# install.packages("remotes")remotes::install_github("abichat/scimo")

library(scimo)data("cheese_abundance","cheese_taxonomy")cheese_abundance#> # A tibble: 9 × 77#>   sample  cheese rind_type asv_01 asv_02 asv_03 asv_04 asv_05 asv_06 asv_07 asv_08#>   <chr>   <chr>  <chr>      <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>#> 1 sample… Saint… Natural        1      0     38     40      1      2     31      8#> 2 sample… Saint… Natural        3      4     38     61      4      4     48     14#> 3 sample… Saint… Natural       28     16     33     23     31     29     21      1#> 4 sample… Livar… Washed         0      2      1      0      5      1      0      0#> 5 sample… Livar… Washed         0      0      4      0      1      1      2      0#> 6 sample… Livar… Washed         0      1      2      0      2      1      0      0#> 7 sample… Epois… Washed         4      2      3      0      2      5      0      0#> 8 sample… Epois… Washed         0      0      0      0      0      0      0      0#> 9 sample… Epois… Washed         0      0      1      0      0      0      2      0#> # ℹ 66 more variables: asv_09 <dbl>, asv_10 <dbl>, asv_11 <dbl>, asv_12 <dbl>,#> #   asv_13 <dbl>, asv_14 <dbl>, asv_15 <dbl>, asv_16 <dbl>, asv_17 <dbl>,#> #   asv_18 <dbl>, asv_19 <dbl>, asv_20 <dbl>, asv_21 <dbl>, asv_22 <dbl>,#> #   asv_23 <dbl>, asv_24 <dbl>, asv_25 <dbl>, asv_26 <dbl>, asv_27 <dbl>,#> #   asv_28 <dbl>, asv_29 <dbl>, asv_30 <dbl>, asv_31 <dbl>, asv_32 <dbl>,#> #   asv_33 <dbl>, asv_34 <dbl>, asv_35 <dbl>, asv_36 <dbl>, asv_37 <dbl>,#> #   asv_38 <dbl>, asv_39 <dbl>, asv_40 <dbl>, asv_41 <dbl>, asv_42 <dbl>, …glimpse(cheese_taxonomy)#> Rows: 74#> Columns: 9#> $ asv     <chr> "asv_01", "asv_02", "asv_03", "asv_04", "asv_05", "asv_06", "asv…#> $ lineage <chr> "k__Fungi|p__Ascomycota|c__Dothideomycetes|o__Dothideales|f__Dot…#> $ kingdom <chr> "Fungi", "Fungi", "Fungi", "Fungi", "Fungi", "Fungi", "Fungi", "…#> $ phylum  <chr> "Ascomycota", "Ascomycota", "Ascomycota", "Ascomycota", "Ascomyc…#> $ class   <chr> "Dothideomycetes", "Eurotiomycetes", "Eurotiomycetes", "Eurotiom…#> $ order   <chr> "Dothideales", "Eurotiales", "Eurotiales", "Eurotiales", "Euroti…#> $ family  <chr> "Dothioraceae", "Aspergillaceae", "Aspergillaceae", "Aspergillac…#> $ genus   <chr> "Aureobasidium", "Aspergillus", "Penicillium", "Penicillium", "P…#> $ species <chr> "Aureobasidium Group pullulans", "Aspergillus fumigatus", "Penic…

list_family<-split(cheese_taxonomy$asv, cheese_taxonomy$family)head(list_family,2)#> $Aspergillaceae#> [1] "asv_02" "asv_03" "asv_04" "asv_05" "asv_06" "asv_07" "asv_08" "asv_09"#>#> $Debaryomycetaceae#>  [1] "asv_10" "asv_11" "asv_12" "asv_13" "asv_14" "asv_15" "asv_16" "asv_17"#>  [9] "asv_18" "asv_19" "asv_20" "asv_21" "asv_22"

rec<-recipe(cheese~ .,data = cheese_abundance)%>%step_aggregate_list(all_numeric_predictors(),list_agg = list_family,fun_agg = sum  )%>%step_rownormalize_tss(all_numeric_predictors())%>%step_select_kruskal(all_numeric_predictors(),outcome ="cheese",cutoff =0.05  )%>%prep()rec#>#> ── Recipe ────────────────────────────────────────────────────────────────────────#>#> ── Inputs#> Number of variables by role#> outcome:    1#> predictor: 76#>#> ── Training information#> Training data contained 9 data points and no incomplete rows.#>#> ── Operations#> • Aggregation of: asv_01, asv_02, asv_03, asv_04, asv_05, ... | Trained#> • TSS normalization on: Aspergillaceae Debaryomycetaceae, ... | Trained#> • Kruskal filtering against cheese on: Aspergillaceae, ... | Trainedbake(rec,new_data =NULL)#> # A tibble: 9 × 8#>   sample    rind_type cheese    Debaryomycetaceae Dipodascaceae Saccharomycetaceae#>   <fct>     <fct>     <fct>                 <dbl>         <dbl>              <dbl>#> 1 sample1-1 Natural   Saint-Ne…            0.719         0.0684           0.113#> 2 sample1-2 Natural   Saint-Ne…            0.715         0.0725           0.119#> 3 sample1-3 Natural   Saint-Ne…            0.547         0.277            0.0938#> 4 sample2-1 Washed    Livarot              0.153         0.845            0.000854#> 5 sample2-2 Washed    Livarot              0.150         0.848            0.00106#> 6 sample2-3 Washed    Livarot              0.160         0.837            0.00108#> 7 sample3-1 Washed    Epoisses             0.0513        0.944            0.00327#> 8 sample3-2 Washed    Epoisses             0.0558        0.941            0.00321#> 9 sample3-3 Washed    Epoisses             0.0547        0.942            0.00329#> # ℹ 2 more variables: `Saccharomycetales fam Incertae sedis` <dbl>,#> #   Trichosporonaceae <dbl>

tidy(rec,3)#> # A tibble: 13 × 4#>    terms                                    pv kept  id#>    <chr>                                 <dbl> <lgl> <chr>#>  1 Aspergillaceae                       0.0608 FALSE select_kruskal_WKayj#>  2 Debaryomycetaceae                    0.0273 TRUE  select_kruskal_WKayj#>  3 Dipodascaceae                        0.0273 TRUE  select_kruskal_WKayj#>  4 Dothioraceae                         0.101  FALSE select_kruskal_WKayj#>  5 Lichtheimiaceae                      0.276  FALSE select_kruskal_WKayj#>  6 Metschnikowiaceae                    0.0509 FALSE select_kruskal_WKayj#>  7 Mucoraceae                           0.0608 FALSE select_kruskal_WKayj#>  8 Phaffomycetaceae                     0.0794 FALSE select_kruskal_WKayj#>  9 Saccharomycetaceae                   0.0273 TRUE  select_kruskal_WKayj#> 10 Saccharomycetales fam Incertae sedis 0.0221 TRUE  select_kruskal_WKayj#> 11 Trichomonascaceae                    0.0625 FALSE select_kruskal_WKayj#> 12 Trichosporonaceae                    0.0273 TRUE  select_kruskal_WKayj#> 13 Wickerhamomyceteae                   0.177  FALSE select_kruskal_WKayj