rna_seq_qc

This repository contains a script that allows for depth quality control by rarefaction in RNA sequencing (RNA-seq) data analysis. It includes functions to compute unique counts in parallel and track progress efficiently.

Functions

compute_sample_sizes_and_probabilities

This function processes the input DataFrame (data) to eliminate very low count values, determine the maximum count values, calculate the desired sample sizes, and compute the probability of gene appearance.

Parameters:

• data (pd.DataFrame): The input DataFrame with gene counts.

Returns:

• sample_sizes (pd.DataFrame): A DataFrame with the desired values per sample at each percentage.
• df_prob (pd.DataFrame): A DataFrame with the probability of gene appearance per sample.

compute_unique_counts

Calculates the number of unique indices that appear in a sample given the sample size and the probabilities associated with the indices.

Parameters:

• sample_size (int): The size of the sample to be drawn.
• prob_series (pd.Series): A series containing the probabilities associated with each index.
• index (tuple): The index of the task (row, column).

Returns:

• tuple: The index of the task and the number of unique indices in the sample.

parallel_compute

Executes parallel computations to count unique indices in samples and show progress using tqdm.

Parameters:

• df_prob (pd.DataFrame): DataFrame containing the probabilities for each index.
• sample_sizes (pd.DataFrame): DataFrame containing the sample sizes for each column.

Returns:

• pd.DataFrame: DataFrame containing the count of unique indices for each sample size.

Example Usage:

import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Compute sample sizes and probabilities
sample_sizes, df_prob = compute_sample_sizes_and_probabilities(data)

# Compute the rarefaction table
df_rarefied = parallel_compute(df_prob, sample_sizes)