resa

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

resa function

<dl><dt>x</dt>
<dd>data matrix. nrow=#gene, ncol=#sample</dd>
<dt>d</dt>
<dd>estimated sequencing depth</dd>
<dt>nresamp</dt>
<dd>number of resamplings</dd></dl>

Arguments

Resampling — resa

<dl>

<dt>x</dt>
<dd>data matrix. nrow=#gene, ncol=#sample</dd>


<dt>d</dt>
<dd>estimated sequencing depth</dd>


<dt>nresamp</dt>
<dd>number of resamplings</dd>

</dl>

resa: Resampling

Description

Usage

Value

Arguments