CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

AbstractBioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

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CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer

PLoS Computational Biology ◽

10.1371/journal.pcbi.1006935 ◽

2019 ◽

Vol 14 (4) ◽

pp. e1006935 ◽

Cited By ~ 1

Author(s):

Thomas D. Sherman ◽

Luciane T. Kagohara ◽

Raymon Cao ◽

Raymond Cheng ◽

Matthew Satriano ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Statistical Modeling ◽

Gene Expression Data ◽

Time Course ◽

Expression Data ◽

Bioconductor Package ◽

Cell Gene Expression ◽

Cell Gene

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dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

10.1101/2021.08.28.458048 ◽

2021 ◽

Author(s):

Yu Xu ◽

Jiaxing Chen ◽

Aiping Lyu ◽

William K Cheung ◽

Lu Zhang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Time Course ◽

Target Genes ◽

Neighborhood Context ◽

Specific Gene ◽

Expression Data ◽

Regulatory Interactions ◽

Gene Functions

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely applied to reconstruct the cell-type-specific gene regulatory networks by exploring the dynamic changes of gene expression between transcription factors (TFs) and their target genes. The existing algorithms were commonly designed to analyze bulk gene expression data and could not deal with the dropouts and cell heterogeneity in scRNA-seq data. In this paper, we developed dynDeepDRIM that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. dynDeepDRIM integrated the primary image, neighbor images with time-course into a four-dimensional tensor and trained a convolutional neural network to predict the direct regulatory interactions between TFs and genes. We evaluated the performance of dynDeepDRIM on five time-course gene expression datasets. dynDeepDRIM outperformed the state-of-the-art methods for predicting TF-gene direct interactions and gene functions. We also observed gene functions could be better performed if more neighbor images were involved.

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Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1618 ◽

2011 ◽

Vol 10 (1) ◽

Cited By ~ 6

Author(s):

Guro Dørum ◽

Lars Snipen ◽

Margrete Solheim ◽

Solve Saebo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Networks ◽

Simulated Data ◽

Real Data ◽

Biological Knowledge ◽

Expression Data ◽

Data Set ◽

Gene Set ◽

Network Information

Gene set analysis methods have become a widely used tool for including prior biological knowledge in the statistical analysis of gene expression data. Advantages of these methods include increased sensitivity, easier interpretation and more conformity in the results. However, gene set methods do not employ all the available information about gene relations. Genes are arranged in complex networks where the network distances contain detailed information about inter-gene dependencies. We propose a method that uses gene networks to smooth gene expression data with the aim of reducing the number of false positives and identify important subnetworks. Gene dependencies are extracted from the network topology and are used to smooth genewise test statistics. To find the optimal degree of smoothing, we propose using a criterion that considers the correlation between the network and the data. The network smoothing is shown to improve the ability to identify important genes in simulated data. Applied to a real data set, the smoothing accentuates parts of the network with a high density of differentially expressed genes.

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ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

10.1101/633958 ◽

2019 ◽

Author(s):

Samuel A Danziger ◽

David L Gibbs ◽

Ilya Shmulevich ◽

Mark McConnell ◽

Matthew WB Trotter ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Immune Cell ◽

De Novo ◽

Cell Types ◽

Expression Data ◽

Cell Type ◽

Data Set ◽

Rnaseq Data

AbstractImmune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

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sciCAN: Single-cell chromatin accessibility and gene expression data integration via Cycle-consistent Adversarial Network

10.1101/2021.11.30.470677 ◽

2021 ◽

Author(s):

Yang Xu ◽

Edmon Begoli ◽

Rachel Patton McCord

Keyword(s):

Gene Expression ◽

Data Integration ◽

Single Cell ◽

Gene Expression Data ◽

Chromatin Accessibility ◽

Cellular Systems ◽

Expression Data ◽

Adversarial Network ◽

Cell Technologies ◽

Cell Data

The booming single-cell technologies bring a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge and gains more and more attention within the community. Here, we present a novel adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 3 state-of-the-art (SOTA) methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with better balance of mutual transferring between modalities than the other 3 SOTA methods. We further applied sciCAN to 10X Multiome data and confirmed the integrated representation preserves information of the hematopoietic hierarchy. Finally, we investigated CRSIPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.

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