scholarly journals Comparative Research of Different Dimension Reduction Methods Combined with RWR Network Smoothing in Single Cell RNA-seq Data

2020 ◽  
Vol 495 ◽  
pp. 012043
Author(s):  
Xuesong Xiao ◽  
Pengchao Ye ◽  
Wenbin Ye ◽  
Guoli Ji
Keyword(s):  
Dimension Reduction ◽  
Single Cell ◽  
Comparative Research ◽  
Rna Seq ◽  
Reduction Methods

scholarly journals Exploring dimension-reduced embeddings with Sleepwalk

2019 ◽  
Author(s):  
Svetlana Ovchinnikova ◽  
Simon Anders
Keyword(s):  
Big Data ◽  
Dimension Reduction ◽  
Single Cell ◽  
Single Cells ◽  
High Dimensional ◽  
Rna Seq ◽  
Mouse Cursor ◽  
Sample Data ◽  
Reduction Methods ◽  
Full Power

AbstractDimension-reduction methods, such as t-SNE or UMAP, are widely used when exploring high-dimensional data describing many entities, e.g., RNA-seq data for many single cells. However, dimension reduction is commonly prone to introducing artefacts, and we hence need means to see where a dimension-reduced embedding is a faithful representation of the local neighbourhood and where it is not.We present Sleepwalk, a simple but powerful tool that allows the user to interactively explore an embedding, using colour to depict original or any other distances from all points to the cell under the mouse cursor. We show how this approach not only highlights distortions, but also reveals otherwise hidden characteristics of the data, and how Sleep-walk’s comparative modes help integrate multi-sample data and understand differences between embedding and preprocessing methods. Sleepwalk is a versatile and intuitive tool that unlocks the full power of dimension reduction and will be of value not only in single-cell RNA-seq but also in any other area with matrix-shaped big data.

scholarly journals A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruizhi Xiang ◽  
Wencan Wang ◽  
Lei Yang ◽  
Shiyuan Wang ◽  
Chaohan Xu ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Dimension Reduction ◽  
Single Cell ◽  
High Throughput Sequencing ◽  
Cellular Heterogeneity ◽  
Specific Situation ◽  
Rna Seq ◽  
Reduction Methods ◽  
The Stability ◽  
Downstream Analysis

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity. However, scRNA-seq data are high-dimensional, noisy, and sparse data. Dimension reduction is an important step in downstream analysis of scRNA-seq. Therefore, several dimension reduction methods have been developed. We developed a strategy to evaluate the stability, accuracy, and computing cost of 10 dimensionality reduction methods using 30 simulation datasets and five real datasets. Additionally, we investigated the sensitivity of all the methods to hyperparameter tuning and gave users appropriate suggestions. We found that t-distributed stochastic neighbor embedding (t-SNE) yielded the best overall performance with the highest accuracy and computing cost. Meanwhile, uniform manifold approximation and projection (UMAP) exhibited the highest stability, as well as moderate accuracy and the second highest computing cost. UMAP well preserves the original cohesion and separation of cell populations. In addition, it is worth noting that users need to set the hyperparameters according to the specific situation before using the dimensionality reduction methods based on non-linear model and neural network.

scholarly journals Author Correction: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
F. William Townes ◽  
Stephanie C. Hicks ◽  
Martin J. Aryee ◽  
Rafael A. Irizarry
Keyword(s):  
Feature Selection ◽  
Dimension Reduction ◽  
Single Cell ◽  
Multinomial Model ◽  
Rna Seq

scholarly journals Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
F. William Townes ◽  
Stephanie C. Hicks ◽  
Martin J. Aryee ◽  
Rafael A. Irizarry
Keyword(s):  
Feature Selection ◽  
Dimension Reduction ◽  
Single Cell ◽  
Current Practice ◽  
Principal Component ◽  
Ground Truth ◽  
Rna Seq ◽  
Normal Distributions ◽  
Multinomial Sampling ◽  
Negative Controls

AbstractSingle-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.

scholarly journals NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Ruiyu Xiao ◽  
Guoshan Lu ◽  
Wanqian Guo ◽  
Shuilin Jin
Keyword(s):  
Dimensionality Reduction ◽  
Data Quality ◽  
Single Cell ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Size Reduction ◽  
Rna Seq ◽  
Reduction Methods ◽  
The Many

Abstract Background Single-cell RNA sequencing can be used to fairly determine cell types, which is beneficial to the medical field, especially the many recent studies on COVID-19. Generally, single-cell RNA data analysis pipelines include data normalization, size reduction, and unsupervised clustering. However, different normalization and size reduction methods will significantly affect the results of clustering and cell type enrichment analysis. Choices of preprocessing paths is crucial in scRNA-Seq data mining, because a proper preprocessing path can extract more important information from complex raw data and lead to more accurate clustering results. Results We proposed a method called NDRindex (Normalization and Dimensionality Reduction index) to evaluate data quality of outcomes of normalization and dimensionality reduction methods. The method includes a function to calculate the degree of data aggregation, which is the key to measuring data quality before clustering. For the five single-cell RNA sequence datasets we tested, the results proved the efficacy and accuracy of our index. Conclusions This method we introduce focuses on filling the blanks in the selection of preprocessing paths, and the result proves its effectiveness and accuracy. Our research provides useful indicators for the evaluation of RNA-Seq data.

scholarly journals Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis

2020 ◽  
Author(s):  
Felix Raimundo ◽  
Celine Vallot ◽  
Jean Philippe Vert
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Biological Diversity ◽  
Unmet Need ◽  
Principal Component ◽  
Parameter Tuning ◽  
Differential Analysis ◽  
Rna Seq ◽  
Reduction Methods ◽  
Complex Models

AbstractBackgroundMany computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering or differential analysis, often relying on default parameters. Yet given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need.ResultsHere, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it.ConclusionsWe find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA and scVI can reach better performance but after parameter tuning.

scholarly journals Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools

2021 ◽  
Vol 2 (2) ◽  
pp. 100450
Author(s):  
Bob Chen ◽  
Marisol A. Ramirez-Solano ◽  
Cody N. Heiser ◽  
Qi Liu ◽  
Ken S. Lau
Keyword(s):  
Dimension Reduction ◽  
Single Cell ◽  
Open Source ◽  
Rna Seq

scholarly journals SHARP: Single-cell RNA-seq Hyper-fast and Accurate Processing via Ensemble Random Projection

2018 ◽  
Author(s):  
Shibiao Wan ◽  
Junil Kim ◽  
Kyoung Jae Won
Keyword(s):  
Dimension Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Random Projection ◽  
Rna Seq ◽  
Running Speed ◽  
Large Size ◽  
Single Cell Rna Sequencing ◽  
Speed And Accuracy

ABSTRACTTo process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm which is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq datasets demonstrate that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size datasets (>40,000 cells), SHARP’s running speed far excels other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

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