Train classifier
To train a classifier with a list of biomarkers with your own dataset:
Select any non-normalized single cell data node, choose the Train classifier task in the Classification section.
Similar to previously described steps, first time users will be asked to create the Marker file, users may add them as library files. Marker files should be a .txt file with the marker information in correct format. The same example in the Garnett tutorial of a simple valid Marker file is provided here. Here is an example:
Note:
Cell type name line starts with ">", followed by the name of the cell type, cell type name can NOT include hyphen (-).
Definition line starts with a keyword e.g. "expressed", "not expressed" e.g. it is case sensitive. It followed by a ":" and space. Each gene name is followed by a comma and space, gene name is case sensitive. The line cannot be ended with comma or space.
For more details on how to construct a Marker file, please refer to Garnett tutorial[3]. Choose a Local file option and browse to the file location, click the Finish button to start running the task as default.
Train classifier task report
Once the task has finished, click the Classifier data node and choose the Task report in the Task results section.
There are two parts in the task report: the marker evaluation plot and the classification gene table. The marker evaluation plot provides some key information about whether the chosen markers are optimal. Ambiguity scores are calculated for each of the markers which indicates how many cells receive ambiguous labels when this marker is included. The classification gene table may give a hint to which genes are chosen as the relevant genes for distinguishing between different cell types.
Other adjustable parameters in this task include:
Number of Unknown: this tells Garnett how many outgroup cells it should compare against; the default is 500. For a dataset with fewer cells, the number should be smaller.
References
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