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

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.

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Gene Regulatory Network Inference Using Time-Stamped Cross-Sectional Single Cell Expression Data

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2016.12.117 ◽

2016 ◽

Vol 49 (26) ◽

pp. 147-152 ◽

Cited By ~ 3

Author(s):

Nan Papili Gao ◽

S.M. Minhaz Ud-Dean ◽

Rudiyanto Gunawan

Keyword(s):

Single Cell ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Network Inference ◽

Expression Data ◽

Gene Regulatory Network Inference ◽

Cross Sectional ◽

Gene Regulatory ◽

Cell Expression

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Discovery of rare cells from voluminous single cell expression data

Nature Communications ◽

10.1038/s41467-018-07234-6 ◽

2018 ◽

Vol 9 (1) ◽

Cited By ~ 20

Author(s):

Aashi Jindal ◽

Prashant Gupta ◽

Jayadeva ◽

Debarka Sengupta

Keyword(s):

Single Cell ◽

Expression Data ◽

Rare Cells ◽

Cell Expression

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ArrayExpress update – from bulk to single-cell expression data

Nucleic Acids Research ◽

10.1093/nar/gky964 ◽

2018 ◽

Vol 47 (D1) ◽

pp. D711-D715 ◽

Cited By ~ 115

Author(s):

Awais Athar ◽

Anja Füllgrabe ◽

Nancy George ◽

Haider Iqbal ◽

Laura Huerta ◽

...

Keyword(s):

Single Cell ◽

Expression Data ◽

Cell Expression

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Single cell transcriptomics reveal temporal dynamics of critical regulators of germ cell fate during mouse sex determination

10.1101/747279 ◽

2019 ◽

Cited By ~ 3

Author(s):

Chloé Mayère ◽

Yasmine Neirijnck ◽

Pauline Sararols ◽

Chris M Rands ◽

Isabelle Stévant ◽

...

Keyword(s):

Sex Determination ◽

Germ Cell ◽

Single Cell ◽

Cell Fate ◽

Gene Networks ◽

Network Inference ◽

Temporal Dynamics ◽

Intron Retention ◽

Gonad Development ◽

Altered Expression

SummaryDespite the importance of germ cell (GC) differentiation for sexual reproduction, the gene networks underlying their fate remain unclear. Here, we comprehensively characterize the gene expression dynamics during sex determination based on single-cell RNA sequencing of 14,914 XX and XY mouse GCs between embryonic days (E) 9.0 and 16.5. We found that XX and XY GCs diverge transcriptionally as early as E11.5 with upregulation of genes downstream of the Bone morphogenic protein (BMP) and Nodal/Activin pathways in XY and XX GCs, respectively. We also identified a sex-specific upregulation of genes associated with negative regulation of mRNA processing and an increase in intron retention consistent with a reduction in mRNA splicing in XY testicular GCs by E13.5. Using computational gene regulation network inference analysis, we identified sex-specific, sequential waves of putative key regulator genes during GC differentiation and revealed that the meiotic genes are regulated by positive and negative master modules acting in an antagonistic fashion. Finally, we found that rare adrenal GCs enter meiosis similarly to ovarian GCs but display altered expression of master genes controlling the female and male genetic programs, indicating that the somatic environment is important for GC function. Our data is available on a web platform and provides a molecular roadmap of GC sex determination at single-cell resolution, which will serve as a valuable resource for future studies of gonad development, function and disease.

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Identifying signaling genes in spatial single-cell expression data

Bioinformatics ◽

10.1093/bioinformatics/btaa769 ◽

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

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

10.1101/208470 ◽

2017 ◽

Cited By ~ 2

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.

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Biological network inference from single-cell multi-omics data using heterogeneous graph transformer

10.1101/2021.10.31.466658 ◽

2021 ◽

Author(s):

Anjun Ma ◽

Xiaoying Wang ◽

Cankun Wang ◽

Jingxian Li ◽

Tong Xiao ◽

...

Keyword(s):

Deep Learning ◽

Single Cell ◽

Gene Networks ◽

Network Inference ◽

Lung Tumor ◽

Superior Performance ◽

Omics Data ◽

Learning Platform ◽

Cell Clustering ◽

Cell Cell

We present DeepMAPS, a deep learning platform for cell-type-specific biological gene network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to infer cell-cell, cell-gene, and gene-gene relations simultaneously. The graph attention neural network considers a cell and a gene with both local and global information, making DeepMAPS more robust to data noises. We benchmarked DeepMAPS on 18 datasets for cell clustering and network inference, and the results showed that our method outperforms various existing tools. We further applied DeepMAPS on a case study of lung tumor leukocyte CITE-seq data and observed superior performance in cell clustering, and predicted biologically meaningful cell-cell communication pathways based on the inferred gene networks. To improve the feasibility and ensure the reproducibility of analyzing scMulti-omics data, we deployed a webserver with multi-functions and various visualizations. Overall, we valued DeepMAPS as a novel platform of the state-of-the-art deep learning model in the single-cell study and can promote the use of scMulti-omics data in the community.

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