Identifying Differentially Expressed Genes in Gene Expression Data

Jul 1, 2025, 8:10 PM

data_set_ALL_AML_independent.csv

Missing file

dentifying differentially expressed genes.

The dataset has been successfully loaded. It contains gene expression data with columns for gene descriptions, accession numbers, and expression values for different samples.
To identify differentially expressed genes, we can perform statistical tests to compare gene expression levels between different conditions or groups. Typically, this involves:
  1. Normalizing the data: Ensuring that the data is comparable across samples.
  2. Statistical testing: Using tests like t-tests or ANOVA to find genes that are significantly differentially expressed.
  3. Multiple testing correction: Adjusting for the fact that many tests are being performed simultaneously.
Would you like to proceed with a specific analysis or visualization? For example, we can start by normalizing the data or performing a specific statistical test.

identifying differentially expressed genes.

The error occurred because the multipletests function from the statsmodels library was not imported. This function is used to adjust p-values for multiple testing. To resolve this issue, we need to import the multipletests function from the statsmodels.stats.multitest module before using it in the code.
It seems that no genes were identified as differentially expressed with an adjusted p-value below the threshold of 0.05. This could be due to the data characteristics or the threshold being too strict.
Here are a few steps we can take next:
  1. Review the data: Ensure that the data is correctly preprocessed and normalized.
  2. Adjust the threshold: Consider using a less strict threshold for adjusted p-values.
  3. Visualize the data: Plot the distribution of p-values or expression levels to better understand the data.
Would you like to explore any of these options or have any other questions?

give me a table of the differentially expressed genes between AML and ALL

It seems that no differentially expressed genes were identified with the current analysis parameters. The table is empty because no genes met the criteria of having an adjusted p-value below 0.05.
To proceed, we can:
  1. Adjust the significance threshold: Consider using a higher threshold for adjusted p-values.
  2. Visualize the p-value distribution: This can help in understanding the spread of p-values and deciding on a threshold.
  3. Check data preprocessing: Ensure that the data is correctly normalized and preprocessed.
Would you like to adjust the threshold or explore the data further?

Adjust the significance threshold:

Even with a more lenient threshold of 0.1, no genes were identified as differentially expressed.
This could indicate that the differences between the groups are not statistically significant under the current analysis conditions, or there might be other factors affecting the results.
Would you like to explore other options, such as visualizing the data or checking the preprocessing steps?

how many gene can you Identify in the table

The table of differentially expressed genes is currently empty, indicating that no genes were identified as significantly differentially expressed under the given conditions.
If you would like to explore further, we can:
  1. Visualize the data: Plot the distribution of p-values or expression levels.
  2. Review the preprocessing steps: Ensure data normalization and transformation are correctly applied.
  3. Explore other statistical methods: Consider alternative approaches for identifying differentially expressed genes.
Would you like to proceed with any of these options?

how many genes can you identify in my original excel file

In the original dataset, there are 6,627 unique genes identified based on the 'Gene Description' column.
If you have any more questions or need further analysis, feel free to ask!

מה אתה יכול להגיד לי על בדאטה- סט הזה?

הדאטה-סט הזה מכיל מידע על ביטוי גנים במקרים של לוקמיה מסוג AML ו-ALL.

מאפיינים עיקריים:

  • Gene Description: תיאור הגן.
  • Gene Accession Number: מספר הגישה של הגן.
  • מדידות ביטוי: עמודות המייצגות את רמות הביטוי של הגנים במדגמים שונים.

מטרות אפשריות:

  • זיהוי גנים מבוטאים באופן שונה: מציאת גנים שמראים הבדלים משמעותיים בביטוי בין קבוצות שונות (כגון AML ו-ALL).
  • אנליזות נוספות: ניתן לבצע אנליזות נוספות כמו ניתוח אשכולות, ניתוח רכיבים עיקריים (PCA) ועוד.
אם יש לך שאלות נוספות או שאתה מעוניין לבצע אנליזה מסוימת, אני כאן לעזור!

Can you visualize the gene expression data?

Can you perform a PCA on this dataset?

The error occurred because the dataset contains non-numeric values, specifically the letter 'A', which cannot be converted to a float. This is likely due to the presence of categorical data in the expression columns, which should be numeric for PCA analysis.
To resolve this issue, we need to ensure that all columns used for PCA contain only numeric data. We can either:
  1. Remove or replace non-numeric values: Identify and handle non-numeric entries appropriately.
  2. Filter the dataset: Use only the columns that contain numeric data for PCA.
Would you like to proceed with cleaning the data or focus on a specific subset of the dataset for PCA?
The error occurred because the dataset contains non-numeric values, specifically the letter 'A', which cannot be converted to a float. This is likely due to the presence of categorical data in the expression columns, which should be numeric for PCA analysis.
To resolve this issue, we need to ensure that all columns used for PCA contain only numeric data. We can either:
  1. Remove or replace non-numeric values: Identify and handle non-numeric entries appropriately.
  2. Filter the dataset: Use only the columns that contain numeric data for PCA.
Would you like to proceed with cleaning the data or focus on a specific subset of the dataset for PCA?

Visualize correlations between gene expression and phenotypes

It seems that the file data_set_ALL_AML_independent.csv is not found in the current directory. This is causing a FileNotFoundError.
To proceed, please ensure that the file is correctly uploaded to the environment or provide the correct path to the file. Once the file is available, I can help you visualize the correlations between gene expression and phenotypes.

data_set_ALL_AML_train.csv

Missing file

Visualize correlations between gene expression and phenotypes

The dataset has been successfully loaded. It contains gene expression data along with some categorical columns labeled as call. To visualize the correlations between gene expression and phenotypes, we need to ensure that we are using only numeric data.
Let's proceed to calculate and visualize the correlations between the numeric gene expression data and any available phenotype data. Since the dataset does not explicitly contain phenotype columns, we will assume the first few columns are related to phenotypes for this analysis.
Get started with Vizly