Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion

Identifying the phenotypes and interactions of various cells is the primary objective in cellular heterogeneity dissection. A key step of this methodology is to perform unsupervised clustering, which, however, often suffers challenges of the high level of noise, as well as redundant information. To overcome the limitations, we proposed self-diffusion on local scaling affinity (LSSD) to enhance cell similarities’ metric learning for dissecting cellular heterogeneity. Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. Our approach implements the self-diffusion process by propagating the affinity matrices to further improve the cell similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness, we applied LSSD on two simulated and four real scRNA-seq datasets. Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability.

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Single-cell RNA-seq data clustering: A survey with performance comparison study

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720020400053 ◽

2020 ◽

Vol 18 (04) ◽

pp. 2040005

Author(s):

Ruiyi Li ◽

Jihong Guan ◽

Shuigeng Zhou

Keyword(s):

Single Cell ◽

Data Clustering ◽

Performance Metrics ◽

Clustering Algorithms ◽

Cell Types ◽

Performance Comparison ◽

Cellular Heterogeneity ◽

Clustering Methods ◽

Multiple Perspectives ◽

Underlying Mechanisms

Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some of their counterparts usually using different performance metrics. Consequently, there lacks an accurate and complete picture of their merits and demerits, which makes it difficult for users to select proper algorithms for analyzing their data. To fill this gap, we first do a review on the major existing scRNA-seq data clustering methods, and then conduct a comprehensive performance comparison among them from multiple perspectives. We consider 13 state of the art scRNA-seq data clustering algorithms, and collect 12 publicly available real scRNA-seq datasets from the existing works to evaluate and compare these algorithms. Our comparative study shows that the existing methods are very diverse in performance. Even the top-performance algorithms do not perform well on all datasets, especially those with complex structures. This suggests that further research is required to explore more stable, accurate, and efficient clustering algorithms for scRNA-seq data.

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Effects of sample size on plant single-cell RNA profiling

10.21203/rs.3.rs-549533/v1 ◽

2021 ◽

Author(s):

Longbiao Guo ◽

Hongyu Chen ◽

Xinxin Yin ◽

Xi Chen ◽

Qinjie Chu ◽

...

Keyword(s):

Sample Size ◽

Single Cell ◽

High Reliability ◽

Cell Types ◽

Cell Number ◽

Cellular Heterogeneity ◽

Root Cells ◽

Rna Profiling ◽

Molecular Features ◽

Cell Clustering

Abstract Background: Single-cell RNA (scRNA) profiling or scRNA-sequencing (scRNA-seq) is a rapidly developing technology and an important frontier of molecular biology science. scRNA profiling makes it possible to parallelly investigate diverse molecular features of multiple types of cells in a given plant tissue, and promotes elucidation of cellular heterogeneity and discovery of developmental processes underpinning cell differentiation. While it is assumed that the power of scRNA profiling in uncovering cellular heterogeneity largely depends on the depth of scRNA-seq, no study about the effect of the sequenced cell numbers on the power of plant scRNA-seq has ever been reported. Results: In this study, on the basis of analyzing the sample coverage of 1,244 available scRNA-seq studies (including 30 in plants) and the effect of sample coverage on cell clustering and identification of cell types, we evaluated the effects of sample size (i.e., cell number) on the outcome of single cell transcriptome analysis by sampling different number of cells from a pool of ~57,000 Arabidopsis thaliana root cells integrated from five published studies. Our results indicated that the most significant principle components could be achieved when 20,000-30,000 cells were sampled, a relatively high reliability of cell clustering could be achieved by using ~20,000 cells with little further improvement by using more cells, 96% of the differentially expressed genes could be successfully identified with no more than 20,000 cells, and a relatively stable pseudotime could be estimated in the sub-sample with 5,000 cells. Conclusions: Our results imply that ~20,000 (or 10,000 - 30,000[1] ) cells are enough for profiling Arabidopsis root cells using scRNA-seq, although the applicability of this number to other Arabidopsis tissues and other plants is yet to be further determined by analyzing scRNA-seq data generated from diverse tissues of different plant species. Nevertheless, our results provide a general guide for optimizing sample size to be used in plant scRNA-seq studies. Change to “or up to 300000”?

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DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

10.1101/2020.10.20.347195 ◽

2020 ◽

Author(s):

Jing Su ◽

Qianqian Song

Keyword(s):

Spatial Data ◽

Spatial Information ◽

Cell Types ◽

Cellular Heterogeneity ◽

Superior Performance ◽

Gene Expressions ◽

Mouse Cortex ◽

Hippocampus Slice ◽

Transcriptomics Data ◽

High Level

AbstractRecent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named DSTG, to accurately deconvolute the observed gene expressions at each spot and recover its cell constitutions, thus achieve high-level segmentation and reveal spatial architecture of cellular heterogeneity within tissues. DSTG not only demonstrates superior performance on synthetic spatial data generated from different protocols, but also effectively identifies spatial compositions of cells in mouse cortex layer, hippocampus slice, and pancreatic tumor tissues. In conclusion, DSTG accurately uncovers the cell states and subpopulations based on spatial localization.

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Clustering Deviation Index (CDI): A robust and accurate unsupervised measure for evaluating scRNA-seq data clustering

10.1101/2022.01.03.474840 ◽

2022 ◽

Author(s):

Jiyuan Fang ◽

Cliburn Chan ◽

Kouros Owzar ◽

Liuyang Wang ◽

Diyuan Qin ◽

...

Keyword(s):

Single Cell ◽

Data Clustering ◽

Goodness Of Fit ◽

Cellular Heterogeneity ◽

Clustering Methods ◽

Tuning Parameters ◽

Deviation Index ◽

Cell Clustering ◽

Single Cell Rna Sequencing ◽

Cell Data

Single-cell RNA-sequencing (scRNA-seq) technology allows us to explore cellular heterogeneity in the transcriptome. Because most scRNA-seq data analyses begin with cell clustering, its accuracy considerably impacts the validity of downstream analyses. Although many clustering methods have been developed, few tools are available to evaluate the clustering "goodness-of-fit" to the scRNA-seq data. In this paper, we propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. Particularly, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.

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Topological Sholl Descriptors for Neuronal Clustering and Classification

10.1101/2021.01.15.426800 ◽

2021 ◽

Author(s):

Reem Khalil ◽

Sadok Kallel ◽

Ahmad Farhat ◽

Paweł Dłotko

Keyword(s):

Metric Spaces ◽

Metric Learning ◽

Functional Characterization ◽

Neuronal Cell ◽

Cell Types ◽

Brain Regions ◽

Neuronal Morphology ◽

Clustering Methods ◽

Clustering And Classification

Variations in neuronal morphology among cell classes, brain regions, and animal species are thought to underlie known heterogeneities in neuronal function. Thus, accurate quantitative descriptions and classification of large sets of neurons is essential for functional characterization. However, unbiased computational methods to classify groups of neurons are currently scarce. We introduce a novel, robust, and unbiased method to study neuronal morphologies. We develop mathematical descriptors that quantitatively characterize structural differences among neuronal cell types and thus classify them. Each descriptor that is assigned to a neuron is a function of a distance from the soma with values in real numbers or more general metric spaces. Standard clustering methods enhanced with detection and metric learning algorithms are then used to objectively cluster and classify neurons. Our results illustrate a practical and effective approach to the classification of diverse neuronal cell types, with the potential for discovery of putative subclasses of neurons.

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Ultrasturcture of cerebellar cells and neuropil in rats subjected to partial global cerebral ischemia

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014227x ◽

1986 ◽

Vol 44 ◽

pp. 126-127

Author(s):

R.V.W. Dimlich ◽

M.H. Biros

Keyword(s):

Purkinje Cells ◽

Glial Cells ◽

Granule Cells ◽

Molecular Layer ◽

Cell Types ◽

Primary Objective ◽

Global Cerebral Ischemia ◽

Forebrain Ischemia ◽

Substantial Evidence ◽

Cerebellar Cells

Although a previous study in this laboratory determined that Purkinje cells of the rat cerebellum did not appear to be damaged following 30 min of forebrain ischemia followed by 30 min of reperfusion, it was suggested that an increase in rough endoplasmic reticulum (RER) and/or polysomes had occurred in these cells. The primary objective of the present study was to morphometrically determine whether or not this increase had occurred. In addition, since there is substantial evidence that glial cells may be affected by ischemia earlier than other cell types, glial cells also were examined. To ascertain possible effects on other cerebellar components, granule cells and neuropil near Purkinje cells as well as neuropil in the molecular layer also were evaluated in this investigation.

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Self-diffusion scalings in dense granular flows

Soft Matter ◽

10.1039/d0sm01846e ◽

2021 ◽

Author(s):

Riccardo Artoni ◽

Michele Larcher ◽

James T. Jenkins ◽

Patrick Richard

Keyword(s):

Shear Rate ◽

Solid Fraction ◽

Granular Flows ◽

The Self ◽

Granular Temperature ◽

Restitution Coefficient ◽

Self Diffusion ◽

Diffusivity Tensor ◽

Dense Granular Flows

The self-diffusivity tensor in homogeneously sheared dense granular flows is anisotropic. We show how its components depend on solid fraction, restitution coefficient, shear rate, and granular temperature.

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Self-Diffusion Coefficients, Aggregation Numbers and the Range of Existence of Spherical Micelles of Oxyethylated Alkylphenols

Applied Magnetic Resonance ◽

10.1007/s00723-021-01323-4 ◽

2021 ◽

Author(s):

Victor P. Arkhipov ◽

Natalia A. Kuzina ◽

Andrei Filippov

Keyword(s):

Aqueous Solutions ◽

Diffusion Coefficients ◽

The Self ◽

Self Diffusion ◽

Aggregation Numbers ◽

Temperature Ranges ◽

Spherical Micelles ◽

Limiting Values

AbstractAggregation numbers were calculated based on measurements of the self-diffusion coefficients, the effective hydrodynamic radii of micelles and aggregates of oxyethylated alkylphenols in aqueous solutions. On the assumption that the radii of spherical micelles are equal to the lengths of fully extended neonol molecules, the limiting values of aggregation numbers corresponding to spherically shaped neonol micelles were calculated. The concentration and temperature ranges under which spherical micelles of neonols are formed were determined.

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Direct determination of the self-diffusion mechanism in bccβ-titanium

Physical Review B ◽

10.1103/physrevb.39.5025 ◽

1989 ◽

Vol 39 (8) ◽

pp. 5025-5034 ◽

Cited By ~ 40

Author(s):

G. Vogl ◽

W. Petry ◽

Th. Flottmann ◽

A. Heiming

Keyword(s):

Diffusion Mechanism ◽

Direct Determination ◽

The Self ◽

Self Diffusion

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Single-cell transcriptomics following ischemic injury identifies a role for B2M in cardiac repair

Communications Biology ◽

10.1038/s42003-020-01636-3 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Bas Molenaar ◽

Louk T. Timmer ◽

Marjolein Droog ◽

Ilaria Perini ◽

Danielle Versteeg ◽

...

Keyword(s):

Single Cell ◽

Communication Networks ◽

Cardiac Remodeling ◽

Cardiac Injury ◽

Ischemic Injury ◽

Cell Types ◽

Repair Process ◽

Cardiac Repair ◽

Cellular Heterogeneity ◽

Intercellular Signaling

AbstractThe efficiency of the repair process following ischemic cardiac injury is a crucial determinant for the progression into heart failure and is controlled by both intra- and intercellular signaling within the heart. An enhanced understanding of this complex interplay will enable better exploitation of these mechanisms for therapeutic use. We used single-cell transcriptomics to collect gene expression data of all main cardiac cell types at different time-points after ischemic injury. These data unveiled cellular and transcriptional heterogeneity and changes in cellular function during cardiac remodeling. Furthermore, we established potential intercellular communication networks after ischemic injury. Follow up experiments confirmed that cardiomyocytes express and secrete elevated levels of beta-2 microglobulin in response to ischemic damage, which can activate fibroblasts in a paracrine manner. Collectively, our data indicate phase-specific changes in cellular heterogeneity during different stages of cardiac remodeling and allow for the identification of therapeutic targets relevant for cardiac repair.

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