Dataset Information

PyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction.

ABSTRACT: MOTIVATION:The emergence of single-cell RNA-sequencing has enabled analyses that leverage transitioning cell states to reconstruct pseudotemporal trajectories. Multidimensional data sparsity, zero inflation and technical variation necessitate the selection of high-quality features that feed downstream analyses. Despite the development of numerous algorithms for the unsupervised selection of biologically relevant features, their differential performance remains largely unaddressed. RESULTS:We implemented the neighborhood variance ratio (NVR) feature selection approach as a Python package with substantial improvements in performance. In comparing NVR with multiple unsupervised algorithms such as dpFeature, we observed striking differences in features selected. We present evidence that quantifiable dataset properties have observable and predictable effects on the performance of these algorithms. AVAILABILITY AND IMPLEMENTATION:pyNVR is freely available at https://github.com/KenLauLab/NVR. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Chen B

PROVIDER: S-EPMC6596893 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction.

Chen Bob B Herring Charles A CA Lau Ken S KS

Bioinformatics (Oxford, England) 20190701 13

<h4>Motivation</h4>The emergence of single-cell RNA-sequencing has enabled analyses that leverage transitioning cell states to reconstruct pseudotemporal trajectories. Multidimensional data sparsity, zero inflation and technical variation necessitate the selection of high-quality features that feed downstream analyses. Despite the development of numerous algorithms for the unsupervised selection of biologically relevant features, their differential performance remains largely unaddressed.<h4>Res ...[more]

PMID: 30445607

Similar Datasets

Project description:BackgroundHigh throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner.ResultsWe introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells.ConclusionsAccording to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

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

PyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction.

Publications

pyNVR: investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets