Transforms

Transforms add a same-shape view of your data as a new layer; they never touch X or drop anything else, so you can always get back to what you started from.

Relative abundance

bt.pp.relative scales every sample (row) so its features sum to one, and stores the result in layers["relative"]:

import biotapy as bt

tdata = bt.datasets.toy()
out = bt.pp.relative(tdata)
out.layers["relative"][0].sum()  # 1.0

X still holds the original counts; out.uns["biotapy"]["provenance"] records that pp.relative ran.

All-zero samples

A sample with no reads has nothing to divide by. Where phyloseq’s transform_sample_counts divides by zero and returns NaN, biotapy leaves an all-zero sample as all zeros, so downstream steps do not have to special-case NaN.