scholarly journals Single-Cell Manifold Preserving Feature Selection (SCMER)

2020 ◽  
Author(s):  
Ken Chen ◽  
Shaoheng Liang ◽  
Vakul Mohanty ◽  
Jinzhuang Dou ◽  
Miao Qi ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cost Effective ◽  
High Dimensional ◽  
Compact Set ◽  
Cell Lineages ◽  
Molecular Features ◽  
Unsupervised Approach ◽  
Cell Data ◽  
Common Cell

Abstract A key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations (RCPs) that drive development, differentiation, and transformation. Molecular features such as genes and proteins defining RCPs are often unknown and difficult to detect from unenriched single-cell data, using conventional dimensionality reduction and clustering-based approaches. Here, we propose a novel unsupervised approach, named SCMER, which performs UMAP style dimensionality reduction via selecting a compact set of molecular features with definitive meanings. We applied SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis, and drug resistance and response. We found that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states ignored by current approaches. SCMER can be widely used for discovering novel molecular features in a high dimensional dataset, designing targeted, cost-effective assays for clinical applications, and facilitating multi-modality integration.

scholarly journals Single-Cell Manifold Preserving Feature Selection (SCMER)

2020 ◽  
Author(s):  
Shaoheng Liang ◽  
Vakul Mohanty ◽  
Jinzhuang Dou ◽  
Qi Miao ◽  
Yuefan Huang ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cost Effective ◽  
High Dimensional ◽  
Compact Set ◽  
Cell Lineages ◽  
Molecular Features ◽  
Unsupervised Approach ◽  
Cell Data ◽  
Common Cell

1AbstractA key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations (RCPs) that drive development, differentiation, and transformation. Molecular features such as genes and proteins defining RCPs are often unknown and difficult to detect from unenriched single-cell data, using conventional dimensionality reduction and clustering-based approaches.Here, we propose a novel unsupervised approach, named SCMER, which performs UMAP style dimensionality reduction via selecting a compact set of molecular features with definitive meanings.We applied SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis, and drug resistance and response. We found that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states ignored by current approaches.SCMER can be widely used for discovering novel molecular features in a high dimensional dataset, designing targeted, cost-effective assays for clinical applications, and facilitating multi-modality integration.

scholarly journals Evaluation of UMAP as an alternative to t-SNE for single-cell data

2018 ◽  
Author(s):  
Etienne Becht ◽  
Charles-Antoine Dutertre ◽  
Immanuel W. H. Kwok ◽  
Lai Guan Ng ◽  
Florent Ginhoux ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Reduction Technique ◽  
High Dimensional ◽  
The Past ◽  
Dimensionality Reduction Technique ◽  
Single Cell Rna Sequencing ◽  
Linear Dimensionality Reduction ◽  
Cell Data

AbstractUniform Manifold Approximation and Projection (UMAP) is a recently-published non-linear dimensionality reduction technique. Another such algorithm, t-SNE, has been the default method for such task in the past years. Herein we comment on the usefulness of UMAP high-dimensional cytometry and single-cell RNA sequencing, notably highlighting faster runtime and consistency, meaningful organization of cell clusters and preservation of continuums in UMAP compared to t-SNE.

scholarly journals SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions

2017 ◽  
Author(s):  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Embryo Development ◽  
Single Cells ◽  
High Dimensional ◽  
Data Sets ◽  
Rna Seq ◽  
Expression Levels ◽  
Cell Lineages ◽  
Cell Data

Measurements of gene expression levels for multiple genes in single cells provide a powerful approach to study heterogeneity of cell populations and cellular plasticity. While the expression levels of multiple genes in each cell are available in such data, the potential connections among the cells (e.g. the lineage relationship) are not directly evident from the measurement. Classifying cellular states and identifying transitions among those states are challenging due to many factors, including the small number of cells versus the large number of genes collected in the data. In this paper we adapt a classical self-organizing-map approach to single-cell gene expression data, such as those based on qPCR and RNA-seq. In this method (SOMSC), a cellular state map (CSM) is derived and employed to identify cellular states inherited in a population of measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers between the basins provide information on transitions among the cellular states. Consequently, paths of cellular state transitions (e.g. differentiation) and a temporal ordering of the measured single cells are obtained. Applied to a set of synthetic data, two single-cell qPCR data sets and two single-cell RNA-seq data sets for a simulated model of cell differentiation, and systems on the early embryo development, haematopoietic cell lineages, human preimplanation embryo development, and human skeletal muscle myoblasts differentiation, the SOMSC shows good capabilities in identifying cellular states and their transitions in the high-dimensional single-cell data. This approach will have broad applications in studying cell lineages and cellular fate specification.

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 Robust lineage reconstruction from high-dimensional single-cell data

2016 ◽  
Vol 44 (14) ◽  
pp. e122-e122 ◽  
Author(s):  
Gregory Giecold ◽  
Eugenio Marco ◽  
Sara P. Garcia ◽  
Lorenzo Trippa ◽  
Guo-Cheng Yuan
Keyword(s):  
Single Cell ◽  
High Dimensional ◽  
Cell Data

scholarly journals SPRING: a kinetic interface for visualizing high dimensional single-cell expression data

2016 ◽  
Author(s):  
Caleb Weinreb ◽  
Samuel Wolock ◽  
Allon Klein
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Nearest Neighbor ◽  
High Dimensional ◽  
K Nearest Neighbor ◽  
Link Type ◽  
Cell Gene Expression ◽  
Graph Layouts ◽  
Cell Expression ◽  
Cell Data

MotivationSingle-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, existing data visualization approaches are imperfect for studying continuous gene expression topologies.ResultsForce-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and allows manually exploration of different stable two-dimensional representations of the same data. We implemented an interactive web-tool to visualize single-cell data using force-directed graph layouts, called SPRING. SPRING reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool.Availabilityhttps://kleintools.hms.harvard.edu/tools/spring.html,https://github.com/AllonKleinLab/SPRING/[email protected], [email protected]

scholarly journals Supervised dimensionality reduction for exploration of single-cell data by Hybrid Subset Selection - Linear Discriminant Analysis

2022 ◽  
Author(s):  
Meelad Amouzgar ◽  
David R Glass ◽  
Reema Baskar ◽  
Inna Averbukh ◽  
Samuel C Kimmey ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Linear Discriminant Analysis ◽  
Single Cell ◽  
Cell Mass ◽  
Subset Selection ◽  
Cellular Heterogeneity ◽  
Linear Discriminant ◽  
Original Dataset ◽  
Cell Data

Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction enables visualization of data by representing cells in two-dimensional plots that capture the structure and heterogeneity of the original dataset. Visualizations contribute to human understanding of data and are useful for guiding both quantitative and qualitative analysis of cellular relationships. Existing algorithms are typically unsupervised, utilizing only measured features to generate manifolds, disregarding known biological labels such as cell type or experimental timepoint. Here, we repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling users to tailor visualizations to separate specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this flexible, computationally-efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes, such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality reduction algorithms and illustrate its utility and versatility for exploration of single-cell mass cytometry, transcriptomics and chromatin accessibility data.

scholarly journals scSemiAE: A Deep Model with Semi-Supervised Learning for Single-Cell Transcriptomics

2021 ◽  
Author(s):  
Jiayi Dong ◽  
Yin Zhang ◽  
Fei Wang
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Subpopulation ◽  
High Dimensions ◽  
Deep Model ◽  
Unsupervised Approach ◽  
Cell Subpopulations ◽  
Downstream Analysis ◽  
Low Dimensional ◽  
Public Datasets

Abstract Background: With the development of modern sequencing technology, hundreds of thousands of single-cell RNA-sequencing(scRNA-seq) profiles allow to explore the heterogeneity in the cell level, but it faces the challenges of high dimensions and high sparsity. Dimensionality reduction is essential for downstream analysis, such as clustering to identify cell subpopulations. Usually, dimensionality reduction follows unsupervised approach. Results: In this paper, we introduce a semi-supervised dimensionality reduction method named scSemiAE, which is based on an autoencoder model. It transfers the information contained in available datasets with cell subpopulation labels to guide the search of better low-dimensional representations, which can ease further analysis. Conclusions: Experiments on five public datasets show that, scSemiAE outperforms both unsupervised and semi-supervised baselines whether the transferred information embodied in the number of labeled cells and labeled cell subpopulations is much or less.

scholarly journals Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Rumana Rashid ◽  
Giorgio Gaglia ◽  
Yu-An Chen ◽  
Jia-Ren Lin ◽  
Ziming Du ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Single Cell ◽  
High Dimensional ◽  
Cancer Tissue ◽  
Lung Cancer Tissue ◽  
Immune Markers ◽  
Basic Tool ◽  
Human Tonsil ◽  
Biological Discovery ◽  
Cell Data

AbstractIn this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.

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