Single cell T-cell Atlas (V3)

<p>The attached datasets comprised of the merging of 21 high quality single cell T cell based dataset that had both the TCR-seq and GEx. The object contains ~1.3 paired TCR-seq with GEx in the Seurat Object (<a target="_blank" rel="noopener noreferrer">supercluster_added_ID-240531.rds</a>).</p> <p>We also included the original identifiers in the&nbsp;<a target="_blank" rel="noopener noreferrer">Sup_Update_labels.csv</a> a. See our <a href="https://stegor.readthedocs.io/en/latest/" target="_blank" rel="noopener">https://stegor.readthedocs.io/en/latest/</a> for how we processed the 12 datasets (V2) and decided on the current 47 T cell annotation models using scGate (TcellFunction). Additionally, based on collaborator recommendataion, we have also now included a simpler T cell annotion model in STEGO.R process (Tsimplefunctions).&nbsp;</p> <p>This is the accompanying data set for the paper entitled &lsquo;T cell receptor-centric approach to streamline multimodal single-cell data analysis.&rsquo;, which is currently available as a preprint (<a href="https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2" target="_blank" rel="noopener">https://www.biorxiv.org/content/10.1101/2023.09.27.559702v2</a>). Details on the origin of the datasets, and processing steps can be found there.</p> <p>The purpose of this atlas both the full dataset and down sampling version is to aid in improving the interpretability of other T cell based datasets. This can be done by adding in the down sampled object that contains up to 500 cells per annotation model. This dataset aims to improve the capacity to identify TCR-specific signature by ensuring a well covered background, which will improve the robustness of the FindMarker Function in Seurat package.</p> <p>&nbsp;</p>