shijie 1.1

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 [secuer](https://arxiv.org/abs/2205.12432v2).

The workflow of Secuer:

<img src=”https://github.com/nanawei11/Secuer/raw/main/Figures/Figure1.png” style=”zoom: 33%;” />

## Installation

Secuer is available in [python](https://www.python.org).

`python pip install secuer `

## Run Seucer (usage)

#### Essential parameters

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

• -i INPUTFILE

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

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

• -p

  The number of anchors, default by 1000.

• -o

  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 dissimilarity between cells or anchors, default by euclidean.

• –transpose

  Require it if your data is a .csv, .txt or tsv file with features by observations.

• –eskMethod

  Specify the method used for estimated the number of cluster, default by subGraph.

• –eskResolution

  Specify the resolution when –eskMethod is subGraph, default by 0.8.

• –gapth

  Specify the gapth largest value when –eskMethod is not subGraph.

Example for run Secuer with all default parameters:

`sh $ Secuer S -i ./example_data/deng-rpkms.csv ` Example for run Secuer with custom parameters:

`sh $ Secuer S -i ./example_data/deng-rpkms.csv -o ./deng_result -p 900 --knn 5 --transpose `

## Run Seucer-consensus (usage)

#### Essential parameters

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

• -i INPUTFILE

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

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

• -p

  The number of anchors, default by 1000.

• -o

  Output file directory and file name, default by outputCon.

• -M

​ The times to run secuer.

• –knn

​ The number of k nearest neighbors anchors, default by 7.

• –transpose Require it if your data is a .csv, .txt or tsv file with features by observations, default by False.

`sh $ Secuer C -i ./example_data/deng-rpkms.csv `

Example for run Secuer-consensus with all default parameters: `sh $ Secuer C -i ./example_data/deng-rpkms.csv -o ./deng_conresult -M 7 -p 900 --knn 5 `

## Output files

1. output/output.txt is the clustering.

2. output/umap.pdf

3. output/tsne.pdf

## Citation