scRNA-seq analysis was conducted on primary gastric cancer samples ... Subsequent steps involved dimensionality reduction techniques, encompassing UMAP, TSNE, and the Louvain clustering algorithm, all ...

There are many heatmaps and tSNE / UMAP plots with unhelpful labels and no statistical ... placental villi in the presence and absence of spontaneous labor at term by scRNA-seq (Miller 2022)" (lines ...

Here, we report the molecular programmes and lineage progression patterns controlling human OA pathogenesis using single-cell RNA sequencing (scRNA-seq). Methods We performed unbiased ...

(h) tSNE projection of all GFP + cells color-coded by clusters ... aggregated expression of indicated genes partitioned by clusters. Taken together, our scRNA-seq profiling from two different genetic ...

Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop. CREsted is a Python package for ...

scRNA-seq combined with spatial transcriptomics (ST ... The phenograph algorithm was applied for clustering, while the tSNE algorithm was used for high-dimensional data reduction. Neighbor analysis ...

Now that we have our high quality cells, we need to first explore our data and identify any sources of unwanted variation. Then we need to normalize the data, perform variance stabilization and ...