Allow scanpy.tl.pca to accept a layer argument

• Additional function parameters / changed functionality / changed defaults?
• New analysis tool: A simple analysis tool you have been using and are missing in sc.tools?
• New plotting function: A kind of plot you would like to seein sc.pl?
• External tools: Do you know an existing package that should go into sc.external.*?
• Other?

...

I'm adapting ScanPy for my compositional data analysis (CoDA) methodologies in the same vein as https://github.com/scverse/scanpy/issues/2475 . In this, I would like to perform Aitchison PCA but I'd also like to keep my Anndata object in counts form instead of creating another one.

For example:

import numpy as np
import pandas as pd
from typing import Union

def clr(x:Union[np.ndarray, pd.Series], multiplicative_replacement:Union[None,str,float,int]="auto") -> Union[np.ndarray, pd.Series]:
    """
    http://scikit-bio.org/docs/latest/generated/skbio.stats.composition.clr.html#skbio.stats.composition.clr
    """
    assert np.all(x >= 0)
    if multiplicative_replacement == "auto":
        if np.any(x == 0):
            multiplicative_replacement = 1/(len(x)**2)
    if multiplicative_replacement is None:
        multiplicative_replacement = 0
    x = x.copy() + multiplicative_replacement
    x = x/x.sum()
    log_x = np.log(x)
    geometric_mean = log_x.mean()
    return log_x - geometric_mean

# Add CLR to layers
adata.layers["clr"] = adata.to_df().apply(clr, axis=1) # Not the fastest way but just to show example

sc.tl.pca(adata, svd_solver='arpack', layer="clr") # -> Return addata object where PCA is performed on CLR

Start at the scanpy.tl.pca entry point and trace how the input matrix is selected from AnnData. Add support for choosing a named layer so PCA runs on that data while existing behavior remains unchanged; verify the requested layer-based example and relevant PCA behavior.