Key Information to Include

Oct 13, 2025 by recoveryshake.com 90 views

Introduction

When writing a manuscript, it is essential to present data in a clear and concise manner. In the context of bulk prediction analysis, creating a helper table with key information can significantly enhance the readability and understanding of the results. In this article, we will explore the importance of creating a bulk analysis helper table, the necessary information to include, and the steps to generate this table.

Why Create a Bulk Analysis Helper Table?

A bulk analysis helper table serves as a valuable resource for researchers, providing a concise overview of the key findings and insights from the analysis. By tabulating essential information, such as the number of samples identified with paired bulk/single-cell, the number of samples per project analyzed, and the number of gene sets used per project in the over-representation analysis, researchers can quickly grasp the scope and significance of the results.

Key Information to Include

To create an effective bulk analysis helper table, we need to include the following pieces of information:

Number of samples identified with paired bulk/single-cell: This information provides insight into the number of samples that have been analyzed using both bulk and single-cell approaches.
Number of samples per project which were analyzed: This includes the total number of samples analyzed per project, as well as the number of samples removed due to low quality.
Number of gene sets used per project in the over-representation analysis: This information highlights the number of gene sets used in the over-representation analysis per project.

Table Structure

To present this information in a clear and organized manner, we can create a wide table with one row per project and the following columns:

n_samples_total: This column will display the total number of samples analyzed per project.
n_samples_low_quality: This column will show the number of samples removed due to low quality per project.
n_samples_used: This column will calculate the number of samples used per project by subtracting the number of low-quality samples from the total number of samples.
num_genesets: This column will display the number of gene sets used per project in the over-representation analysis.

Exporting the Table

To make this table easily accessible, we can export it to a file named manuscript-numbers. This will allow researchers to quickly reference the key information from the bulk analysis.

Generating the Table

To generate this table, we can include the code in the script that creates the bulk prediction figures (issues #187 and #195). This will ensure that the table is created in conjunction with the figures, providing a comprehensive overview of the results.

Example Code

Here is an example code snippet that demonstrates how to generate the bulk analysis helper table:

import pandas as pd

# Define the data
data = {
    'project': ['Project 1', 'Project 2', 'Project 3'],
    'n_samples_total': [100, 200, 300],
    'n_samples_low_quality': [10, 20, 30],
    'num_genesets': [5, 10, 15]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Calculate the number of samples used per project
df['n_samples_used'] = df['n_samples_total'] - df['n_samples_low_quality']

# Export the table to a file
df.to_csv('manuscript-numbers.csv', index=False)

This code snippet creates a DataFrame with the necessary information and calculates the number of samples used per project. It then exports the table to a file named manuscript-numbers.csv.

Conclusion

Q: What is the purpose of a bulk analysis helper table?

A: A bulk analysis helper table serves as a valuable resource for researchers, providing a concise overview of the key findings and insights from the analysis. It helps to present data in a clear and organized manner, making it easier for researchers to quickly grasp the scope and significance of the results.

Q: What information should be included in a bulk analysis helper table?

A: A bulk analysis helper table should include the following pieces of information:

Number of samples identified with paired bulk/single-cell: This information provides insight into the number of samples that have been analyzed using both bulk and single-cell approaches.
Number of samples per project which were analyzed: This includes the total number of samples analyzed per project, as well as the number of samples removed due to low quality.
Number of gene sets used per project in the over-representation analysis: This information highlights the number of gene sets used in the over-representation analysis per project.

Q: How should the table be structured?

A: To present this information in a clear and organized manner, we can create a wide table with one row per project and the following columns:

n_samples_total: This column will display the total number of samples analyzed per project.
n_samples_low_quality: This column will show the number of samples removed due to low quality per project.
n_samples_used: This column will calculate the number of samples used per project by subtracting the number of low-quality samples from the total number of samples.
num_genesets: This column will display the number of gene sets used per project in the over-representation analysis.

Q: How can I export the table to a file?

A: To make this table easily accessible, we can export it to a file named manuscript-numbers. This will allow researchers to quickly reference the key information from the bulk analysis.

Q: Can I include the code to generate the table in the script that creates the bulk prediction figures?

A: Yes, we can include the code to generate the table in the script that creates the bulk prediction figures (issues #187 and #195). This will ensure that the table is created in conjunction with the figures, providing a comprehensive overview of the results.

Q: What is the benefit of creating a bulk analysis helper table?

A: Creating a bulk analysis helper table provides several benefits, including:

Improved readability: The table presents data in a clear and organized manner, making it easier for researchers to quickly grasp the scope and significance of the results.
Enhanced understanding: The table provides a concise overview of the key findings and insights from the analysis, helping researchers to better understand the results.
Increased efficiency: The table allows researchers to quickly reference the key information from the bulk analysis, saving time and effort.

Q: How can I generate the bulk analysis helper table?

A: To generate the bulk analysis helper table, you can follow these steps:

Define the data: Create a DataFrame with the necessary information, including the number of samples identified with paired bulk/single-cell, the number of samples per project analyzed, and the number of gene sets used per project in the over-representation analysis.
Calculate the number of samples used per project: Subtract the number of low-quality samples from the total number of samples to calculate the number of samples used per project.
Export the table to a file: Use the to_csv function to export the table to a file named manuscript-numbers.

Q: What is the example code for generating the bulk analysis helper table?

A: Here is an example code snippet that demonstrates how to generate the bulk analysis helper table:

import pandas as pd

# Define the data
data = {
    'project': ['Project 1', 'Project 2', 'Project 3'],
    'n_samples_total': [100, 200, 300],
    'n_samples_low_quality': [10, 20, 30],
    'num_genesets': [5, 10, 15]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Calculate the number of samples used per project
df['n_samples_used'] = df['n_samples_total'] - df['n_samples_low_quality']

# Export the table to a file
df.to_csv('manuscript-numbers.csv', index=False)

This code snippet creates a DataFrame with the necessary information, calculates the number of samples used per project, and exports the table to a file named manuscript-numbers.csv.