Package 'scimo'

Title: Extra Recipes Steps for Dealing with Omics Data
Description: Omics data (e.g. transcriptomics, proteomics, metagenomics...) offer a detailed and multi-dimensional perspective on the molecular components and interactions within complex biological (eco)systems. Analyzing these data requires adapted procedures, which are implemented as steps according to the 'recipes' package.
Authors: Antoine BICHAT [aut, cre] , Julie AUBERT [ctb]
Maintainer: Antoine BICHAT <[email protected]>
License: GPL (>= 3)
Version: 0.0.2
Built: 2024-10-31 04:21:32 UTC
Source: https://github.com/abichat/scimo

Help Index

Abundance of Fungal Communities in Cheese

Description

Fungal community abundance of 74 ASVs sampled from the surface of three different French cheeses.

Usage

data("cheese_abundance", package = "scimo")

data("cheese_taxonomy", package = "scimo")

Format

For cheese_abundance, a tibble with columns:

sample

Sample ID.

cheese

Appellation of the cheese. One of Saint-Nectaire, Livarot or Epoisses.

rind_type

One of Natural or Washed.

other columns

Count of the ASV.

For cheese_taxonomy, a tibble with columns:

asv

Amplicon Sequence Variant (ASV) ID.

lineage

Character corresponding to a standard concatenation of taxonomic clades.

other columns

Clade to which the ASV belongs.

Source

This dataset came from doi:10.24072/pcjournal.321.

Examples

data("cheese_abundance", package = "scimo")
cheese_abundance
data("cheese_taxonomy", package = "scimo")
cheese_taxonomy

Gene Expression of Pediatric Cancer

Description

Gene expression of 108 CCLE cell lines from 5 different pediatric cancers.

Usage

data("pedcan_expression", package = "scimo")

Format

A tibble with columns:

cell_line

Cell line name.

sex

One of Male, Female or Unknown.

event

One of Primary, Metastasis or Unknown.

disease

One of Neuroblastoma, ⁠Ewing Sarcoma⁠, Rhabdomyosarcoma, ⁠Embryonal Tumor⁠ or Osteosarcoma.

other columns

Expression of the gene, given in log2(TPM + 1).

Source

This dataset is generated from DepMap Public 23Q4 primary files. https://depmap.org/portal/download/all/.

Examples

data("pedcan_expression", package = "scimo")
pedcan_expression

Feature aggregation step based on a hierarchical clustering

Description

Aggregate variables according to hierarchical clustering.

Usage

step_aggregate_hclust(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  n_clusters,
  fun_agg,
  dist_metric = "euclidean",
  linkage_method = "complete",
  res = NULL,
  prefix = "cl_",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("aggregate_hclust")
)

## S3 method for class 'step_aggregate_hclust'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
n_clusters

Number of cluster to create.
fun_agg

Aggregation function like sum or mean.
dist_metric

Default to euclidean. See stats::dist() for more details.
linkage_method

Default to complete. See stats::hclust() for more details.
res

This parameter is only produced after the recipe has been trained.
prefix

A character string for the prefix of the resulting new variables.
keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_aggregate_hclust object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

rec <-
  iris %>%
  recipe(formula = Species ~ .) %>%
  step_aggregate_hclust(all_numeric_predictors(),
                        n_clusters = 2, fun_agg = sum) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Feature aggregation step based on a defined list

Description

Aggregate variables according to prior knowledge.

Usage

step_aggregate_list(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  list_agg = NULL,
  fun_agg = NULL,
  others = "discard",
  name_others = "others",
  res = NULL,
  prefix = "agg_",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("aggregate_list")
)

## S3 method for class 'step_aggregate_list'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
list_agg

Named list of aggregated variables.
fun_agg

Aggregation function like sum or mean.
others

Behavior for the selected variables in ... that are not present in list_agg. If discard (the default), they are not kept. If asis, they are kept without modification. If aggregate, they are aggregated in a new variable.
name_others

If others is set to aggregate, name of the aggregated variable. Not used otherwise.
res

This parameter is only produced after the recipe has been trained.
prefix

A character string for the prefix of the resulting new variables that are not named in list_agg.
keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_aggregate_list object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

list_iris <- list(sepal.size = c("Sepal.Length", "Sepal.Width"),
                  petal.size = c("Petal.Length", "Petal.Width"))
rec <-
  iris %>%
  recipe(formula = Species ~ .) %>%
  step_aggregate_list(all_numeric_predictors(),
                      list_agg = list_iris, fun_agg = prod) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Feature normalization step using total sum scaling

Description

Normalize a set of variables by converting them to proportion, making them sum to 1. Also known as simplex projection.

Usage

step_rownormalize_tss(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  skip = FALSE,
  id = rand_id("rownormalize_tss")
)

## S3 method for class 'step_rownormalize_tss'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

Not used by this step since no new variables are created.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
res

This parameter is only produced after the recipe has been trained.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_rownormalize_tss object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

rec <-
  recipe(Species ~ ., data = iris) %>%
  step_rownormalize_tss(all_numeric_predictors()) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Feature selection step using background level

Description

Select features that exceed a background level in at least a defined number of samples.

Usage

step_select_background(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  background_level = NULL,
  n_samples = NULL,
  prop_samples = NULL,
  res = NULL,
  skip = FALSE,
  id = rand_id("select_background")
)

## S3 method for class 'step_select_background'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

Not used by this step since no new variables are created.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
background_level

Background level to exceed.
n_samples, prop_samples

Count or proportion of samples in which a feature exceeds background_level to be retained.
res

This parameter is only produced after the recipe has been trained.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_select_background object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

rec <-
  iris %>%
  recipe(formula = Species ~ .) %>%
  step_select_background(all_numeric_predictors(),
                         background_level = 4, prop_samples = 0.5) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Feature selection step using the coefficient of variation

Description

Select variables with highest coefficient of variation.

Usage

step_select_cv(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  n_kept = NULL,
  prop_kept = NULL,
  cutoff = NULL,
  res = NULL,
  skip = FALSE,
  id = rand_id("select_cv")
)

## S3 method for class 'step_select_cv'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

Not used by this step since no new variables are created.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
n_kept

Number of variables to keep.
prop_kept

A numeric value between 0 and 1 representing the proportion of variables to keep. n_kept and prop_kept are mutually exclusive.
cutoff

Threshold beyond which (below or above) the variables are discarded.
res

This parameter is only produced after the recipe has been trained.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_select_cv object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

rec <-
  recipe(Species ~ ., data = iris) %>%
  step_select_cv(all_numeric_predictors(), n_kept = 2) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Feature selection step using Kruskal test

Description

Select variables with the lowest (adjusted) p-value of a Kruskal-Wallis test against an outcome.

Usage

step_select_kruskal(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  n_kept = NULL,
  prop_kept = NULL,
  cutoff = NULL,
  correction = "none",
  res = NULL,
  skip = FALSE,
  id = rand_id("select_kruskal")
)

## S3 method for class 'step_select_kruskal'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

Not used by this step since no new variables are created.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
outcome

Name of the variable to perform the test against.
n_kept

Number of variables to keep.
prop_kept

A numeric value between 0 and 1 representing the proportion of variables to keep. n_kept and prop_kept are mutually exclusive.
cutoff

Threshold beyond which (below or above) the variables are discarded.
correction

Multiple testing correction method. One of p.adjust.methods. Default to "none".
res

This parameter is only produced after the recipe has been trained.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_select_kruskal object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

rec <-
  iris %>%
  recipe(formula = Species ~ .) %>%
  step_select_kruskal(all_numeric_predictors(), outcome = "Species",
                      correction = "fdr", prop_kept = 0.5) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Feature selection step using Wilcoxon test

Description

Select variables with the lowest (adjusted) p-value of a Wilcoxon-Mann-Whitney test against an outcome.

Usage

step_select_wilcoxon(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  outcome = NULL,
  n_kept = NULL,
  prop_kept = NULL,
  cutoff = NULL,
  correction = "none",
  res = NULL,
  skip = FALSE,
  id = rand_id("select_wilcoxon")
)

## S3 method for class 'step_select_wilcoxon'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

Not used by this step since no new variables are created.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
outcome

Name of the variable to perform the test against.
n_kept

Number of variables to keep.
prop_kept

A numeric value between 0 and 1 representing the proportion of variables to keep. n_kept and prop_kept are mutually exclusive.
cutoff

Threshold beyond which (below or above) the variables are discarded.
correction

Multiple testing correction method. One of p.adjust.methods. Default to "none".
res

This parameter is only produced after the recipe has been trained.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_select_wilcoxon object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

rec <-
  iris %>%
  dplyr::filter(Species != "virginica") %>%
  recipe(formula = Species ~ .) %>%
  step_select_wilcoxon(all_numeric_predictors(), outcome = "Species",
                       correction = "fdr", prop_kept = 0.5) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

Taxonomic clades feature generator

Description

Extract clades from a lineage, as defined in the {yatah} package.

Usage

step_taxonomy(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rank = NULL,
  res = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("taxonomy")
)

## S3 method for class 'step_taxonomy'
tidy(x, ...)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.
...

One or more selector functions to choose variables for this step. See selections() for more details.
role

For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
trained

A logical to indicate if the quantities for preprocessing have been estimated.
rank

The desired ranks, a combinaison of "kingdom", "phylum", "class", "order", "family", "genus", "species", or "strain". See yatah::get_clade() for more details.
res

This parameter is only produced after the recipe has been trained.
keep_original_cols

A logical to keep the original variables in the output. Defaults to FALSE.
skip

A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id

A character string that is unique to this step to identify it.
x

A step_taxonomy object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

data("cheese_taxonomy")
rec <-
  cheese_taxonomy %>%
  select(asv, lineage) %>%
  recipe(~ .) %>%
  step_taxonomy(lineage, rank = c("order", "genus")) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)