Project Utility

This project is a collection of essential functions and utilities that I frequently use in my data analysis workflows.

Why This Project Exists

As a data analyst, I found myself repeatedly writing the same helper functions across different projects. ProjectUtility centralizes these common utilities into a single, well-documented package that can be easily imported into any analysis.

Documentation

Install ProjectUtility in Development mode

If you are new to using nbdev here are some useful pointers to get you started.

# make sure ProjectUtility package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to ProjectUtility
$ nbdev_prepare

Usage

Install latest from the GitHub repository:

$ pip install git+https://github.com/mtinti/ProjectUtility.git

or from conda

$ conda install -c mtinti ProjectUtility

or from pypi

$ pip install ProjectUtility

Data Science Q&A

🔍 Question

Can you visualize the pattern of missing values in my dataset?

from ProjectUtility import mis_val_utility
import pandas as pd
df = pd.read_csv('../tests/missing_values/test_df.csv.gz',index_col=[0])
mv_analyzer = mis_val_utility.MissingValuesAnalyzer(df)
fig, axes, summary = mv_analyzer.plot_missing_dashboard(figsize=(10, 12))

/Users/MTinti/miniconda3/envs/work3/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).
  from pandas.core import (

🔍 Question

Can you reduce the dimensionality of my dataset to visualize patterns and relationships?

from ProjectUtility import dim_red_utility
import matplotlib.pyplot as plt
from ProjectUtility.core import convert_palette_to_hex, create_group_color_mapping


sample_groups, color_dictionary = create_group_color_mapping(df.columns, 
                                                             group_size=3, 
                                                             return_color_to_group=True)

color_dictionary = {'#FF5733': 'B1', '#33FF57': 'C3', '#3357FF': 'WT'}

fig, axes, results_dict = dim_red_utility.create_dim_reduction_dashboard(
    in_df=df.dropna(),
    sample_palette=sample_groups,
    feature_palette={},
    top=500,
    color_dictionary=color_dictionary,
    title="Dim Red Analysis",
    figsize=(12, 10)  
)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])

Explained variance ratio: [0.81546122 0.12599201 0.02508285 0.01313488 0.00740248]

🔍 Question

Can you assess the reproducibility of my dataset?

from ProjectUtility import correlation_utilities
import numpy as np
cv_sample_groups = [
    'B1', 'B1', 'B1', 
    'C3', 'C3', 'C3',
    'WT', 'WT', 'WT']
analyzer = correlation_utilities.ReplicateAnalyzer(
    np.log10(df.dropna()), cv_sample_groups)
# Calculate coefficient of variation
cv_results = analyzer.calculate_coefficient_of_variation()
print("Coefficient of Variation Results:")
print(cv_results)

# Visualize the results
fig = analyzer.plot_coefficient_of_variation(
    title="CV Comparison Between Sample Groups",
    figsize=(8, 5)
)

Coefficient of Variation Results:
{'WT': 1.5810533161044804, 'B1': 1.17165193540931, 'C3': 1.191373053916986}

Can you do the same row wise?

cv_distribution = analyzer.calculate_cv_distribution(exclude_zeros=True)
# Create the CV boxplot
print("\nGenerating CV distribution boxplot...")
fig2 = analyzer.plot_cv_boxplot(
    min_y=0,  # Minimum y-axis value
    max_y=5,  # Maximum y-axis value
    figsize=(6, 6),
    color_palette="viridis",
    display_median=True,
    reference_line=3,  
    title="Distribution of Coefficient of Variation Across Sample Groups"
)

# Show plots
plt.tight_layout()
plt.show()

Generating CV distribution boxplot...

🔍 Question

Can you show me an interactive volcan plot?

from ProjectUtility import diff_expr
column_mapping = {
    'log2fc': 'logFC',            # logFC column from your data
    'fdr': 'FDR',                # FDR column from your data
    'avg_intensity': 'log_AveExpr', # log_AveExpr column from your data
    'id': 'Gene_id',            # Gene_acc column from your data
    'description': 'Desc'         # Desc column from your data
}

file_path = '../tests/volcano_plots/for_web_limma_WT-C3.csv.zip'
# Create the PlotData instance
plot_data = diff_expr.PlotData(file_path, column_mapping,
                               highlight_ids=['Blasticidin','Puromycin'])
# Quick access to all plotting data
_ = plot_data.get_data_for_plotting()

fig = diff_expr.create_volcano_ma_plots(
    plot_data,
    plot_title="Differential Expression Analysis: Sample vs Control"
)

fig.show('iframe')

you can play with the interactive output here: https://mtinti.github.io/ProjectUtility/

🔍 Question

Can you analyse the identification rate of my proteomics sample?

import polars as pl
from ProjectUtility import proteomics 
fname = '../tests/proteomics/dia_nn_report.tsv.gz'
tmp = pl.read_csv(fname,separator='\t')
count_data= proteomics.prepare_data_DiaNN(tmp)
count_data=count_data.to_pandas()
_ = proteomics.plot_proteomics_run(count_data,
                               figsize=(8,4))

Documentation

Documentation can be found hosted on this GitHub repository’s pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.