Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Zhang, Shilu; Pyne, Saptarshi; Pietrzak, Stefan; Halberg, S.; McCalla, S. Grace; Siahpirani, Alireza Fotuhi; Sridharan, Rupa; Roy, Sushmita

doi:10.1038/s41467-023-38637-9

Cited by 45 publications

(27 citation statements)

References 80 publications

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“…Future work includes (1) adopting our computational framework to analyze deep transfer learning models with different network topologies and thereby identifying the best practice for deep transfer learning; (2) incorporating multi-omics datasets with images to improve the prediction performance using deep transfer learning; (3) developing a boosting mechanism to improve the performance of deep transfer learning in different biological problems [30,31]; and (4) utilizing our framework to speed up the learning process, e.g., TFeVGG19 was 802.67x faster than VGG19, trained from scratch.…”

Section: Discussionmentioning

confidence: 99%

“…Rapid advancement in single-cell technologies has played a key role in biological analysis and the existence of biological experiments related to gene regulatory networks [1][2][3]. By analyzing single-cell gene regulatory networks (SCGRNs), biologists can understand disease pathogenesis and treatment by unmasking various cellular mechanisms and functions pertaining to cellular heterogeneity presented at the tissue level [4,5].…”

Section: Introductionmentioning

confidence: 99%

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A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks

Alghamdi,

Turki

2023

Preprint

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Rapid progress in single-cell technologies has contributed to the availability of biological experiments pertaining to gene regulatory networks (GRNs). Although inferring GRNs can unveil in-sights about cellular mechanisms and functions to understand disease pathogenesis, existing deep learning tools are far from perfect and not providing appropriate interpretation as a guideline to explain the superior performance in the target task. Therefore, we provide an interpretation approach for our presented deep transfer learning (DTL) models to overcome such drawbacks, working as follows. We utilize several pre-trained models including SEResNet152, and SEResNeXt101. Then, we transfer knowledge from pre-trained models via keeping the weights in the convolutional base (i.e., feature extraction part) while modifying the classification part with the use of Adam optimizer to deal with classifying healthy controls and type 2 diabetes single-cell gene regulatory network (SCGRN) images. Another DTL models work in a similar manner but just with keeping weights of the bottom layers in the feature extraction unaltered while updating weights of consecutive layers through training from scratch. Experimental results on the whole 224 SCGRN images using 5-fold cross-validation show that our model (TFeSEResNeXT101) achieving the highest average balanced accuracy (BAC) of 0.97 and thereby significantly outperforming the baseline that resulted in an average BAC of 0.86. Moreover, the simulation study demonstrated that the superiority is attributed to the distributional conformance of model weight parameters obtained with Adam optimizer when coupled with weights from a pre-trained model.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks

Alghamdi,

Turki

2023

Preprint

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“…We further benchmark the inference accuracy of CeSpGRN on simulated datasets, and compare it with baseline GRN inference methods including scMTNI [22], GENIE3 [47], and CellOracle [48]. scMTNI is a recently proposed method that infers cluster-level GRNs using scATAC-seq and scRNA-seq data.…”

Section: Benchmark On Simulated Datasetsmentioning

confidence: 99%

CeSpGRN: Inferring cell-specific gene regulatory networks from single cell multi-omics and spatial data

Zhang

Jongseok

Song

et al. 2022

Preprint

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Single cell gene expression datasets have been used to uncover differences between single cells, leading to discoveries of new cell types and cell identities, which are usually defined by the transcriptome profiles of cells. Biological networks, in particular, gene regulatory networks (GRNs), can be viewed as another feature of the cells, that contributes to the uniqueness of each single cell. However, methods that reconstruct cell-specific GRNs are still missing. We propose CeSpGRN (Cell Specific GRN), which infers cell-specific GRNs from single cell gene expression data. CeSpGRN uses a Gaussian weighted kernel which allows the GRN of a given cell to be learned from the gene expression profile of this cell and cells that are upstream and downstream of this cell in the developmental process. CeSpGRN can be applied to infer cell-specific GRNs in cell populations of any trajectory or cluster structure, and it does not require time information of cells as additional input. We compared the performance of CeSpGRN and baseline methods on simulated datasets obtained under various settings. CeSpGRN showed superior performance in reconstructing the GRN for each cell, as well as in detecting the regulatory interactions that differ between cells. We also applied CeSpGRN to real datasets including THP-1 human myeloid monocytic leukemia cells and mouse embryonic stem cells, where CeSpGRN suggested a set of interactions between genes that rewire during the differentiation process in these cells.

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“…This development has surpassed the impressive trajectory of Moore's law regarding the number of cells measured (3) and opens new avenues for exploring individual cells in great detail. Single-cell transcripts can uncover regulatory mechanisms such as cellcell dependencies (4)(5)(6), reveal cellular differences between health and disease states (7)(8)(9)(10), clarify distinct cellular states (11,12) and determine gene regulatory mechanisms across cell populations (13)(14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

Cluster-free annotation of single cells using Earth mover’s distance-based classification

Forlin,

Tajvar,

Wang

et al. 2024

Preprint

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Grouping individual cells in clusters and annotating these based on feature expression is a common procedure in single-cell analysis pipelines. Multiple methods have been reported for single-cell mRNA sequencing and cytometry datasets where the vast majority rely on sequential 2-step procedures involving I) cell clustering based on notions of similarity and II) cluster annotation via manual or semiautomated methods. However, as arbitrary borders are drawn between more or less similar groups of cells, one cannot guarantee that all cells within a cluster are of the same type. Further, dimensionality reduction has been shown to cause considerable distortion in high-dimensional datasets and is prone to variable annotations of the same cell when relative changes occur in data composition. Another limitation of existing methods is that simultaneous analyses of large sets of cells are computationally expensive and difficult to scale for growing datasets or metanalyses across multiple datasets. Here we present an alternative method based on calculation of Earth Mover's Distance and a Bayesian classifier coupled to Random Forest, which annotates one cell at a time removing the need for prior clustering and resulting in improved accuracy, better scaling with increasing cell numbers and less computational resources needed.

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Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Cited by 45 publications

References 80 publications

A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks

A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks

CeSpGRN: Inferring cell-specific gene regulatory networks from single cell multi-omics and spatial data

Cluster-free annotation of single cells using Earth mover’s distance-based classification

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