Dataset Information

A clustering method for small scRNA-seq data based on subspace and weighted distance.

ABSTRACT:

Background

Identifying the cell types using unsupervised methods is essential for scRNA-seq research. However, conventional similarity measures introduce challenges to single-cell data clustering because of the high dimensional, high noise, and high dropout.

Methods

We proposed a clustering method for small ScRNA-seq data based on Subspace and Weighted Distance (SSWD), which follows the assumption that the sets of gene subspace composed of similar density-distributing genes can better distinguish cell groups. To accurately capture the intrinsic relationship among cells or genes, a new distance metric that combines Euclidean and Pearson distance through a weighting strategy was proposed. The relative Calinski-Harabasz (CH) index was used to estimate the cluster numbers instead of the CH index because it is comparable across degrees of freedom.

Results

We compared SSWD with seven prevailing methods on eight publicly scRNA-seq datasets. The experimental results show that the SSWD has better clustering accuracy and the partitioning ability of cell groups. SSWD can be downloaded at https://github.com/ningzilan/SSWD.

SUBMITTER: Ning Z

PROVIDER: S-EPMC9879162 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

A clustering method for small scRNA-seq data based on subspace and weighted distance.

Ning Zilan Z Dai Zhijun Z Zhang Hongyan H Chen Yuan Y Yuan Zheming Z

PeerJ 20230123

<h4>Background</h4>Identifying the cell types using unsupervised methods is essential for scRNA-seq research. However, conventional similarity measures introduce challenges to single-cell data clustering because of the high dimensional, high noise, and high dropout.<h4>Methods</h4>We proposed a clustering method for small <b>S</b>cRNA-seq data based on <b>S</b>ubspace and <b>W</b>eighted <b>D</b>istance (SSWD), which follows the assumption that the sets of gene subspace composed of similar densi ...[more]

PMID: 36710872

Similar Datasets

Project description:BackgroundSingle-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underlying cell identities. The high dimensionality of scRNA-seq data and its significant sparsity accentuated by frequent dropout events, introducing false zero count observations, make the clustering analysis computationally challenging. Even though multiple scRNA-seq clustering techniques have been proposed, there is no consensus on the best performing approach. On a parallel research track, self-supervised contrastive learning recently achieved state-of-the-art results on images clustering and, subsequently, image classification.ResultsWe propose contrastive-sc, a new unsupervised learning method for scRNA-seq data that perform cell clustering. The method consists of two consecutive phases: first, an artificial neural network learns an embedding for each cell through a representation training phase. The embedding is then clustered in the second phase with a general clustering algorithm (i.e. KMeans or Leiden community detection). The proposed representation training phase is a new adaptation of the self-supervised contrastive learning framework, initially proposed for image processing, to scRNA-seq data. contrastive-sc has been compared with ten state-of-the-art techniques. A broad experimental study has been conducted on both simulated and real-world datasets, assessing multiple external and internal clustering performance metrics (i.e. ARI, NMI, Silhouette, Calinski scores). Our experimental analysis shows that constastive-sc compares favorably with state-of-the-art methods on both simulated and real-world datasets.ConclusionOn average, our method identifies well-defined clusters in close agreement with ground truth annotations. Our method is computationally efficient, being fast to train and having a limited memory footprint. contrastive-sc maintains good performance when only a fraction of input cells is provided and is robust to changes in hyperparameters or network architecture. The decoupling between the creation of the embedding and the clustering phase allows the flexibility to choose a suitable clustering algorithm (i.e. KMeans when the number of expected clusters is known, Leiden otherwise) or to integrate the embedding with other existing techniques.

Dataset Information

A clustering method for small scRNA-seq data based on subspace and weighted distance.

Background

Methods

Results

Publications

A clustering method for small scRNA-seq data based on subspace and weighted distance.

Similar Datasets

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