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W: Compute quality control metrics (multi-dataset)

Brief description

Compute spatial quality control metrics such as ppm error, resolving power, TIC fluctuation, etc.

Parameters

Tag (identifier) (type - string)

Tag to use to help identify object.

m/zs (type - array)

M/z values.

Filename (type - string)

Path to peaklist file.

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
W: Extract ion centroids (single/multi-dataset) required
W: Extract ion centroids (subset-project) required
P: Normalization (single-dataset) optional
P: Normalization (multi-dataset) optional
P: Normalization (merged-project) optional
W: M/z feature detection (single-dataset) optional
W: M/z feature detection (multi-dataset) optional
W: Ion mobility feature detection (single-dataset) optional

Dependents (tasks that might depend on this task)

Dependants Required/Optional
W: Visualise quality control metrics (subset-project) required
W: Visualise quality control metrics (multi-dataset) 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).