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grunwaldlab / metacoder

Parsing, Manipulation, and Visualization of Metabarcoding/Taxonomic data
http://grunwaldlab.github.io/metacoder_documentation
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Running into error when plotting alpha diversity indexes #365

Open ChathuZ opened 3 weeks ago

ChathuZ commented 3 weeks ago

Here is the error

> ps_obj <- as_phyloseq(obj,
+                       otu_table = presence_data,
+                       otu_id_col = "#OTU ID",
+                       sample_data = sample_data,
+                       sample_id_col = "SampleID")
Error in as_phyloseq(obj, otu_table = presence_data, otu_id_col = "#OTU ID",  : 
  OTU table does not have an OTU ID column named "#OTU ID". Use the "otu_id_col" option if it is named something else.

Here is the full console results up to the error 

> #Data quality control
> library(metacoder)
> obj$data$otu_counts <- zero_low_counts(obj, "otu_counts", min_count = 10,
+                                        other_cols = TRUE) # keep OTU_ID column
No `cols` specified, so using all numeric columns:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

No counts found less than 10.
Warning message:
The following columns will be replaced in the output:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

> 
> print(obj)
<Taxmap>
  1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
  1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
  3 data sets:
    otu_counts:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        22         0         0         0         0         0         0         0        12
      2 avu      cb5168e06325147…        13        10        10         0         0         0        12        14        13
      3 aet      3968cb5b7766a80…        89        13        15        16        22        40        31        14         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_rarefied:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        14         0         0         0         0         0         0         0         7
      2 avu      cb5168e06325147…         9        10         6         0         0         0         7         3         6
      3 aet      3968cb5b7766a80…        49         9         9        12         9        31        18         4         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_props:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…  0.000851  0         0         0         0          0        0         0         0.000387
      2 avu      cb5168e06325147…  0.000503  0.000359  0.000310  0         0          0        0.000474  0.000440  0.000420
      3 aet      3968cb5b7766a80…  0.00344   0.000466  0.000465  0.000544  0.000741   0.00161  0.00122   0.000440  0       
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
  0 functions:
> 
> no_reads <- rowSums(obj$data$otu_counts[, sample_data$SampleID]) == 0
> sum(no_reads) # when `sum` is used on a TRUE/FALSE vector it counts TRUEs
[1] 0
> 
> obj <- filter_obs(obj, "otu_counts", ! no_reads, drop_taxa = TRUE)
> print(obj)
<Taxmap>
  1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
  1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
  3 data sets:
    otu_counts:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        22         0         0         0         0         0         0         0        12
      2 avu      cb5168e06325147…        13        10        10         0         0         0        12        14        13
      3 aet      3968cb5b7766a80…        89        13        15        16        22        40        31        14         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_rarefied:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        14         0         0         0         0         0         0         0         7
      2 avu      cb5168e06325147…         9        10         6         0         0         0         7         3         6
      3 aet      3968cb5b7766a80…        49         9         9        12         9        31        18         4         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_props:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…  0.000851  0         0         0         0          0        0         0         0.000387
      2 avu      cb5168e06325147…  0.000503  0.000359  0.000310  0         0          0        0.000474  0.000440  0.000420
      3 aet      3968cb5b7766a80…  0.00344   0.000466  0.000465  0.000544  0.000741   0.00161  0.00122   0.000440  0       
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
  0 functions:
> 
> 
> hist(colSums(obj$data$otu_counts[, sample_data$SampleID]))
> 
> obj$data$otu_rarefied <- rarefy_obs(obj, "otu_counts", other_cols = TRUE)
No `cols` specified, so using all numeric columns:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

Rarefying to 16141 since that is the lowest sample total.
Warning messages:
1: In vegan::rrarefy(t(count_table), sample = sample_size) :
  function should be used for observed counts, but smallest count is 10
2: The following columns will be replaced in the output:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

> print(obj)
<Taxmap>
  1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
  1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
  3 data sets:
    otu_counts:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        22         0         0         0         0         0         0         0        12
      2 avu      cb5168e06325147…        13        10        10         0         0         0        12        14        13
      3 aet      3968cb5b7766a80…        89        13        15        16        22        40        31        14         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_rarefied:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        14         0         0         0         0         0         0         0         4
      2 avu      cb5168e06325147…         7         5         5         0         0         0         8         7         6
      3 aet      3968cb5b7766a80…        60         7         6        11        15        23        18        11         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_props:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…  0.000851  0         0         0         0          0        0         0         0.000387
      2 avu      cb5168e06325147…  0.000503  0.000359  0.000310  0         0          0        0.000474  0.000440  0.000420
      3 aet      3968cb5b7766a80…  0.00344   0.000466  0.000465  0.000544  0.000741   0.00161  0.00122   0.000440  0       
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
  0 functions:
> 
> no_reads <- rowSums(obj$data$otu_rarefied[, sample_data$SampleID]) == 0
> obj <- filter_obs(obj, "otu_rarefied", ! no_reads)
> print(obj)
<Taxmap>
  1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
  1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
  3 data sets:
    otu_counts:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        22         0         0         0         0         0         0         0        12
      2 avu      cb5168e06325147…        13        10        10         0         0         0        12        14        13
      3 aet      3968cb5b7766a80…        89        13        15        16        22        40        31        14         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_rarefied:
      # A tibble: 2,619 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        14         0         0         0         0         0         0         0         4
      2 avu      cb5168e06325147…         7         5         5         0         0         0         8         7         6
      3 aet      3968cb5b7766a80…        60         7         6        11        15        23        18        11         0
      # ℹ 2,616 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_props:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…  0.000851  0         0         0         0          0        0         0         0.000387
      2 avu      cb5168e06325147…  0.000503  0.000359  0.000310  0         0          0        0.000474  0.000440  0.000420
      3 aet      3968cb5b7766a80…  0.00344   0.000466  0.000465  0.000544  0.000741   0.00161  0.00122   0.000440  0       
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
  0 functions:
> 
> 
> obj$data$otu_props <- calc_obs_props(obj, "otu_counts", other_cols = TRUE)
No `cols` specified, so using all numeric columns:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

Calculating proportions from counts for 72 columns for 2620 observations.
Warning message:
The following columns will be replaced in the output:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

> print(obj)
<Taxmap>
  1186 taxa: aab. Bacteria, aac. Bacteroidota ... cjf. uncultured_delta, cjl. uncultured_Dokdonella
  1186 edges: NA->aab, aab->aac, aab->aad, aab->aae, aab->aaf ... bhz->cjc, bft->cjd, bng->cje, axx->cjf, bke->cjl
  3 data sets:
    otu_counts:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        22         0         0         0         0         0         0         0        12
      2 avu      cb5168e06325147…        13        10        10         0         0         0        12        14        13
      3 aet      3968cb5b7766a80…        89        13        15        16        22        40        31        14         0
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_rarefied:
      # A tibble: 2,619 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…        14         0         0         0         0         0         0         0         4
      2 avu      cb5168e06325147…         7         5         5         0         0         0         8         7         6
      3 aet      3968cb5b7766a80…        60         7         6        11        15        23        18        11         0
      # ℹ 2,616 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
    otu_props:
      # A tibble: 2,620 × 74
        taxon_id `#OTU ID`        `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3`
        <chr>    <chr>                <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
      1 bnk      8ffd3dc50d6274b…  0.000851  0         0         0         0          0        0         0         0.000387
      2 avu      cb5168e06325147…  0.000503  0.000359  0.000310  0         0          0        0.000474  0.000440  0.000420
      3 aet      3968cb5b7766a80…  0.00344   0.000466  0.000465  0.000544  0.000741   0.00161  0.00122   0.000440  0       
      # ℹ 2,617 more rows
      # ℹ 63 more variables: `DR40X-4` <dbl>, `DR40X-5` <dbl>, `DR40X-6` <dbl>, `DR20X-1` <dbl>, `DR20X-2` <dbl>,
      #   `DR20X-3` <dbl>, `DR20X-4` <dbl>, `DR20X-5` <dbl>, `DR20X-6` <dbl>, `DR80M-1` <dbl>, …
      # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
  0 functions:
> 
> 
> 
> ########################
>   library(vegan)
>   rarecurve(t(obj$data$otu_counts[, "DR80X-1"]), step = 20,
+             sample = min(colSums(obj$data$otu_counts[, sample_data$SampleID])),
+             col = "blue", cex = 1.5)
Warning message:
In rarecurve(t(obj$data$otu_counts[, "DR80X-1"]), step = 20, sample = min(colSums(obj$data$otu_counts[,  :
  most observed count data have counts 1, but smallest count is 10
> 
> counts_to_presence(obj, "otu_rarefied")
No `cols` specified, so using all numeric columns:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

# A tibble: 2,619 × 73
   taxon_id `DR80X-1` `DR80X-2` `DR80X-3` `DR80X-4` `DR80X-5` `DR80X-6` `DR40X-1` `DR40X-2` `DR40X-3` `DR40X-4` `DR40X-5` `DR40X-6`
   <chr>    <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>     <lgl>    
 1 bnk      TRUE      FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     TRUE      TRUE      FALSE     FALSE    
 2 avu      TRUE      TRUE      TRUE      FALSE     FALSE     FALSE     TRUE      TRUE      TRUE      FALSE     TRUE      FALSE    
 3 aet      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      FALSE     TRUE      TRUE      TRUE     
 4 bnm      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE     
 5 als      FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE    
 6 bnn      FALSE     FALSE     TRUE      TRUE      FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     TRUE     
 7 alu      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      FALSE     TRUE      TRUE      TRUE      TRUE      TRUE     
 8 bnp      FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE    
 9 bnr      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE      TRUE     
10 awh      FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE     FALSE    
# ℹ 2,609 more rows
# ℹ 60 more variables: `DR20X-1` <lgl>, `DR20X-2` <lgl>, `DR20X-3` <lgl>, `DR20X-4` <lgl>, `DR20X-5` <lgl>, `DR20X-6` <lgl>,
#   `DR80M-1` <lgl>, `DR80M-2` <lgl>, `DR80M-3` <lgl>, `DR80M-4` <lgl>, `DR80M-5` <lgl>, `DR80M-6` <lgl>, `DR40M-1` <lgl>,
#   `DR40M-2` <lgl>, `DR40M-3` <lgl>, `DR40M-4` <lgl>, `DR40M-5` <lgl>, `DR40M-6` <lgl>, `DR20M-1` <lgl>, `DR20M-2` <lgl>,
#   `DR20M-3` <lgl>, `DR20M-4` <lgl>, `DR20M-5` <lgl>, `DR20M-6` <lgl>, `RC80X-1` <lgl>, `RC80X-2` <lgl>, `RC80X-3` <lgl>,
#   `RC80X-4` <lgl>, `RC80X-5` <lgl>, `RC80X-6` <lgl>, `RC40X-1` <lgl>, `RC40X-2` <lgl>, `RC40X-3` <lgl>, `RC40X-4` <lgl>,
#   `RC40X-5` <lgl>, `RC40X-6` <lgl>, `RC20X-1` <lgl>, `RC20X-2` <lgl>, `RC20X-3` <lgl>, `RC20X-4` <lgl>, `RC20X-5` <lgl>, …
# ℹ Use `print(n = ...)` to see more rows
> # Apply the function
> presence_data <- counts_to_presence(obj, "otu_rarefied")
No `cols` specified, so using all numeric columns:
   DR80X-1, DR80X-2, DR80X-3, DR80X-4, DR80X-5, DR80X-6, DR40X-1 ... RC20M-1, RC20M-2, RC20M-3, RC20M-4, RC20M-5, RC20M-6

> packageVersion("phyloseq")
[1] ‘1.41.1’
> 
> # Example of converting to a phyloseq object (if needed)
> ps_obj <- as_phyloseq(obj,
+                       otu_table = presence_data,
+                       otu_id_col = "#OTU ID",
+                       sample_data = sample_data,
+                       sample_id_col = "SampleID")
Error in as_phyloseq(obj, otu_table = presence_data, otu_id_col = "#OTU ID",  : 
  OTU table does not have an OTU ID column named "#OTU ID". Use the "otu_id_col" option if it is named something else.
>
ChathuZ commented 3 weeks ago

This worked for me. Is it ok to continue with this?

ps_obj <- as_phyloseq(obj,
                      otu_table = obj$data$otu_rarefied,
                      otu_id_col = "#OTU ID",
                      sample_data = sample_data,
                      sample_id_col = "SampleID")