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

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.

scholarly journals Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

2016 ◽  
Author(s):  
Gregory Giecold ◽  
Eugenio Marco ◽  
Lorenzo Trippa ◽  
Guo-Cheng Yuan
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Quantitative Estimate ◽  
Cell Lineage ◽  
Computational Method ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR, a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.

scholarly journals SOMSC: Self-Organization-Map for High-Dimensional Single-Cell Data of Cellular States and Their Transitions

2017 ◽  
Author(s):  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Single Cells ◽  
High Dimensional ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

AbstractMeasurement of gene expression levels for multiple genes in single cells provides a powerful approach to study heterogeneity of cell populations and cellular plasticity. While the expression levels of multiple genes in each cell are available in such data, the potential connections among the cells (e.g. the cellular state transition relationship) are not directly evident from the measurement. Classifying the cellular states, identifying their transitions among those states, and extracting the pseudotime ordering of cells are challenging due to the noise in the data and the high-dimensionality in the number of genes in the data. In this paper we adapt the classical self-organizing-map (SOM) approach for single-cell gene expression data (SOMSC), such as those based on single cell qPCR and single cell RNA-seq. In SOMSC, a cellular state map (CSM) is derived and employed to identify cellular states inherited in the population of the measured single cells. Cells located in the same basin of the CSM are considered as in one cellular state while barriers among the basins in CSM provide information on transitions among the cellular states. A cellular state transitions path (e.g. differentiation) and a temporal ordering of the measured single cells are consequently obtained. In addition, SOMSC could estimate the cellular state replication probability and transition probabilities. Applied to a set of synthetic data, one single-cell qPCR data set on mouse early embryonic development and two single-cell RNA-seq data sets, SOMSC shows effectiveness in capturing cellular states and their transitions presented in the high-dimensional single-cell data. This approach will have broader applications to analyzing cellular fate specification and cell lineages using single cell gene expression data

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

2018 ◽  
Author(s):  
Thomas D Sherman ◽  
Luciane T Kagohara ◽  
Raymon Cao ◽  
Raymond Cheng ◽  
Matthew Satriano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Time Course ◽  
Ground Truth ◽  
Real Data ◽  
Cellular Systems ◽  
Expression Data ◽  
Bioconductor Package ◽  
Data Set

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/

scholarly journals Inferring time-lagged causality using the derivative of single-cell expression

2021 ◽  
Author(s):  
Huan-Huan Wei ◽  
Hui Lu ◽  
Hongyu Zhao
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Single Cell ◽  
Causal Relationship ◽  
Gene Expression Data ◽  
Expression Data ◽  
Causal Relationships ◽  
Expression Levels ◽  
Gene Pairs ◽  
Time Lagged

AbstractMany computational methods have been developed for inferring causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by “RNA velocity”. We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships, with many of these inferred results supported by the literature.Author summaryComputational causal inference is a promising way to survey causal relationships between genes efficiently. Though many causal inference methods have been applied to gene expression data, none considers the time-lagged causal relationship, which means that some genes may take some time to affect their target genes with several reactions. If relationships between genes are time-lagged, the existing methods’ assumptions will be violated. The relationships will be challenging to recognize. We demonstrate that this is indeed the case through simulation. Therefore, we develop a method for inferring time-lagged causal relationships of single-cell gene expression data. We assume that a time-lagged causal relationship should present a strong association between the cause and the effect’s changing. To calculate such correlation, we first estimate the derivative of gene expression using the information from unspliced transcripts. Then, we use conditional independent tests to search gene pairs satisfying our assumption. Our results suggest that we could accurately infer time-lagged causal gene pairs validated by published literature. This method may complement gene regulatory analysis and provide candidate gene pairs for further controlled experiments.

scholarly journals Putative cell type discovery from single-cell gene expression data

2020 ◽  
Vol 17 (6) ◽  
pp. 621-628 ◽  
Author(s):  
Zhichao Miao ◽  
Pablo Moreno ◽  
Ni Huang ◽  
Irene Papatheodorou ◽  
Alvis Brazma ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Expression Data ◽  
Cell Type ◽  
Cell Gene Expression ◽  
Cell Gene
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