Note the galaxy link they should use: https://usegalaxy.org/join-training/itn-at-moffitt-2025/

Tutorial Steps

Get Data onto Galaxy and generate a Seurat object

EBI Data Retrieval

Seurat Read10X

QC and further processing of the Seurat object

QC: Visualize Counts

QC: Visualize Features

Finding our filtering parameters

Filter Cells

Normalize Data

Find Variable Genes

Scale Data

Dimensionality Reduction

Run PCA

Run UMAP

Note here this walkthrough diverges from the Galaxy Training tutorial, rearranging the dimensionality reduction steps.

Change the input such that for this tutorial it should be the PCA Processed Seurat Object (instead of the Preprocessed Seurat Object with Clusters)

Keep the Dims 1:15 argument.

The Reduction argument autofilled to be PCA and I kept it.

Plot Gapdh

Use the output of the previous step “Seurat UMAP on data X: Seurat RDS” as the input for this one.

Keep the Plot_type_selector as FeaturePlot and Features as Gapdh as described in the Galaxy Training Tutorial.

Plot Il2ra

Use the output of the Run UMAP step “Seurat UMAP on data X: Seurat RDS” as the input for this one.

Keep the Plot_type_selector as FeaturePlot and Features as Il2ra as described in the Galaxy Training Tutorial.

NOTE: If you plot UMAP at this stage, do not use “Group by: RNA_nn_res.0.5” argument

Find Neighbors

Input is Y: Seurat UMAP on data X: Seurat RDS (the output of the Run UMAP step “Seurat UMAP on data X: Seurat RDS”)

“Reduction”: PCA “Dimensions”: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15 “Assay”: RNA –> Z: Seurat FindNeighbours on data Y: Seurat RDS

Find Clusters

In “Advanced Options “ “Resolution”: 0.5

Z:Seurat FindNeighbours on data Y: Seurat RDS –> A: Seurat FindClusters on data Z: Seurat RDS

Plot UMAP

Group by: RNA_nn_res.0.5

A: Seurat FindClusters on data Z: Seurat RDS –> B: Seurat DimPlot on data A: png plot

Other Resources