What is AUCell?

AUCell is a tool to identify cells that are actively expressing genes within a gene list [1]. For each input gene list, AUCell calculates a value for each cell by ranking all genes by their expression level in the cell and identifying what proportion of the genes from the gene list fall within the top 5% (default cutoff) of genes.  This method allows the AUCell value to represent the proportion of genes from the gene list that are expressed in the cell and their relative expression compared to other genes within the cell. Because this is a rank-based method and is calculated for each cell individually, AUCell can be run on raw or normalized data. As an AUCell value is the proportion of genes from the list that are within the top percentile of expressed genes, AUCell values can range from 0 to 1, but may have a more restricted range.  

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To produce a data node containing only the AUCell values, use Split matrix to split the AUCell result data node into separate data nodes for each of its data types. This can be helpful if you intend on performing downstream analysis on the AUCell values. To perform differential analysis, it is advisable to normalize the values by adding a small offset (e.g. 1E-9) and Logit transformation to the base Log2 using the Normalization task. This will make the values continuous and suitable for differential analysis with methods such as ANOVA/LIMMA-trend/LIMMA-voom, Non-parametric ANOVA or Welch's ANOVA. For differential analysis, please check the Low-value filter is set to None and the values are correctly recognized as Log2 transformed in the Advanced settings. 

If an AUCell result data node or other downstream data node containing AUCell Values is used as the input for AUCell, the additional AUCell values will be added as additional features of the AUCell values data type in the new AUCell result data node. 

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