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 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 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 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.

scholarly journals Meeting the challenges of high-dimensional data analysis in immunology

2018 ◽  
Author(s):  
Subarna Palit ◽  
Fabian J. Theis ◽  
Christina E. Zielinski
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Immune Regulation ◽  
Cellular Heterogeneity ◽  
High Dimensionality ◽  
High Dimensional ◽  
Mass Cytometry ◽  
Analysis Techniques ◽  
The Impact ◽  
Cell Data

AbstractRecent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.

Statistical test of structured continuous trees based on discordance matrix

2019 ◽  
Vol 35 (23) ◽  
pp. 4962-4970
Author(s):  
Xiangqi Bai ◽  
Liang Ma ◽  
Lin Wan
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Matrix Theory ◽  
Continuous Process ◽  
Cell Types ◽  
Supplementary Information ◽  
High Dimensional ◽  
Intrinsic Structure ◽  
Statistical Framework ◽  
Cell Data

Abstract Motivation Cell fate determination is a continuous process in which one cell type diversifies to other cell types following a hierarchical path. Advancements in single-cell technologies provide the opportunity to reveal the continuum of cell progression which forms a structured continuous tree (SCTree). Computational algorithms, which are usually based on a priori assumptions on the hidden structures, have previously been proposed as a means of recovering pseudo trajectory along cell differentiation process. However, there still lack of statistical framework on the assessments of intrinsic structure embedded in high-dimensional gene expression profile. Inherit noise and cell-to-cell variation underlie the single-cell data, however, pose grand challenges to testing even basic structures, such as linear versus bifurcation. Results In this study, we propose an adaptive statistical framework, termed SCTree, to test the intrinsic structure of a high-dimensional single-cell dataset. SCTree test is conducted based on the tools derived from metric geometry and random matrix theory. In brief, by extending the Gromov–Farris transform and utilizing semicircular law, we formulate the continuous tree structure testing problem into a signal matrix detection problem. We show that the SCTree test is most powerful when the signal-to-noise ratio exceeds a moderate value. We also demonstrate that SCTree is able to robustly detect linear, single and multiple branching events with simulated datasets and real scRNA-seq datasets. Overall, the SCTree test provides a unified statistical assessment of the significance of the hidden structure of single-cell data. Availability and implementation SCTree software is available at https://github.com/XQBai/SCTree-test. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SCHNEL: Scalable clustering of high dimensional single-cell data

2020 ◽  
Author(s):  
Tamim Abdelaal ◽  
Paul de Raadt ◽  
Boudewijn P.F. Lelieveldt ◽  
Marcel J.T. Reinders ◽  
Ahmed Mahfouz
Keyword(s):  
Single Cell ◽  
Graph Clustering ◽  
Original Data ◽  
Large Datasets ◽  
Supplementary Information ◽  
High Dimensional ◽  
Sequencing Data ◽  
Novel Approach ◽  
Cellular Markers ◽  
Cell Data

AbstractMotivationSingle cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.ResultsWe developed SCHNEL, a scalable, reliable and automated clustering tool for high-dimensional single-cell data. SCHNEL transforms large high-dimensional data to a hierarchy of datasets containing subsets of data points following the original data manifold. The novel approach of SCHNEL combines this hierarchical representation of the data with graph clustering, making graph clustering scalable to millions of cells. Using seven different cytometry datasets, SCHNEL outperformed three popular clustering tools for cytometry data, and was able to produce meaningful clustering results for datasets of 3.5 and 17.2 million cells within workable timeframes. In addition, we show that SCHNEL is a general clustering tool by applying it to single-cell RNA sequencing data, as well as a popular machine learning benchmark dataset MNIST.Availability and ImplementationImplementation is available on GitHub (https://github.com/paulderaadt/HSNE-clustering)[email protected] informationSupplementary data are available at Bioinformatics online.

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