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Initial processing using RaceID (version 0.2.3+galaxy3)
Filtering 0


RaceID is a clustering algorithm for the identification of cell types from single-cell RNA-sequencing data. It was specifically designed for the detection of rare cells which correspond to outliers in conventional clustering methods.

This module performs filtering, normalisation, and batch effect removal in the same step.

Example Usage: Inspecting the Aggregated Expression for a Group of Genes

Our cells come from 5 different batches (I5,II5,III5,IV5,V5) and are labelled to reflect this (i.e. "I5_1", "I5_2", ..., "I5_129", "II5_1", ..., "V5_236" )

We wish to filter out the gene Lpca5 and Atk2 which we know in advance will saturate our analysis with unwanted expression.

We will also be interested in the cluster that contains significant expression for Apoa genes (Apoa1, Apoa1bp, Apoa2, Apoa4, Apoa5).

First, we must load in our count matrix in order to correct for batch effects, filter out unwanted genes, and compute our clusters and outliers.

  • Mode of AnalysisCluster
    • Count Matrix → [input tabular]
    • Filtering:
      • Use Defaults?No
      • Batch Regex → "^I5,^II5,^III5,^IV5,^V5"
      • FGenes → "Lpca5,Atk2"

A PDF report will be generated giving metrics about the library size and number of features as histograms, and additional metrics relating to cell-cycle correction will be produced if that option has been selected.