Function to preprocess your data for input into run_ml().
Usage
preprocess_data(dataset, ...)
# S4 method for class 'TreeSummarizedExperiment'
preprocess_data(
dataset,
outcome_colname,
assay.type = "counts",
col.var = NULL,
altexp = NULL,
name = "preprocessed",
...
)
# S4 method for class 'ANY'
preprocess_data(
dataset,
outcome_colname,
method = c("center", "scale"),
remove_var = "nzv",
collapse_corr_feats = TRUE,
corr_method = "spearman",
corr_thresh = 1,
to_numeric = TRUE,
group_neg_corr = TRUE,
prefilter_threshold = 1,
...
)Arguments
- dataset
Data frame with an outcome variable and other columns as features. Alternatively, the input can be in
TreeSummarizedExperimentformat.- ...
All additional arguments are passed on to
caret::train(), such as case weights via theweightsargument orntreeforrfmodels. See thecaret::train()docs for more details.- outcome_colname
Column name as a string of the outcome variable (default
NULL; the first column will be chosen automatically).- assay.type
The name of assay from
datasetwhen the object is inTreeSummarizedExperimentformat. This assay is used as an input.- col.var
The name of sample matdata variables from
colDataslot ofdatasetwhen the object is inTreeSummarizedExperimentformat. These variables are used as predictors.- altexp
The name of alternative experiment (
altExp) fromdatasetwhen the object is inTreeSummarizedExperimentformat. This can be used to select an experiment for the input.- name
Name of results used when the input is
TreeSummarizedExperiment. This same name is used forassayandaltExp.- method
Methods to preprocess the data, described in
caret::preProcess()(default:c("center","scale"), useNULLfor no normalization).- remove_var
Whether to remove variables with near-zero variance (
'nzv'; default), zero variance ('zv'), or none (NULL).- collapse_corr_feats
Whether to keep only one of correlated features (see
corr_methodandcorr_thresh)- corr_method
Correlation method. Options are the same as those supported by
stats::cor: spearman, pearson, kendall. (default: spearman)- corr_thresh
group correlations above or equal to
corr_thresh(range0to1; default:1).- to_numeric
Whether to change features to numeric where possible.
- group_neg_corr
Whether to group negatively correlated features together (e.g. c(0,1) and c(1,0)).
- prefilter_threshold
Remove features which only have non-zero & non-NA values in N rows or fewer (default: 1). Set this to -1 to keep all columns at this step. This step will also be skipped if
to_numericis set toFALSE.
Value
Named list including:
dat_transformed: Preprocessed data.grp_feats: If features were grouped together, a named list of the features corresponding to each group.removed_feats: Any features that were removed during preprocessing (e.g. because there was zero variance or near-zero variance for those features).
If the input is TreeSummarizedExperiment, the output is added as an
additional data to the input object. If the set of features match in output
and input, the results are stored directly to assay slot. If they
do not match, the output is stored to altExp slot of the object.
If the progressr package is installed, a progress bar with time elapsed
and estimated time to completion can be displayed.
More details
See the preprocessing vignette for more details.
Note that if any values in outcome_colname contain spaces, they will be
converted to underscores for compatibility with caret.
Examples
preprocess_data(mikropml::otu_small, "dx")
#> Using 'dx' as the outcome column.
#> $dat_transformed
#> # A tibble: 200 × 61
#> dx Otu00001 Otu00002 Otu00003 Otu00004 Otu00005 Otu00006 Otu00007 Otu00008
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 norm… -0.420 -0.219 -0.174 -0.591 -0.0488 -0.167 -0.569 -0.0624
#> 2 norm… -0.105 1.75 -0.718 0.0381 1.54 -0.573 -0.643 -0.132
#> 3 norm… -0.708 0.696 1.43 0.604 -0.265 -0.0364 -0.612 -0.207
#> 4 norm… -0.494 -0.665 2.02 -0.593 -0.676 -0.586 -0.552 -0.470
#> 5 norm… 1.11 -0.395 -0.754 -0.586 -0.754 2.73 0.191 -0.676
#> 6 norm… -0.685 0.614 -0.174 -0.584 0.376 0.804 -0.337 -0.00608
#> 7 canc… -0.770 -0.496 -0.318 0.159 -0.658 2.20 -0.717 0.0636
#> 8 norm… -0.424 -0.478 -0.397 -0.556 -0.391 -0.0620 0.376 -0.0222
#> 9 norm… -0.556 1.14 1.62 -0.352 -0.275 -0.465 -0.804 0.294
#> 10 canc… 1.46 -0.451 -0.694 -0.0567 -0.706 0.689 -0.370 1.59
#> # ℹ 190 more rows
#> # ℹ 52 more variables: Otu00009 <dbl>, Otu00010 <dbl>, Otu00011 <dbl>,
#> # Otu00012 <dbl>, Otu00013 <dbl>, Otu00014 <dbl>, Otu00015 <dbl>,
#> # Otu00016 <dbl>, Otu00017 <dbl>, Otu00018 <dbl>, Otu00019 <dbl>,
#> # Otu00020 <dbl>, Otu00021 <dbl>, Otu00022 <dbl>, Otu00023 <dbl>,
#> # Otu00024 <dbl>, Otu00025 <dbl>, Otu00026 <dbl>, Otu00027 <dbl>,
#> # Otu00028 <dbl>, Otu00029 <dbl>, Otu00030 <dbl>, Otu00031 <dbl>, …
#>
#> $grp_feats
#> NULL
#>
#> $removed_feats
#> character(0)
#>
# the function can show a progress bar if you have the progressr package installed
## optionally, specify the progress bar format
progressr::handlers(progressr::handler_progress(
format = ":message :bar :percent | elapsed: :elapsed | eta: :eta",
clear = FALSE,
show_after = 0
))
## tell progressor to always report progress
if (FALSE) { # \dontrun{
progressr::handlers(global = TRUE)
## run the function and watch the live progress udpates
dat_preproc <- preprocess_data(mikropml::otu_small, "dx")
# Create TreeSE object
library(TreeSummarizedExperiment)
df <- mikropml::otu_small
assay <- df[, !colnames(df) %in% c("dx"), drop = FALSE] |> t() |> as.matrix()
tse <- TreeSummarizedExperiment(assays = SimpleList(counts = assay))
colData(tse)[["dx"]] <- df[["dx"]]
# Preprocess
tse <- preprocess_data(
dataset = tse,
assay.type = "counts",
outcome_colname = "dx"
)
# The result is in assay slot
tse
} # }