RpartDT

The decision tree analysis is implemented over a training
 dataset, which consisted of the DEGs obtained by either SAMseq or the
 binomial differential expression.

An open, multi-algorithmic pipeline for easy, fast and efficient
analysis of cellular sub-populations and the molecular signatures that
characterize them. The pipeline consists of four successive steps: data
pre-processing, cellular clustering with pseudo-temporal ordering, defining
differential expressed genes and biomarker identification. More details on
Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements
extensions of the work published by Ghannoum et. al. (2019)
<doi:10.1101/700989>.

Waldir Leoncio

DIscBIO

A User-Friendly Pipeline for Biomarker Discovery in Single-Cell
Transcriptomics

Salim Ghannoum

Alvaro Köhn-Luque

Damiano Fantini

RpartDT function

<dl><dt>data</dt>
<dd>The exact output of the exprmclust function.</dd>
<dt>quiet</dt>
<dd>If `TRUE`, suppresses intermediary output</dd>
<dt>plot</dt>
<dd>If `FALSE`, suppresses plot output</dd></dl>

Arguments

RPART Decision Tree — RpartDT

<dl>

<dt>data</dt>
<dd>The exact output of the exprmclust function.</dd>


<dt>quiet</dt>
<dd>If `TRUE`, suppresses intermediary output</dd>


<dt>plot</dt>
<dd>If `FALSE`, suppresses plot output</dd>

</dl>

RpartDT: RPART Decision Tree

Description

Usage

Value

Arguments