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Seurat FindNeighbours (version 4.0.4+galaxy0)

Choose the format of the input:

Seurat RDS, Seurat H5, Single Cell Experiment RDS, Loom or AnnData

RDS file:

Select RDS file(s) with Seurat object for input

Choose the format of the output:

Seurat, Single Cell Experiment, AnnData or Loom

Features:

Comma-separated list of genes to use for building SNN.

Features file:

Text file with one gene per line to use for building SNN. Overrides Features.

Plot SNN on tSNE:

Plot SNN graph on tSNE coordinates

Reduction:

Reduction to use as input for building the SNN

Dimensions:

Dimensions of reduction to use as input. A comma-separated list of the dimensions to use in construction of the SNN graph (e.g. To use the first 5 PCs, pass 1,2,3,4,5).

Assay:

Assay to use in construction of SNN

Distance matrix:

Boolean value of whether the provided matrix is a distance matrix; note, for objects of class dist, this parameter will be set automatically.

Defines k for the k-nearest neighbor algorithm

Compute SNN:

Also compute the shared nearest neighbor graph

Prune SNN:

Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the strigency of pruning (0 --- no pruning, 1 --- prune everything).

NN method:

Method for nearest neighbor finding. Options include: rann (default), annoy

Annoy metric:

Distance metric for annoy. Options include: euclidean (default), cosine, manhattan, and hamming

Graph name:

Name of graph to use for the clustering algorithm.

NN Error bound:

Error bound when performing nearest neighbor seach using RANN; default of 0.0 implies exact nearest neighbor search

Force recalc:

Force recalculation of SNN

What it does

Constructs a Shared Nearest Neighbor (SNN) Graph for a given dataset. We first determine the k-nearest neighbors of each cell. We use this knn graph to construct the SNN graph by calculating the neighborhood overlap (Jaccard index) between every cell and its k.param nearest neighbors.

Seurat is a toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. It is developed and maintained by the Satija Lab at NYGC. Seurat aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data.

Inputs

Seurat RDS object. Probably the one produced by Seurat create object.

Subset names. A list of attributes to subset on, colon separated (:).

Low thresholds. A minimum value for each of the attributes set in subset names, again, colon separated (:). Optional.

High thresholds. A maximum value for each of the attributes set in subset names, again, colon separated (:). Optional.

Cells to use. A list of cell names/idenfifiers to filter positively by.

Outputs

Seurat RDS object filtered according to the inputs.

Version history 4.0.0: Moves to Seurat 4.0.0, introducing a number of methods for merging datasets, plus the whole suite of Seurat plots. Pablo Moreno with funding from AstraZeneca.

3.2.3+galaxy0: Moves to Seurat 3.2.3 and introduce convert method, improving format interconversion support.

3.1.2_0.0.8: Update metadata parsing

3.1.1_0.0.7: Exposes perplexity and enables tab input.

3.1.1_0.0.6+galaxy0: Moved to Seurat 3.

Find clusters: removed dims-use, k-param, prune-snn.

2.3.1+galaxy0: Improved documentation and further exposition of all script's options. Pablo Moreno, Jonathan Manning and Ni Huang, Expression Atlas team https://www.ebi.ac.uk/gxa/home at EMBL-EBI https://www.ebi.ac.uk/. Parts obtained from wrappers from Christophe Antoniewski (GitHub drosofff) and Lea Bellenger (GitHub bellenger-l).

0.0.1: Initial contribution. Maria Doyle (GitHub mblue9).