corrVar

Function for regressing out the mean-variance dependence.
This function corrects for the systematic dependence of the variance on the mean by a local regression.

Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) <DOI:10.1038/nmeth.4662>). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) <DOI:10.1186/s13059-023-02974-1>).

Dominic Grc3<bc>n

RaceID

Identification of Cell Types, Inference of Lineage Trees, and
Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Dominic Grün

corrVar function

<dl><dt>m</dt>
<dd>Vector of mean expression estimates for a set of genes.</dd>
<dt>v</dt>
<dd>Vector of variance etsimates for a set of genes.</dd>
<dt>span</dt>
<dd>Parameter for the local regression. See help(loess). Default value is 0.75.</dd>
<dt>degree</dt>
<dd>Parameter for the local regression. See help(loess). Default value is 2.</dd></dl>

Arguments

Function for regressing out the mean-variance dependence.
This function corrects for the systematic dependence of the variance on the mean by a local regression. — corrVar

<dl>

<dt>m</dt>
<dd>Vector of mean expression estimates for a set of genes.</dd>


<dt>v</dt>
<dd>Vector of variance etsimates for a set of genes.</dd>


<dt>span</dt>
<dd>Parameter for the local regression. See help(loess). Default value is 0.75.</dd>


<dt>degree</dt>
<dd>Parameter for the local regression. See help(loess). Default value is 2.</dd>

</dl>

Function for regressing out the mean-variance dependence.
This function corrects for the systematic dependence of the variance on the mean by a local regression.

corrVar: Function for regressing out the mean-variance dependence. This function corrects for the systematic dependence of the variance on the mean by a local regression.

Description

Usage

Value

Arguments