The Gene section visualizes the correlation between immune checkpoint gene expression and miRNAs over diverse cancer types from a gene perspective.
In this section, you are supposed to receive a heatmap and its corresponding table with statistical values. Please follow these steps:
Fill in the name of an immune checkpoint gene.
Choose whether to select 'Purity Adjustment' or not (the default option is selected).
Click the 'Submit' button to submit your data.
Note: Since most immune cell types negatively correlate with tumor purity, tumor purity is considered a main confounding factor in the following analysis. Therefore, we recommend selecting the 'Purity Adjustment' option below, for this option will utilize partial Spearman's correlation to perform a more accurate association analysis.
Association heatmap of immune checkpoint gene and miRNAs
The heatmap illustrates the correlation between a user-selected immune checkpoint gene and miRNAs of 32 cancer types. Each cell's color in the heatmap represents the correlation between variables (red for positive, blue for negative, and grey for non-correlated). Additional information includes the p-value, correlation coefficient, number of validated miRNA interactions, and the number of tools that predicted the miRNA interactions.
(Note: For details on miRNA-validated and predicted tools, please refer to Help.)
On the top of the plot is the cancer type. You can hover the mouse on a specific cancer type to view its full name and corresponding tissue. On the left of the heatmap is the 30 microRNAs that are correlated with the most cancer types.
Hover on a heatmap cell to view detailed information.
Click on a distinct heatmap cell, and the corresponding correlation plots will pop up.
(Note: If the data is not linear, there might be a disparity between the correlation coefficient (Rho) and the slope of the line.)
The table provides the statistical values of user-selected immune checkpoint genes, miRNA, and immune checkpoint pathways across 32 cancer types.
The information below includes the p-value, correlation coefficient, number of validated miRNA interactions, and the number of tools that predicted the miRNA interactions.
For details on miRNA-validated and predicted tools, please refer to Help.
(Note: The miRNA listed in this table must be associated with more than two cancer types.)
Click on a specific row, and the corresponding correlation plots will pop up.
(Note: If the data is not linear, there might be a disparity between the correlation coefficient (Rho) and the slope of the line.)
The miRNA section visualizes the correlation between miRNA and immune checkpoint genes over diverse cancer types from a miRNA perspective.
In this section, you are supposed to receive an immune checkpoint response barplot, a heatmap, and a corresponding table with statistical values. Please follow the steps below:
Fill in a miRNA ID.
Choose whether to select 'Purity Adjustment' or not (the default option is selected).
Click the 'Submit' button to submit your data.
Note: Since most immune cell types negatively correlate with tumor purity, tumor purity is considered a main confounding factor in the following analysis. Therefore, we recommend selecting the 'Purity Adjustment' option below, for this option will utilize partial Spearman's correlation to perform a more accurate association analysis.
The bar plot illustrates the immune checkpoint response of the user-selected miRNA across 32 cancer types, calculated by TIDE (Tumor Immune Dysfunction and Exclusion).
Hover on a sepcific bar to view the correlation coefficient and -log10(p-value).
Click on a sepcific bar, and the corresponding correlation plots will pop up.
(Note: If the data is not linear, there might be a disparity between the correlation coefficient (Rho) and the slope of the line.)
Association heatmap of miRNA and immune checkpoint genes
The heatmap illustrates the correlation between a user-selected miRNA and immune checkpoint genes across 32 cancer types. Each cell's color in the heatmap represents the correlation between variables (red for positive, blue for negative, and grey for non-correlated). Additional information includes the p-value, correlation coefficient, number of validated miRNA interactions, and the number of tools that predicted the miRNA interactions.
(Note: For details on miRNA-validated and predicted tools, please refer to Help.)
On the top of the plot is the cancer type. You can hover the mouse on a specific cancer type to view its full name and corresponding tissue. On the left of the heatmap is the immune checkpoint genes; you can view their corresponding pathways by hovering on the side color bar on the right side.
Hover on a heatmap cell to view detailed information.
Click on a distinct heatmap cell, and the corresponding correlation plots will pop up.
(Note: If the data is not linear, there might be a disparity between the correlation coefficient (Rho) and the slope of the line.)
The table provides the statistical values of user-selected miRNA, immune checkpoint genes, and immune checkpoint pathways across 32 cancer types.
The information below includes the p-value, correlation coefficient, number of validated miRNA interactions, and the number of tools that predicted the miRNA interactions.
For details on miRNA-validated and predicted tools, please refer to Help.
(Note: The immune checkpoint genes listed in this table must be associated with more than one cancer type.)
Click on a specific row, and the corresponding correlation plots will pop up.
(Note: If the data is not linear, there might be a disparity between the correlation coefficient (Rho) and the slope of the line.)
The Pathway section visualizes the correlation between the immune checkpoint pathway, miRNAs, and immune checkpoint genes over diverse cancer types from an immune checkpoint pathway perspective.
In this section, you are supposed to receive a Sankey diagram and a corresponding table with statistical values. Please follow the steps below:
Select an immune checkpoint pathway from the drop-down list.
Choose whether to select 'Purity Adjustment' or not (the default option is selected).
Click the 'Submit' button to submit your data.
Note: Since most immune cell types negatively correlate with tumor purity, tumor purity is considered a main confounding factor in the following analysis. Therefore, we recommend selecting the 'Purity Adjustment' option below, for this option will utilize partial Spearman's correlation to perform a more accurate association analysis.
The Sankey diagram illustrates how immune checkpoint pathways are associated with genes, miRNAs, and 32 cancer types. The more a node in the diagram is connected, the more important it is in influencing the immune checkpoint analysis.
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Association result table
The table provides the statistical values of miRNA, immune checkpoint genes, and immune checkpoint pathways across diverse cancer types, including p-value, q-value, and correlation coefficients.
Click on a specific row, and the corresponding correlation plots will pop up.
(Note: If the data is not linear, there might be a disparity between the correlation coefficient (Rho) and the slope of the line.)
The Cancer section visualizes the correlation between the immune checkpoint pathway, miRNAs, and immune checkpoint genes over diverse cancer types from an immune checkpoint pathway perspective.
In this section, you are supposed to receive a Sankey diagram and a corresponding table with statistical values. Please follow the steps below:
Select a tissue type from the drop-down list.
Select a related dataset of this tissue type from the drop-down list.
Choose whether to select 'Purity Adjustment' or not (the default option is selected).
Click the 'Submit' button to submit your data.
Note: Since most immune cell types negatively correlate with tumor purity, tumor purity is considered a main confounding factor in the following analysis. Therefore, we recommend selecting the 'Purity Adjustment' option below, for this option will utilize partial Spearman's correlation to perform a more accurate association analysis.
The Sankey diagram illustrates how each cancer type is associated with genes, miRNAs, and immune checkpoint pathways. The more a node in the diagram is connected, the more important it is in influencing the immune checkpoint analysis.
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Association result table
The table provides the statistical values of miRNA, immune checkpoint genes, and immune checkpoint pathways across diverse cancer types, including p-value, q-value, and correlation coefficients.
Click on a specific row, and the corresponding correlation plots will pop up.
(Note: If the data is not linear, there might be a disparity between the correlation coefficient (Rho) and the slope of the line.)