Options for Feature-table merge and group

[ngilbert (Naomi)]

Hi all, I have a few questions concerning what some options in Feature-table group and merge specifically do.

Feature-table group:
What does it mean to use sum vs median ceiling vs mean ceiling? I am not clear on what it means by grouping based on “ceiling”. Are there advantages/disadvantages to using either one?

Feature-table merge:
What are the functions of the different overlap methods (error_on_overlapping_sample, error_on_overlapping_feature, sum). They each produce very different sequence counts. Do the overlapping methods filter out features or samples that show up multiple times?

Any detail on these will be appreciated!

Thanks,
N

[thermokarst]

Hi @ngilbert! Can you take a look at this post here:

Best way to merge or group runs/samples

Hi @Sarah_McGrath! To answer your questions about what the different parameter choices for merge and group mean, I think some examples might help illustrate the concepts: merge from biom import Table import numpy as np from qiime2 import Artifact from qiime2.plugins import feature_table t1 = Artifact.import_data('FeatureTable[Frequency]', Table(np.array([[0, 1, 3], [1, 1, 2]]), ['O1', 'O2'], ['S1', 'S2', 'S3'])) t2 = Artifact.i…

A similar question was just asked earlier today, so maybe these examples will help clear up any questions. Thanks!