scholarly journals Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data

2023 ◽  
Author(s):  
Qiuyu Wu ◽  
Xiangyu Luo
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Nonparametric Bayesian ◽  
Cell Expression

scholarly journals Discovery of rare cells from voluminous single cell expression data

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Aashi Jindal ◽  
Prashant Gupta ◽  
Jayadeva ◽  
Debarka Sengupta
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Rare Cells ◽  
Cell Expression

scholarly journals ArrayExpress update – from bulk to single-cell expression data

2018 ◽  
Vol 47 (D1) ◽  
pp. D711-D715 ◽  
Author(s):  
Awais Athar ◽  
Anja Füllgrabe ◽  
Nancy George ◽  
Haider Iqbal ◽  
Laura Huerta ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Data ◽  
Cell Expression

scholarly journals Identifying signaling genes in spatial single-cell expression data

2020 ◽  
Author(s):  
Dongshunyi Li ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Interactions ◽  
Excitatory Neuron ◽  
Expression Data ◽  
Mixture Of Experts ◽  
Prediction Of Response ◽  
Technological Advances ◽  
A Cell ◽  
Cell Expression ◽  
Cell Cell

Abstract Motivation Recent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell–cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact. Results We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells. Availability and implementation MESSI is available at: https://github.com/doraadong/MESSI

scholarly journals Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes

2017 ◽  
Author(s):  
Patrick S Stumpf ◽  
Ben D MacArthur
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Regulatory Network ◽  
Cell Biology ◽  
Embryonic Stem ◽  
Network Activity ◽  
Expression Data ◽  
Cell Expression

AbstractThe molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the ‘average’ pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells – corresponding to naïve and formative pluripotent states and an early primitive endoderm state – and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell’s response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference

2019 ◽  
Vol 14 (3) ◽  
pp. 255-268 ◽  
Author(s):  
Wei Zhang ◽  
Wenchao Li ◽  
Jianming Zhang ◽  
Ning Wang
Keyword(s):  
Single Cell ◽  
In Silico ◽  
Gene Networks ◽  
Network Inference ◽  
Performance Metrics ◽  
Information Source ◽  
Inference Algorithm ◽  
Expression Data ◽  
Complementary Information ◽  
Cell Expression

Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference. Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution. Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list. Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source. Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

scholarly journals Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Fatemeh Behjati Ardakani ◽  
Kathrin Kattler ◽  
Karl Nordström ◽  
Nina Gasparoni ◽  
Gilles Gasparoni ◽  
...  
Keyword(s):  
Single Cell ◽  
Integrative Analysis ◽  
Expression Data ◽  
Bidirectional Promoters ◽  
Cell Expression

scholarly journals Probabilistic count matrix factorization for single cell expression data analysis

2019 ◽  
Vol 35 (20) ◽  
pp. 4011-4019 ◽  
Author(s):  
Ghislain Durif ◽  
Laurent Modolo ◽  
Jeff E Mold ◽  
Sophie Lambert-Lacroix ◽  
Franck Picard
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Matrix Factorization ◽  
Principal Component ◽  
Data Representation ◽  
Population Diversity ◽  
Supplementary Information ◽  
Expression Data ◽  
Statistical Point ◽  
Cell Expression

Abstract Motivation The development of high-throughput single-cell sequencing technologies now allows the investigation of the population diversity of cellular transcriptomes. The expression dynamics (gene-to-gene variability) can be quantified more accurately, thanks to the measurement of lowly expressed genes. In addition, the cell-to-cell variability is high, with a low proportion of cells expressing the same genes at the same time/level. Those emerging patterns appear to be very challenging from the statistical point of view, especially to represent a summarized view of single-cell expression data. Principal component analysis (PCA) is a most powerful tool for high dimensional data representation, by searching for latent directions catching the most variability in the data. Unfortunately, classical PCA is based on Euclidean distance and projections that poorly work in presence of over-dispersed count data with dropout events like single-cell expression data. Results We propose a probabilistic Count Matrix Factorization (pCMF) approach for single-cell expression data analysis that relies on a sparse Gamma-Poisson factor model. This hierarchical model is inferred using a variational EM algorithm. It is able to jointly build a low dimensional representation of cells and genes. We show how this probabilistic framework induces a geometry that is suitable for single-cell data visualization, and produces a compression of the data that is very powerful for clustering purposes. Our method is competed against other standard representation methods like t-SNE, and we illustrate its performance for the representation of single-cell expression data. Availability and implementation Our work is implemented in the pCMF R-package (https://github.com/gdurif/pCMF). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Using Neural Networks to Improve Single Cell RNA-Seq Data Analysis

2017 ◽  
Author(s):  
Chieh Lin ◽  
Siddhartha Jain ◽  
Hannah Kim ◽  
Ziv Bar-Joseph
Keyword(s):  
Neural Networks ◽  
Single Cell ◽  
Incomplete Data ◽  
Single Cells ◽  
Expression Data ◽  
Rna Seq ◽  
Retrieval Task ◽  
Database Queries ◽  
Reduced Dimension ◽  
Cell Expression

AbstractWhile only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges including questions related to handling the noisy and sometimes incomplete data, how to identify unique group of cells in such experiments and how to determine the state or function of specific cells based on their expression profile. To address these issues we develop and test a method based on neural networks (NN) for the analysis and retrieval of single cell RNA-Seq data. We tested various NN architectures, some biologically motivated, and used these to obtain a reduced dimension representation of the single cell expression data. We show that the NN method improves upon prior methods in both, the ability to correctly group cells in experiments not used in the training and the ability to correctly infer cell type or state by querying a database of tens of thousands of single cell profiles. Such database queries (which can be performed using our web server) will enable researchers to better characterize cells when analyzing heterogeneous scRNA-Seq samples.Supporting website: http://sb.cs.cmu.edu/scnn/Password for accessing the retrieval task webserver: scRNA-Seq

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