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Filter out discriminatory taxa in utils.py, add in README that QADABRA is WIP software, add install from source instructions in README #53

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Jan 21, 2024
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Filter out discriminatory taxa in utils.py, add in README that QADABRA is WIP software, add install from source instructions in README #53

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777e975
filter out discriminatory taxa
yangchen2 Nov 27, 2023
889f414
remove previous code block
yangchen2 Nov 27, 2023
a2ae889
Merge pull request #3 from biocore/main
yangchen2 Jan 19, 2024
6719013
update README to say QADABRA is WIP software
yangchen2 Jan 19, 2024
33b8985
Merge branch 'master' of https://github.com/yangchen2/qadabra
yangchen2 Jan 19, 2024
71de05c
f-string format for discriminatory features warning, log number of di…
yangchen2 Jan 19, 2024
a78121a
add install from source instructions
yangchen2 Jan 20, 2024
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4 changes: 3 additions & 1 deletion README.md
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Expand Up @@ -10,6 +10,8 @@ Importantly, Qadabra focuses on both FDR corrected p-values *and* [feature ranks

![Schematic](images/Qadabra_schematic.svg)

Please note this software is currently a work in progress. Your patience is appreciated as we continue to develop and enhance its features. Please leave an issue on GitHub should you run into any errors.

## Installation
```
pip install qadabra
Expand Down Expand Up @@ -97,7 +99,7 @@ This will create a zipped directory containing the report.
Unzip this file and open the `report.html` file to view the report containing results and visualizations in your browser.

## Tutorial
See the [tutorial](tutorial.md) page for a walkthroughon using Qadabra workflow with a microbiome dataset.
See the [tutorial](tutorial.md) page for a walkthrough on using Qadabra workflow with a microbiome dataset.

## FAQs
Coming soon: An [FAQs](FAQs.md) page of commonly asked question on the statistics and code pertaining to Qadabra.
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21 changes: 14 additions & 7 deletions qadabra/utils.py
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Expand Up @@ -4,7 +4,7 @@

import biom
import pandas as pd

import warnings

def _validate_input(
logger: logging.Logger,
Expand Down Expand Up @@ -51,11 +51,18 @@ def _validate_input(
joint_df = tbl_df.join(md)
gb = joint_df.groupby(factor_name).sum(numeric_only=True)
feat_presence = gb.apply(lambda x: x.all())
if not feat_presence.all():
raise ValueError(
"Some taxa in the table perfectly discriminate factor groups. "
"Please filter out these taxa before running Qadabra."
)

discriminating_feats = feat_presence[~feat_presence].index.tolist()

if len(discriminating_feats) > 0:
warning_msg = "Some features in the table perfectly discriminate factor groups:\n" + '\n'.join(discriminating_feats) + ".\nAutomatically filtering out these features before running Qadabra..."
print("Number of discriminating features: " + str(len(discriminating_feats)))
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warnings.warn(warning_msg, category=Warning)

# Filtering out the discriminating features from the BIOM table
tbl = tbl.filter(lambda value, id_, metadata: id_ not in discriminating_feats, axis='observation', inplace=False)
logger.info(f"Table shape after filtering: {tbl.shape}")


if tree:
from bp import parse_newick, to_skbio_treenode
Expand All @@ -69,4 +76,4 @@ def _validate_input(
raise ValueError("Tree tips are not a subset of table features!")
else:
logger.info("Reading phylogenetic tree...")
logger.info("(Optional tree file not provided. Skipping tree validation.)")
logger.info("(Optional tree file not provided. Skipping tree validation.)")
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