scholarly journals A generalization of t-SNE and UMAP to single-cell multimodal omics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Van Hoan Do ◽  
Stefan Canzar
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Types ◽  
High Dimensional ◽  
Omics Data ◽  
Relative Contribution ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Concise Representation ◽  
Cellular Identity

AbstractEmerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes.

scholarly journals Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Akram Vasighizaker ◽  
Saiteja Danda ◽  
Luis Rueda
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Rna Seq ◽  
Reduction Techniques ◽  
Non Linear ◽  
Dimensionality Reduction Techniques ◽  
Linear Dimensionality Reduction ◽  
The Impact

AbstractIdentifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method’s performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets.

scholarly journals A SURVEY ON THE CURES FOR THE CURSE OF DIMENSIONALITY IN BIG DATA

2017 ◽  
Vol 10 (13) ◽  
pp. 355 ◽  
Author(s):  
Reshma Remesh ◽  
Pattabiraman. V
Keyword(s):  
Dimensionality Reduction ◽  
Input Data ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
High Dimensional ◽  
Data Sets ◽  
Learning Approaches ◽  
Data Set ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

Dimensionality reduction techniques are used to reduce the complexity for analysis of high dimensional data sets. The raw input data set may have large dimensions and it might consume time and lead to wrong predictions if unnecessary data attributes are been considered for analysis. So using dimensionality reduction techniques one can reduce the dimensions of input data towards accurate prediction with less cost. In this paper the different machine learning approaches used for dimensionality reductions such as PCA, SVD, LDA, Kernel Principal Component Analysis and Artificial Neural Network  have been studied.

scholarly journals Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering

2019 ◽  
Vol 8 (S3) ◽  
pp. 66-71
Author(s):  
T. Sudha ◽  
P. Nagendra Kumar
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.

scholarly journals Analysis of unsupervised dimensionality reduction techniques

2009 ◽  
Vol 6 (2) ◽  
pp. 217-227 ◽  
Author(s):  
Aswani Kumar
Keyword(s):  
Dimensionality Reduction ◽  
Approximation Error ◽  
High Dimensional ◽  
Retrieval Task ◽  
Document Collections ◽  
Noise Effects ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Text Images ◽  
High Dimensional Datasets

Domains such as text, images etc contain large amounts of redundancies and ambiguities among the attributes which result in considerable noise effects (i.e. the data is high dimension). Retrieving the data from high dimensional datasets is a big challenge. Dimensionality reduction techniques have been a successful avenue for automatically extracting the latent concepts by removing the noise and reducing the complexity in processing the high dimensional data. In this paper we conduct a systematic study on comparing the unsupervised dimensionality reduction techniques for text retrieval task. We analyze these techniques from the view of complexity, approximation error and retrieval quality with experiments on four testing document collections.

A Review on Dimensionality Reduction Techniques

2019 ◽  
Vol 33 (10) ◽  
pp. 1950017 ◽  
Author(s):  
Xuan Huang ◽  
Lei Wu ◽  
Yinsong Ye
Keyword(s):  
Pattern Recognition ◽  
Dimensionality Reduction ◽  
Implementation Process ◽  
Recognition System ◽  
Research Trend ◽  
High Dimensional ◽  
Open Problems ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Scope Of Application

High-dimensional data is ubiquitous in scientific research and industrial production fields. It brings a lot of information to people, at the same time, because of its sparse and redundancy, it also brings great challenges to data mining and pattern recognition. Dimensionality reduction can reduce redundancy and noise, reduce the complexity of learning algorithms, and improve the accuracy of classification, it is an important and key step in pattern recognition system. In this paper, we overview the classical techniques for dimensionality reduction and review their properties, and categorize these techniques according to their implementation process. We deduce each algorithm in detail and intuitively show their underlying mathematical principles. Thereby, the focus is to uncover the optimization process for each technique. We compare the characteristics and limitations of each technique and summarize the scope of application, discussing a number of open problems and a perspective of research trend in future.

scholarly journals Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoxiao Sun ◽  
Yiwen Liu ◽  
Lingling An
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Dimensional Space ◽  
Essential Feature ◽  
Empirical Studies ◽  
Expression Patterns ◽  
Cell Types ◽  
Stochastic Gradient Descent ◽  
Reduction Techniques ◽  
Low Dimensional

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization, dimensionality reduction is necessary to capture gene expression patterns in low-dimensional space. Here we propose an ensemble method for simultaneous dimensionality reduction and feature gene extraction (EDGE) of scRNA-seq data. Different from existing dimensionality reduction techniques, the proposed method implements an ensemble learning scheme that utilizes massive weak learners for an accurate similarity search. Based on the similarity matrix constructed by those weak learners, the low-dimensional embedding of the data is estimated and optimized through spectral embedding and stochastic gradient descent. Comprehensive simulation and empirical studies show that EDGE is well suited for searching for meaningful organization of cells, detecting rare cell types, and identifying essential feature genes associated with certain cell types.

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