secuer 1.0.5

Secuer: ultrafast, scalable and accurate clustering of single-cell RNA-seq data

# Secuer: ultrafast, scalable and accurate clustering of single-cell RNA-seq data

Secuer is a superfast and scalable clustering algorithm for (ultra-)large scRNA-seq data analysis based on spectral clustering. Secuer-consensus is a consensus clustering algorithm with Secuer as a subroutine. In addition, Secuer can also be applied to other large-scale omics data with two-dimenational (features * obsevation). For more details see xxx. Secuer is available in Python.

The workflow of Secuer:

<img src=”D:My_dataAllprojectSecuerUSPECSecuerFiguresFigure1.png” style=”zoom: 33%;” />

## Installation Secuer requires [python](https://www.python.org) to run.

`python pip install Secuer `

## Run Seucer (usage)

#### Essential paramters

To run Secuer with default parameters, you only need to give:

• -i INPUTFILE

  two-dimensional data (features by observations) file for clustering

Example for run Secuer with all default parameters:

`sh $ Secuer S -i ${inputpath} `

Example for run Secuer-consensus with all default parameters: `sh $ Secuer C -i ${inputpath} `

#### options You can also specify the following options:

• -p

  The number of anchors, default by 1000.

• -o OUTFILE

  Output file directory and file name, default by output.

• –knn The number of k nearest neighbors anchors, default by 7.

• –distance

  The metrics measuring the dissimmlarity between cells or anchors, default by euclidean.

## Output files

1. output/output.txt is the clustering reslut:

   • The clustering label.

## Citation