scimo 
scimo provides extra recipes steps for dealing with omics data, while also being adaptable to other data types.
Installation
You can install scimo from GitHub with:
# install.packages("remotes")
remotes::install_github("abichat/scimo")Example
The cheese_abundance dataset describes fungal community abundance of
74 Amplicon Sequences Variants (ASVs) sampled from the surface of three
different French cheeses.
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
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sample1-1 Saint-Ne… Natural 1 0 38 40 1 2 31
#> 2 sample1-2 Saint-Ne… Natural 3 4 38 61 4 4 48
#> 3 sample1-3 Saint-Ne… Natural 28 16 33 23 31 29 21
#> 4 sample2-1 Livarot Washed 0 2 1 0 5 1 0
#> 5 sample2-2 Livarot Washed 0 0 4 0 1 1 2
#> 6 sample2-3 Livarot Washed 0 1 2 0 2 1 0
#> 7 sample3-1 Epoisses Washed 4 2 3 0 2 5 0
#> 8 sample3-2 Epoisses Washed 0 0 0 0 0 0 0
#> 9 sample3-3 Epoisses Washed 0 0 1 0 0 0 2
#> # ℹ 67 more variables: asv_08 <dbl>, 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>, …
glimpse(cheese_taxonomy)
#> Rows: 74
#> Columns: 9
#> $ asv <chr> "asv_01", "asv_02", "asv_03", "asv_04", "asv_05", "asv_06", "a…
#> $ lineage <chr> "k__Fungi|p__Ascomycota|c__Dothideomycetes|o__Dothideales|f__D…
#> $ kingdom <chr> "Fungi", "Fungi", "Fungi", "Fungi", "Fungi", "Fungi", "Fungi",…
#> $ phylum <chr> "Ascomycota", "Ascomycota", "Ascomycota", "Ascomycota", "Ascom…
#> $ class <chr> "Dothideomycetes", "Eurotiomycetes", "Eurotiomycetes", "Euroti…
#> $ order <chr> "Dothideales", "Eurotiales", "Eurotiales", "Eurotiales", "Euro…
#> $ family <chr> "Dothioraceae", "Aspergillaceae", "Aspergillaceae", "Aspergill…
#> $ genus <chr> "Aureobasidium", "Aspergillus", "Penicillium", "Penicillium", …
#> $ species <chr> "Aureobasidium Group pullulans", "Aspergillus fumigatus", "Pen…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"The following recipe will
- aggregate the ASV variables at the family level, as defined by
list_family; - transform counts into proportions;
- discard variables those p-values are above 0.05 with a
Kruskal-Wallis test against
cheese.
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 and Debaryomycetaceae, ... | Trained
#> • Kruskal filtering against cheese on: Aspergillaceae, ... | Trained
bake(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-… 0.719 0.0684 0.113
#> 2 sample1-2 Natural Saint-… 0.715 0.0725 0.119
#> 3 sample1-3 Natural Saint-… 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 Epoiss… 0.0513 0.944 0.00327
#> 8 sample3-2 Washed Epoiss… 0.0558 0.941 0.00321
#> 9 sample3-3 Washed Epoiss… 0.0547 0.942 0.00329
#> # ℹ 2 more variables: `Saccharomycetales fam Incertae sedis` <dbl>,
#> # Trichosporonaceae <dbl>To see which variables are kept and the associated p-values, you can use
the tidy method on the third step:
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_WKayjNotes
protection stack overflow error
If you have a very large dataset, you may encounter this error:
data("pedcan_expression")
recipe(disease ~ ., data = pedcan_expression) %>%
step_select_cv(all_numeric_predictors(), prop_kept = 0.1)
#> Error: protect(): protection stack overflowIt is linked to how R handles many variables in
formulas. To solve
it, pass only the dataset to recipe() and manually update roles with
update_role(), like in the example below:
recipe(pedcan_expression) %>%
update_role(disease, new_role = "outcome") %>%
update_role(-disease, new_role = "predictor") %>%
step_select_cv(all_numeric_predictors(), prop_kept = 0.1)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 19196
#>
#> ── Operations
#> • Top CV filtering on: all_numeric_predictors()Steps for variable selection
Like colino, scimo
proposes 3 arguments for variable selection steps based on a statistic:
n_kept, prop_kept and cutoff.
-
n_keptandprop_keptdeal with how many variables will be kept in the preprocessed dataset, based on an exact count of variables or a proportion relative to the original dataset. They are mutually exclusive. -
cutoffremoves variables whose statistic is below (or above, depending on the step) it. It could be used alone or in addition to the two others.
Dependencies
scimo doesn’t introduce any additional dependencies compared to recipes.