cluster_cells

Cluster cells based on scDHA methods to get cluster labels

Performing single-cell imputation in a way that preserves the biological variations in the data. The package clusters the input data to do imputation for each cluster, and do a distribution check using the Anderson-Darling normality test to impute dropouts using mean or median (Yazici, B., & Yolacan, S. (2007) <DOI:10.1080/10629360600678310>).

Mohamed Soudy

ScRNAIMM

Performing Single-Cell RNA-Seq Imputation by Using Mean/Median
Imputation

Sascha Jung

Antonio DEL SOL

cluster_cells function

<dl><dt>ScRNA_filtered</dt>
<dd>ScRNA-seq data set generated by filter_ScRNA function</dd>
<dt>Normalize</dt>
<dd>Boolean parameter whether to apply log10 normalization for the data or not</dd>
<dt>k</dt>
<dd>Number of clusters if there is a prior knowledge about that</dd>
<dt>n</dt>
<dd>Number of genes to keep after feature selection step</dd></dl>

Arguments

Mohamed Soudy <a href="/link/Mohmedsoudy2009%40gmail.com?package=ScRNAIMM&version=0.1" data-mini-rdoc="ScRNAIMM::Mohmedsoudy2009@gmail.com">Mohmedsoudy2009@gmail.com</a>

Author

Perform cell clustering based on scDHA method — cluster_cells

<dl>

<dt>ScRNA_filtered</dt>
<dd>ScRNA-seq data set generated by filter_ScRNA function</dd>


<dt>Normalize</dt>
<dd>Boolean parameter whether to apply log10 normalization for the data or not</dd>


<dt>k</dt>
<dd>Number of clusters if there is a prior knowledge about that</dd>


<dt>n</dt>
<dd>Number of genes to keep after feature selection step</dd>

</dl>

Mohamed Soudy <a href='mailto:Mohmedsoudy2009@gmail.com'>Mohmedsoudy2009@gmail.com</a>

cluster_cells: Perform cell clustering based on scDHA method

Description

Usage

Value

Arguments

Author