Search results
The mapper and mapping positions store all positions in Python coordinates, i.e. 0-based. Many users are working with data that is 1-based, such as GenBank or HGVS. Each map position type has a “to_hgvs” and “to_genbank” method for easy conversion.
In this tutorial, we demonstrate gene-gene distance computation by selecting the genes expressed by 1% to 50% of cells among the top 500 variable genes. The expected running time for this computation is approximately 1 to 3 minutes.
9 sty 2019 · You can't measure the distance between a gene on chromosome 1, and a gene on chromosome 2. I thought of calculating the distance like this: start location of gene 2 - stop location of gene 1. Then, you have the distance between these genes.
Step 3. Generate a low-dimensional representation of genes (using Diffusion Map by default) based on the gene-gene Wasserstein distance matrix. Identify gene trajectories in a sequential manner. Step 4. Determine the order of genes along each gene trajectory.
Compute pairwise graph-based Wasserstein distance between gene distributions, which quantifies the minimum cost of transporting the distribution of a given gene into the distribution of another gene in the cell graph.
Specifically, optimal transport distances are calculated between gene distributions across the cell–cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation.
21 maj 2024 · Specifically, optimal transport distances are calculated between gene distributions across the cell–cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation.
