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8472ea8
19.07.02.presentation
R
example
man
above_percentage.Rd
add_assay_classification.Rd
add_assay_norm.Rd
add_coldata_classification.Rd
add_exon_order.Rd
add_expression.Rd
add_stage_simple.Rd
calc_auc.Rd
calc_prediction.Rd
calculate_annotation.Rd
calculate_factor.Rd
calculate_fit.Rd
calculate_numerical.Rd
collapse_column.Rd
combine_se.Rd
correct_output.Rd
count_column.Rd
counts_per_group.Rd
data_summary.Rd
drop_unused_levels.Rd
extract_mode.Rd
factorize_columns.Rd
filter_columns_with_na.Rd
filter_duplicates.Rd
filter_fold_change.Rd
filter_for_factor.Rd
filter_mean.Rd
filter_rare.Rd
filter_single_level.Rd
filter_unimodal.Rd
filter_zero.Rd
get_no_cores.Rd
is_correct.Rd
make_SummarizedExperiment.Rd
make_coeff_frame.Rd
make_explicit_na.Rd
make_kruskal_frame.Rd
make_model_matrix.Rd
make_nested_metadata.Rd
make_prop_frame.Rd
make_summary.Rd
make_wilcox_frame.Rd
merge_duplicated.Rd
merge_duplicated_se.Rd
methyl_frame.Rd
methylation_beta_value.Rd
methylation_cpg_annotation.Rd
methylation_model_matrix.Rd
not_zero.Rd
order_by_column.Rd
plot_beta.Rd
plot_catdes.Rd
plot_heatmap.Rd
preprocess_assay.Rd
preprocess_coldata.Rd
quote_var.Rd
remove_x.Rd
rename_margin.Rd
reorder_all.Rd
reorder_column.Rd
rerun_mclust.Rd
results_mclust.Rd
simple_summary.Rd
stage_to_numeral.Rd
subset_metadata.Rd
test_algo.Rd
transform_assay.Rd
unnest_dataframe.Rd
use_biomaRt.Rd
use_catdes.Rd
use_cvglmnet.Rd
use_diptest.Rd
use_glmnet.Rd
use_kruskal_test.Rd
use_mclust.Rd
use_p_adjust.Rd
use_prop_test.Rd
use_siber.Rd
use_silvermantest.Rd
use_wilcox_test.Rd
verify_mclust.Rd
.Rbuildignore
.gitignore
DESCRIPTION
NAMESPACE
R.install.packages.txt
README.md
Thumbs.db
ToDo.txt
links.txt
mmRmeta.Rproj
termClusterBimodalBRCA.tab
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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/use_siber.R \name{use_siber} \alias{use_siber} \title{SIBER to calculate BI} \usage{ use_siber(summarized_experiment, index_assay = 1, norm_or_method = "TMM", model = "LN", parallel = TRUE, no_cluster = NULL, ...) } \arguments{ \item{summarized_experiment}{SummarizedExperiment or any data frame like object.} \item{index_assay}{Integer - index of assay of gene expression if input object is a SummarizedExperiment.} \item{norm_or_method}{Character/Numeric - normalization method or a scaling vector that is applied to the expression data} \item{model}{Character - model used in the calculation of the bimodal index (see SIBER::model)} \item{parallel}{Logical - use parallel computation} \item{no_cluster}{Integer - number of clusters used in parallelization | Default (number of cores - 1)} \item{...}{further arguments for SIBER} } \value{ Returns either a vector of pValues or a filtered input object. } \description{ SIBER to calculate BI } \details{ If filter is set to TRUE the function returns an object with the same class as summarized_experiment. Otherwise a vector of p-values is returned. }
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