5.1      2020-10-15 [1] CRAN (R 4.1.1)
   fs                     1.5.2      2021-12-08 [1] CRAN (R 4.1.2)
   future                 1.23.0     2021-10-31 [1] CRAN (R 4.1.1)
   future.apply           1.8.1      2021-08-10 [1] CRAN (R 4.1.1)
   gdtools                0.2.3      2021-01-06 [1] CRAN (R 4.1.1)
   generics               0.1.1      2021-10-25 [1] CRAN (R 4.1.1)
   GenomeInfoDb           1.30.0     2021-10-26 [1] Bioconductor
   GenomeInfoDbData       1.2.7      2022-01-13 [1] Bioconductor
   GenomicRanges          1.44.0     2021-05-19 [1] Bioconductor
   ggforce              * 0.3.3      2021-03-05 [1] CRAN (R 4.1.2)
   ggfun                  0.0.4      2021-09-17 [1] CRAN (R 4.1.2)
   ggnewscale             0.4.5      2021-01-11 [1] CRAN (R 4.1.2)
   ggplot2              * 3.3.5      2021-06-25 [1] CRAN (R 4.1.1)
   ggplotify              0.1.0      2021-09-02 [1] CRAN (R 4.1.2)
   ggpmisc              * 0.4.5      2021-12-11 [1] CRAN (R 4.1.2)
   ggpp                 * 0.4.3      2021-12-17 [1] CRAN (R 4.1.2)
   ggpubr               * 0.4.0      2020-06-27 [1] CRAN (R 4.1.1)
   ggrepel                0.9.1      2021-01-15 [1] CRAN (R 4.1.1)
   ggsignif               0.6.3      2021-09-09 [1] CRAN (R 4.1.1)
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 [1] C:/Users/Simon/Documents/R/win-library/4.1
 [2] C:/Program Files/R/R-4.1.1/library

 D -- DLL MD5 mismatch, broken installation.

@xiangpin
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I checked the taxonomy table and found some Kingdom of ASVs is k__, which means the annotation of kingdom level is Unknown. The original version cannot handle this. Now, this issue was fixed by github version. You can reinstall it via remotes::install_github("YuLab-SMU/MicrobiotaProcess")

> library(MicrobiotaProcess)
> otuda <- read.csv("./otu_table.csv", row.names=1)
> taxada <- read.csv("./tax_table.csv", row.names=1)
> taxada %>% filter(Kingdom=="k__")
         Kingdom Phylum Class Order Family Genus
ASV23455     k__    p__   c__   o__    f__   g__
ASV24768     k__    p__   c__   o__    f__   g__
ASV24998     k__    p__   c__   o__    f__   g__
ASV25075     k__    p__   c__   o__    f__   g__
ASV26098     k__    p__   c__   o__    f__   g__
ASV26141     k__    p__   c__   o__    f__   g__
ASV26149     k__    p__   c__   o__    f__   g__
ASV26302     k__    p__   c__   o__    f__   g__
ASV26318     k__    p__   c__   o__    f__   g__
ASV26466     k__    p__   c__   o__    f__   g__
ASV26526     k__    p__   c__   o__    f__   g__
ASV26527     k__    p__   c__   o__    f__   g__
ASV26848     k__    p__   c__   o__    f__   g__
ASV26912     k__    p__   c__   o__    f__   g__
ASV26931     k__    p__   c__   o__    f__   g__
ASV27027     k__    p__   c__   o__    f__   g__
ASV27040     k__    p__   c__   o__    f__   g__
ASV27165     k__    p__   c__   o__    f__   g__
> mpse <- MPSE(assays=list(Abundance=t(otuda)))
> mpse
# A MPSE-tibble (MPSE object) abstraction: 1,519,506 × 3
# OTU=28139 | Samples=54 | Assays=Abundance | Taxonomy=NULL
   OTU   Sample Abundance
   <chr> <chr>      <int>
 1 ASV1  S1-1         204
 2 ASV2  S1-1         269
 3 ASV3  S1-1           0
 4 ASV4  S1-1         113
 5 ASV5  S1-1          67
 6 ASV6  S1-1          54
 7 ASV7  S1-1         127
 8 ASV8  S1-1         103
 9 ASV9  S1-1           0
10 ASV10 S1-1           0
# … with 1,519,496 more rows
> taxonomy(mpse) <- taxada
> mpse
# A MPSE-tibble (MPSE object) abstraction: 1,519,506 × 9
# OTU=28139 | Samples=54 | Assays=Abundance | Taxonomy=Kingdom, Phylum, Class, Order, Family, Genus
   OTU   Sample Abundance Kingdom     Phylum   Class    Order   Family   Genus
   <chr> <chr>      <int> <chr>       <chr>    <chr>    <chr>   <chr>    <chr>
 1 ASV1  S1-1         204 k__Bacteria p__Prot… c__Alph… o__Rhi… f__Xant… g__un_…
 2 ASV2  S1-1         269 k__Bacteria p__Prot… c__Alph… o__Rhi… f__Meth… g__un_…
 3 ASV3  S1-1           0 k__Bacteria p__Prot… c__Alph… o__Rhi… f__Meth… g__un_…
 4 ASV4  S1-1         113 k__Bacteria p__Nitr… c__Nitr… o__Nit… f__Nitr… g__Nit…
 5 ASV5  S1-1          67 k__Bacteria p__Prot… c__Alph… o__Rhi… f__Xant… g__Bra…
 6 ASV6  S1-1          54 k__Bacteria p__Prot… c__Alph… o__Rhi… f__Xant… g__un_…
 7 ASV7  S1-1         127 k__Bacteria p__Myxo… c__bact… o__un_… f__un_c… g__un_…
 8 ASV8  S1-1         103 k__Bacteria p__Acti… c__Acti… o__Mic… f__Micr… g__Pse…
 9 ASV9  S1-1           0 k__Bacteria p__Prot… c__Gamm… o__Bur… f__SC-I… g__un_…
10 ASV10 S1-1           0 k__Bacteria p__Chlo… c__Chlo… o__Chl… f__Rose… g__un_…
# … with 1,519,496 more rows

Or you can use convert_to_treedata to convert the taxada to a treedata class first, then specifying the taxatree in MPSE function.

> taxa.tree <- taxada %>% convert_to_treedata(include.rownames=T)
> mpse2 <- MPSE(assays=list(Abundance=t(otuda)), taxatree=taxa.tree)
> identical(mpse, mpse2)

Then you can use as.phyloseq to convert the MPSE class to phyloseq class, if you want to do it.

ps <- mpse %>% as.phyloseq(.abundance=Abundance)
ps

@Turbinenoli
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image

Thank you very much for taking your time and the adaptation to the package. It worked right from the start and I am therefore closing this issue. Looking forward to do more work with MicrobiotaProcess!

Cheers and best regards

Simon

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