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dslice (version 1.2.2)

Dynamic Slicing

Description

Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.

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Version

Install

install.packages('dslice')

Monthly Downloads

176

Version

1.2.2

License

GPL (>= 2)

Maintainer

Chao Ye

Last Published

November 22nd, 2023

Functions in dslice (1.2.2)

gsa_set

Gene set list in gene set analysis
load_cls

Load phenotype file
load_gmt

Load gene set file
slice_show

Show the slicing result
ds_eqp_k

Dependency detection between level \(k\) (\(k > 1\)) categorical variable and continuous variable
ds_gsa

Gene set analysis via dynamic slicing
bfslice_u

Dependency detection between a level \(k\) (\(k > 1\)) categorical variable and a continuous variable via Bayes factor.
bfslice_c

Dependency and conditional dependency detection between a level \(k\) (\(k > 1\)) categorical variable and a continuous variable via Bayes factor.
ds_k

Dependency detection between level \(k\) (\(k > 1\)) categorical variable and continuous variable
ds_eqp_1

Non-parametric one-sample hypothesis testing via dynamic slicing
bfslice_eqp_u

Dependency detection between a level \(k\) (\(k > 1\)) categorical variable and a continuous variable via Bayes factor with given size of each group.
ds_1

Non-parametric one-sample hypothesis testing via dynamic slicing
ds_test

Hypothesis testing via dynamic slicing
bfslice_eqp_c

Dependency and conditional dependency detection between a level \(k\) (\(k > 1\)) categorical variable and a continuous variable via Bayes factor.
gsa_label

Sample labels in gene set analysis
gsa_exp

Gene expression matrix in gene set analysis
ds_type_one_error

Relationship between penalty and Type I error
export_res

Export gene set analysis result
rank_by_s2n

Ranking genes by signal to noise ratio
load_gct

Load gene expression file
relabel

Reassigning values of categorical variable