find.dim.genes

This function takes an object of class iCellR finds the model genes to run a second round of PCA.

A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.

Alireza Khodadadi-Jamayran

iCellR

Analyzing High-Throughput Single Cell Sequencing Data

Joseph Pucella

Hua Zhou

Nicole Doudican

John Carucci

Adriana Heguy

Boris Reizis

Aristotelis Tsirigos

find.dim.genes function

<dl><dt>x</dt>
<dd>An object of class iCellR.</dd>
<dt>dims</dt>
<dd>PC dimentions to be used.</dd>
<dt>top.pos</dt>
<dd>Number of top positive marker genes to be taken from each PC, default = 15.</dd>
<dt>top.neg</dt>
<dd>Number of top negative marker genes to be taken from each PC, default = 5.</dd></dl>

Arguments

Find model genes from PCA data — find.dim.genes

<dl>

<dt>x</dt>
<dd>An object of class iCellR.</dd>


<dt>dims</dt>
<dd>PC dimentions to be used.</dd>


<dt>top.pos</dt>
<dd>Number of top positive marker genes to be taken from each PC, default = 15.</dd>


<dt>top.neg</dt>
<dd>Number of top negative marker genes to be taken from each PC, default = 5.</dd>

</dl>

find.dim.genes: Find model genes from PCA data

Description

Usage

Value

Arguments