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W: Average mass spectrum / mask (single-dataset)

Brief description

Generate average mass spectrum per mask. Masks must be provided in the extra's sub-directory.

Parameters

Mask tag (type - array)

Tag or name of the mask to be used as the input mask.

Normalization tag (type - string)

Tag to use for normalization.

Help

Rather than applying a normalization to the entire dataset, we apply it as needed to each task at hand.
You can compare the effect normalization has on specific task by repeating it with different normalization.
In some cases, its advised to use 'multi-dataset' normalization, in particular when doing comparisons.

Normalization name (type - string)

Name of the normalization.

Dependencies (other tasks that this task might depend on)

Depends on Required/Optional
P: Normalization (single-dataset) optional
P: Normalization (multi-dataset) optional
P: Normalization (merged-project) optional

Dependents (tasks that might depend on this task)

Dependants Required/Optional
P: Mass calibration (single-dataset) required
W: M/z feature detection (single-dataset) required
W: M/z feature detection (multi-dataset) required
W: Compare spectra (interactive, multi-dataset) required
W: Compare spectra (one-vs-one; single-dataset) required
W: Compare spectra (one-vs-one; multi-dataset) required
W: Annotate mass spectra (single-dataset) required
W: Annotate mass spectrum (multi-dataset) required
W: Annotate average mass spectrum (merged-project) required

Attributes

Attribute Value Description
Multiple allowed True Allow multiple instances of this task in a workflow.
Task can fail True Task is optional and can fail without causing the entire workflow to fail.
Step can fail True Sub-tasks of this task can fail without causing the entire task (and workflow) to fail.
Requires ion mobility False Task requires ion mobility data.
Task can fail (with ion mobility) False This task uses ion mobility data but it is allowed to fail, without causing the entire workflow to fail.
Allowed in reference dataset True Task is to be performed on a 'reference' dataset. This will allow for multiple analyses to be performed on the same dataset, without cluttering or duplicating certain tasks (unused at the moment).