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W: Extract ion centroids (single/multi-dataset)

Brief description

Extract ion images. These images can be used in visualisation, unsupervised or supervised training.

Parameters

Tag (identifier) (type - string)

Tag to use for the object.

m/zs (type - array)

M/z values.

Filename (type - string)

Path to peaklist file.

Filename (reference) (type - string)

Path to peaklist file.

PPM tolerance (type - number)

Integration window around each m/z during image extracting. This is an approximate value and
might be slightly smaller or larger, depending on how the mass bins are aligned.

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
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: Compute quality control metrics (multi-dataset) required
W: Group statistics of ion centroids (multi-dataset) required
W: Compare mosaic images (single-dataset) required
W: Compare mosaic images (multi-dataset) required
W: Unsupervised training (single-dataset) required
W: Unsupervised training (merged-project) required
W: Supervised training (single-dataset) required
W: Supervised training (merged-project) required

Attributes

Attribute Value Description
Multiple allowed True Allow multiple instances of this task in a workflow.
Task can fail False 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).