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Dataset results
17 results for “RNA velocity”
SIRV: Spatial inference of RNA velocity at the single-cell resolution
<p>Spatial transcriptomics and scRNA-seq datasets used for integration and prediction of un/spliced expression for spatially measured genes using SIRV, used to infer the RNA velocity in the spatial context</p>
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of individual pathological entities
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of individual pathological entities<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>-i) AVM sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- ii) LGG sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from lower-grade gliomas (LGG) (a brain tumor)<br><br><em>- iii) GBM sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- iv) MET sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- v) MEN sorted endothelial cells_RNA velocity.zip: </em><br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain meningioma (MEN) (a brain tumor).</p>
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of fetal and adult/control brains
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of individual fetal and adult/control brains entities<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Fetal CNS sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain (Fetal CNS).</p> <p><em>- ii) Adult control brain (temporal lobe) sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells isolated from adult/control brains (temporal lobes).</p>
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of brain tumors
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of brain tumors:<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><br>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>- Overall merge of tumor sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas). </p>
VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories
<p>Single cell transcriptomic technologies enable genome-wide gene expression measurements in individual cells but can only provide a static snapshot of cell states. RNA velocity analysis can infer cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to consistently capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bio- conductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/.</p> <p> </p> <p>Here, we have included the data used in the package vignettes: <br> <br> 1) Pancreas endocrinogenesis data was obtained from Bergen et. al. Nature Biotechnology 2020 and Bastidas-Ponce et. al. Development 2019 via the scVelo package. </p> <p>- pancreas.rda contains spliced and unspliced count matrices, list of cluster IDs, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R</p> <p>- pancreasWithGap.rda includes the same data as in pancreas rda but with a subset of intermediate cells () removed to simulate data with missing intermediates. </p> <p>2) MERFISH data was obtained from Xia et. al. PNAS 2019</p> <p>- MERFISH.rda contains nuclear and cytoplasmic counts, colors used for plotting based on Louvain clustering, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R</p>
h5ad (adata) for RNA velocity
<pre>adata files for each cell population and condition. The files were generated using spliced/unspliced transcripts and merged with filtered features, barcodes and counts and, pre-calculated in Seurat. One can load the datasets in python using the command below: </pre> <pre><code class="language-python">import scvelo as scv adata = scv.read('uninfected_keratinocyte.h5ad')</code></pre> <p> </p>
RNA velocity analysis of the integrated single cell atlas of neural crest lineages along the posterior developing zebrafish
<p>H5AD files of an RNA velocity analysis of the integrated single cell atlas of neural crest lineages along the posterior developing zebrafish . For this, we have used Velocyto and scVelo. The notebooks to reproduce the scVelo part are here https://github.com/brunicardoso/e-signal-lab/blob/main/scvelo_sox10_integration48_68h__Uribe_using_original_seurat_metadata.ipynb .</p> <p>This analysis was based on data published here https://elifesciences.org/articles/60005</p>
RNA velocity code and data of control/Vcl cKO/public CNCC
<p>RNA velocity code and data of control/Vcl cKO/public CNCC</p>
Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning
GEO Series GSE167609. Homo sapiens; Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Projecting development of TB-IRIS using RNA velocity of whole blood
GEO Series GSE274086. Homo sapiens. 95 samples. Type: Expression profiling by high throughput sequencing.
RNA velocity objects of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of pathological entities (brain tumors and brain vascular malformations)
<p><strong>RNA velocity objects (zip files of .h5ad) of FACS-sorted (CD31+/CD45-) endothelial cells of overall merge of pathological entities (brain tumors and brain vascular malformations):<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><br>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><em>- Overall merge of pathological sorted endothelial cells_RNA velocity.zip:</em> <br> -> this is a zip file of an RNA velocity object (.h5ad) of the overall merge of FACS-sorted (CD31+/CD45-) endothelial cells isolated from pathological entities including brain tumors (lower-grade glioma, high-grade glioma (glioblastoma), brain metastasis, meningiomas) and brain vascular malformations (brain arteriovenous malformations). </p>
RNA velocity analysis of human retinal organoids
<p>The scVelo package (Bergen, V., Lange, M., Peidli, S. et al. , 2020) was used to derive RNA velocities from two human retinal organoid models.</p> <p>Using the PAGA tool (Wolf, F.A., Hamey, F.K., Plass, M. et al., 2019), abstracted graphs of the cell types and the pseudotime assessment were visualized</p>
DOT1L activity limits transcription elongation velocity and favors RNAPII pausing to facilitate mutagenesis by AID. [RNA-Seq]
GEO Series GSE233974. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (mESCs)
GEO Series GSE167608. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Gene Regulation Dynamics during the Cell Cycle uncovered by RNA velocity and deep-learning (IMR90)
GEO Series GSE167607. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations
GEO Series GSE250148. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Longer read lengths are required for RNA velocity analysis of single-cell RNA-sequencing with paired immune repertoire and transcriptome profiling
GEO Series GSE169657. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.