Today we released two new datasets!

1. Brener et al., cardiac tissue of COVID19 pt with microthrombi.

2. Miao et al., hypoplastic left heart patient.

3. Zhang et al., mesoderm and cardiac lineage study.

We have been working hard on the release of PlaqView 2.0 and its associated manuscript. Keep an eye out for us!

Today we released three new, and exciting datasets!

1. Yang et al. A vascular map of human Alzheimers disease. 

2. Zhao et al. A mouse atlas of the heart and aorta in standard vs. chow fed mouse.

3. Chowdhury et al. An immune cell focused study of human coronary arteries in different stages of disease.

We have been working hard on a new project! We are happy to announce the public beta of our new PlaqView-ATAC portal. This portal, a part of PlaqView, will be dedicated to sn/scATAC-seq data, powered by ArchR and R/Shiny.

One of the biggest obstacles with ATAC-seq data is data sparsity. These type of data are enormous, and it is extremely computationally intensive to calculate. Please let us know your experience and suggestions for improvement. 

In this portal, we feature the Turner_2022 data (preprint available). We are actively working on new datasets! Send us your data!

We were aware of the bug in the download functions. This was due to a load-balancer issue on the back-end and we have now fixed it! Please let us know if you have any other suggestions or improvements.

Many single-cell RNA-seq data comes with unstructured metadata, such as gender, sex, phenotype of the samples. Sometimes, it may be helpful to explore these metadata and query gene expression based on phenotypes. Here, we introduce Metadata Explorer.

To test the Metadata Explorer to the fullest extent, we recommend trying to load any of the Litvinukova datasets, since they have the most well-curated metadata.

Please send us feedback on what else you would like to see.

In this release we added scRNA-seq data from Xu et al. We processed this dataset from their SRA archive, please send us feedback if you note any error or would like to add any metadata we missed.

In this release we added scRNA-seq data from mouse experiments. In the next week we will be releasing a "Metadata explorer" that will further facilitate the exploration of the various disease states in these datasets! 

We thank Dr. Judith Sluimer for these following datasets.

Dataset Added:

• • Andueza et al. (Disturbed flow)

  • Dobnikar et al. (Myh11-CreERt2/EYFP and Myh11-CreERt2/Confetti Aorta, 10x data portion)

  • Gu et al. (Adventitial of ApoE KO)

It has been brought to our attention that Li_2020 dataset could not be processed in our standardized pipeline. Normally, low mitochondrial read cells (<5%) are deemed "high quality cells" (see Seurat notes). However in our analysis this dataset contained extremely high mitochondria reads (mitochondrial expression was an important aspect in the study). Because original authors did not make available their original scripts (see their data supplement), we did our best to recapitulate the original dataset.

In the end we decided to make available two versions of this dataset:

• original, full dataset with minimum filtering (cut off for mitochondrial read is 100% and nfeature was set to <7000/cell).

• lowmito, we filtered and processed this dataset in the same manner as all our other datasets (nfeature <2500/cell and mitochondrial read <5%). 

For further questions about this dataset please refer to their publication, or contact the original authors.

In this release we added scRNA-seq data from Delorey et al., who presented data from COVID-19 patient autopsies. 

We also introduce CIPR (Cell Identity PRedictor), which adds expanded capability to cross-examine cell identification labels against know references. CIPR calculates a Cell Identity Score based on how similar our PlaqView clusters are to the known references' expression patterns.

Dataset Added:

• • Delorey et al., 2021. 

In this release we focused on large heart atlases! Two (very) large human references, Tucker et al., from Circulation, and Litvinukova et al., from Nature Medicine, have been processed and made available.  Additionally, Litvinukova et al. divided their dataset into sub-datasets (e.g. focusing on just Fibroblasts, Immune Cells, etc.), and these subdatasets are also available on PlaqView. These are very large datasets so please be mindful of processing time when exploring. Please send feedback to plaqview@virginia.edu. 

Dataset Added:

• • Tucker et al., 2020. (Adult myocardium).

  • Litvinukova et al., 2020. (Adult whole heart).

    • Combined

    • Adipocytes

    • Atrial Cardiomyocytes

    • Ventricular Cardiomyocytes

    • Fibroblasts

    • Immune Cells

    • Vascular Tissue

    • Skeletal Muscles

  • Zernecke et al. (Meta-analysis of immune cells).

    • Mouse and Human

In this release we focused on making the app more user-friendly, adding more features and more disease-relevant datasets. Please send feedback to plaqview@virginia.edu. 

• UI Updates:

  • Layout Improvement for all pages to facilitate better user experience.

  • Changed the way we load dataset.

  • Added clearer instruction on how to use PlaqView.

  • Changed major function button colors to make clear how to run queries, etc.

• Feature Updates:

  • Added ability to download druggable genome UMAP.

  • Removed PAGA, slingshot, and SCORPIUS (due to underperformance in larger datasets)

  • Added 'Trajectory Explorer' where users can view the original trajectory as inferred by Monocle3, and user can re-defined cells of interests to re-calculate trajectory to further explore cell types of interest.

  • Added the ability to download original trajectory as well as new subset trajectory.

• Dataset Added:

  • Alencar et al., 2020 (human portion)

  • Li et al., 2020. 

  • Alsaigh et al., 2020.