Jaccard

Robustness of the clusters can be assessed by Jaccard’s
 similarity, which reflects the reproducibility of individual clusters
 across bootstrapping runs. Jaccard’s similarity is the intersect of two
 clusters divided by the union.

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

Jaccard function

<dl><dt>object</dt>
<dd><code>DISCBIO</code> class object.</dd>
<dt>Clustering</dt>
<dd>Clustering has to be one of the following:
["K-means","MB"]. Default is "K-means"</dd>
<dt>K</dt>
<dd>A numeric value of the number of clusters</dd>
<dt>plot</dt>
<dd>if `TRUE`, plots the mean Jaccard similarities</dd>
<dt>R</dt>
<dd>number of bootstrap replicates</dd></dl>

Arguments

Jaccard’s similarity — Jaccard

<dl>

<dt>object</dt>
<dd><code>DISCBIO</code> class object.</dd>


<dt>Clustering</dt>
<dd>Clustering has to be one of the following:
["K-means","MB"]. Default is "K-means"</dd>


<dt>K</dt>
<dd>A numeric value of the number of clusters</dd>


<dt>plot</dt>
<dd>if `TRUE`, plots the mean Jaccard similarities</dd>


<dt>R</dt>
<dd>number of bootstrap replicates</dd>

</dl>

Jaccard: Jaccard’s similarity

Description

Usage

Value

Arguments