Dimension Reduction scClusters UMAPs
Users can view and compare side-by-side UMAPs' representing identified scATAC-seq clusters, origin of sample, unconstrained and constrained integration with scRNA-seq datasets, and integrated remapped clusters.
Peak browser of scATAC-seq clusters
Users can view and compare the single-cell chromatin accessibility data in scalable peak browser view along with co-accessibility of peaks on scATAC-seq modality.
Browser view of Peak2GeneLinks
User can visualize genome accessibility tracks of marker genes with peak co-accessibility
Feature of interest : Dimensionality Reduction UMAPs
Users can view and compare side-by-side UMAPs representing features of interest in GeneScoreMatrix, GeneIntegrationMatrix or MotifMatrix with a representative sequence logo. Download list of Motif Positions.
ArchR defined trajectory heatmaps
Users can visualize heatmaps from ArchR defined trajectory analysis on four different computed matrices.
Peak2GeneLinks heatmaps
Users can view Peak2GeneLinks identified across the dataset with ArchR.

Scope

hESCNeuroDiffParacet is an user-friendly, integrative open-source Shiny-based web app using R programming for visualization of massive single-cell chromatin accessibility data (scATAC-seq) of paracetamol exposure on human embryonic stem cells (hESCs) undergoing in vitro neuronal differentiation analyzed using on ArchR (Corces et al., 2021).

Approach

The ArchR objects saved in folders along with HDF5 formatted Arrow files are used for input in hESCNeuroDiffParacet. Here we show single-cell ATAC seq data of paracetamol exposure on human embryonic stem cells (hESCs) undergoing in vitro neuronal differentiation.

Data Visualization of ShinyArchR.UiO:
  • scATAC-seq clusters, unconstrained and constrained clusters on integrated reduced dimensions UMAP from ArchR objects
  • Peaks using plot browser tracks on clusters on scATAC-seq modality
  • Peaks2Genelinks tracks on single-cell RNA sequencing (scRNA-seq) integrated data with scATAC-seq using plot browser tracks. The co-accessibility among the genes can be visualized in the bottom panel
  • Heatmaps of pseudo time trajectory
  • Heatmaps of top 50 markers Peak2Genelinks in scATAC-seq and scRNA-seq

    • Sources

    Source code for ShinyArchR.UiO is available at ShinyArchR.UiO.

    For more details please visit https://Cancell.medisin.uio.no.

Acknowledgement

ShinyArchR.UiO was developed at University of Oslo.

Datasets

Our latest preprint associated with this dataset on multi-omics analysis of understanding effects of paracetamol exposure on hESC undegoing in-vitro neuronal differentiation is available at Spildrejorde et al,2022

Source codes for analysis is freely available at Github Neuralparacet. For more details on scATAC seq data analysis,please refer to full manual of ArchR and Nature genetics paper.

For more details on neuronal differentiation protocol, please see our Star Protocol article

Citations

Shiny and the tidyverse, of course.

Seurat and SingleCellExperiment are used in integration of the scRNA Seq data .

Batch Effect Correction wtih Harmony Harmony.

Heatmap plotting using Complex Heatmap ComplexHeatmap.

MAGIC to impute gene scores by smoothing signal across nearby cells Magic.

Contributions and Citation info

To know more about our other datsets associated with these Neuronal differentiation protocol please see Samara et al.2022, iScience.

If this visualization of chromatin accessibility of neuronal differentiation dataset is in any way helps you in your research work such that it warrants a citation, please cite theSharma et al, Bioinformatics,2021 and Samara et al.2022, iScience.

We thank authors for the acknowledgement of our efforts.

hESCNeuroDiffParacet is developed using ShinyArchR.UiO by Ankush Sharma with scientific input from Ragnhild Eskeland, and is maintained by Chromatin Biology Lab at, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Norway.

We thank the PharmaTox Research Initiative and Centre for Cancer Cell Reprogramming at UiO for engaging research work environments.

We also thank Norwegian Research Council for funding.

For more information about source code used for hESCNeuroDiff.ArchR please refer to ShinyArchR.UiO ShinyArchR.UiO repository. If you have any questions, requests, comments, or encounter any bugs in app, please raise issue on GitHub or contact corresponding authors

This source code is developed at University of Oslo as an open-source project mainly under the GPL license version 3 (see Github for details). As part of our commitment to the FAIR principles, this single-cell data generated at the University of Oslo is available to the public under the permissive CC BY 4.0 license. Datasets will be made (or is) available upon publication in peer reviewed journal at NCBI GEO GSE220027.