Unraveling the Regulatory Landscape of Immune Checkpoint Genes
This module of ImmiR provides a comprehensive analytical platform for investigating the complex regulatory interactions between immune checkpoint genes and miRNAs across diverse cancer types. By integrating multi-dimensional datasets including gene expression profiles, miRNA target prediction algorithms, and tumor purity metrics, this section enables systematic identification of potential miRNA-mediated immune checkpoint gene regulatory mechanisms.

Steps:

1. Input Gene: Enter the immune checkpoint gene identifier of interest for analysis (e.g., "CD274" for PD-L1 or other immune checkpoint targets).
2. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed gene-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships.
   Click on the info icon to view more information regarding purity adjustment.
3. Submit: Execute the analysis to generate interactive heatmaps and comprehensive statistical summary tables.

Heatmap Overview:

The interactive heatmap displays correlation patterns between the selected immune checkpoint genes and associated miRNAs across 32 cancer types, providing comprehensive visualization of immune checkpoint-miRNA regulatory relationships within diverse tumor microenvironments.

Data Representation:

• Each cell represents a correlation between the selected immune checkpoint gene and associated miRNA within a specific cancer type, quantified using robust statistical methods to ensure methodological transparency across different tumor contexts.
• Color-coded cells indicate correlation direction, significance, and biological
  

  interpretation:

  • Red cells: Positive correlation suggesting the miRNA and immune cell infiltration covary positively, potentially indicating the miRNA promotes infiltration or activity of that immune cell type
  • Blue cells: Negative correlations suggesting inverse relationships where immune cell infiltration decreases as miRNA expression increases (or vice versa), potentially indicating the miRNA suppresses infiltration or activity of that immune cell type
  • Gray cells: Non-significant correlations
  • Color intensity: Reflects correlation strength, with darker shades indicating stronger associations
• Cancer types are displayed along the x-axis —hover over any type to view its full name and tissue origin
• Cancer types are displayed along the x-axis —hover over any type to view its full name and tissue origin

Interactive Elements:

1. Hover over any cell to reveal detailed statistics including correlation coefficient, p-value, number of validated miRNA interactions, and prediction tool consensus count.
2. Click on any cell to generate detailed correlation plots that visualize the relationship between the selected immune checkpoint gene expression and associated miRNA expression in that specific cancer type through scatter plots, allowing closer examination of the regulatory relationship and revealing the potential influence of tumor purity on observed correlations.
   Note: For non-linear relationships, correlation coefficient (Rho) may differ from regression line slope.

Example Interpretation:

1. The interactive heatmap displays correlation patterns between CD276 expression and associated miRNAs (C) across 32 cancer types (A), providing a comprehensive overview of gene-miRNA regulatory relationships within diverse tumor contexts.
2. The selected cell (B) demonstrates a statistically significant negative correlation between hsa-miR-625-5p and CD276 expression in esophageal carcinoma (ESCA), indicating potential regulatory interaction within this specific cancer type.
3. Interactive cell exploration reveals quantitative metrics including the Pearson correlation coefficient (-0.24), statistical significance (p-value = 0.00197), and experimental validation status (0, denoting absence of direct experimental validation for this regulatory interaction).
4. Cell selection triggers generation of detailed scatter plot visualizations illustrating the inverse relationship between hsa-miR-625-5p and CD276 expression levels in ESCA samples, enabling comprehensive assessment of the regulatory correlation and potential confounding factors.
   

Table Overview:

The Association Result Table provides a comprehensive summary of all statistically significant correlations between the selected gene and associated miRNAs across multiple cancer types, offering detailed quantitative metrics for systematic analysis and prioritization. Relevant pathway involvement and supporting evidence are included.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis
Immune Checkpoint Gene: Target immune checkpoint gene - key regulatory protein in immune response
Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)
P-value: Statistical significance of the correlation (lower values indicate higher confidence)
Cancer: Cancer type where the association was observed
Immune Checkpoint Pathway: Biological pathway context for the gene-miRNA interaction
Evidence: Classification of the miRNA-gene relationship (e.g., Predicted, Validated)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNAs, genes, cancer types, or significance thresholds
3. Row Selection: Click on any row to generate detailed correlation plots for the selected miRNA-gene pair:
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Example Interpretation:

1. The statistical table presents comprehensive correlation data between specific immune checkpoint gene CD276 and miRNAs across multiple cancer types, providing quantitative support for patterns observed in the heatmap visualization.
2. The highlighted row (highlighted section) demonstrates a negative correlation between hsa-miR-29b-3p expression and CD276 in Ovarian Cancer (OV).
3. Row selection generates corresponding scatter plots that visualize the direct relationship between hsa-miR-29b-3p expression levels and CD276 expression within the OV cohort, enabling detailed examination of the statistical association and potential biological significance.

Deciphering miRNA-Mediated Immune Checkpoint Regulation
This module of ImmiR provides a comprehensive analytical platform for investigating the complex regulatory interactions between specific miRNAs and immune checkpoint genes across diverse cancer types. By integrating multi-dimensional datasets including miRNA expression profiles, immune checkpoint gene expression data, and tumor purity metrics, this section enables systematic identification of potential miRNA-mediated immune checkpoint regulatory mechanisms with significant implications for cancer immunotherapy.

Steps:

1. Input miRNA: Enter the miRNA identifier of interest for analysis (e.g., "hsa-miR-155-5p" for miR-155, "hsa-let-7a-5p" for let-7a, or other miRNA targets).
2. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed miRNA-gene correlations, and adjustment facilitates identification of genuine biological regulatory relationships.
   Click on the info icon to view more information regarding purity adjustment.
3. Submit: Execute the analysis to generate interactive immune checkpoint response barplots, association heatmaps, and comprehensive statistical summary tables.

Heatmap Overview:

The interactive barplot presents immune checkpoint response correlations for the selected miRNA across 32 distinct cancer types, utilizing the TIDE (Tumor Immune Dysfunction and Exclusion) computational framework. This analytical visualization provides systematic assessment of miRNA expression associations with predicted immune checkpoint inhibitor efficacy across diverse tumor contexts. The predictive analytics are generated through the TIDE algorithm, which integrates multiple parameters related to tumor immune dysfunction, T cell exclusion mechanisms, and immune evasion pathways to quantify potential immunotherapeutic responsiveness.

Data Representation:

Y-axis: Bidirectional Correlation Coefficient Display

• Bar magnitude corresponds to correlation strength, with increased absolute values denoting enhanced statistical association
• Positive correlations (upward bars): Indicate direct associations between elevated miRNA expression and enhanced immune checkpoint response, suggesting miRNA-mediated promotion of anti-tumor immune activation
• Negative correlations (downward bars): Indicate inverse associations between miRNA expression and immune checkpoint response, suggesting potential miRNA-mediated immune suppression or evasion facilitation

X-axis: Cancer Type Classification

• Standardized cancer type nomenclature utilizing established abbreviations across the comprehensive 32-cancer dataset
• Statistical Significance Encoding: Red bars denote correlations achieving statistical significance thresholds, with bar length proportional to effect size magnitude

Methodological Considerations:

• Non-parametric correlation coefficients (Rho) may deviate from linear regression slopes in cases of non-linear associations, providing enhanced analytical granularity

Interactive Elements:

Data Interrogation Capabilities:

• Hover-activated Information Display: Cursor positioning over individual bars reveals comprehensive analytical metrics including:
• Quantitative correlation coefficients between miRNA expression and immune checkpoint response
• Statistical significance measures (-log10 transformed p-values)
• Complete cancer type nomenclature and anatomical tissue classification
• Contextual metadata for biological interpretation

Advanced Analytical Tools:

• Click-triggered Detailed Analysis: Bar selection initiates generation of comprehensive scatter plot visualizations for granular correlation examination
• Visualization Customization: Access to sophisticated display controls via integrated Help interface for personalized analytical presentation
• Data Export Functionality: Comprehensive visualization and underlying dataset export capabilities for downstream computational analysis

Example Interpretation:

Analytical Case Study: hsa-miR-152-3p Regulatory Assessment

1. Systematic Visualization Analysis: The barplot demonstrates correlation patterns between hsa-miR-152-3p expression profiles and corresponding TIDE algorithmic scores across multiple cancer histologies, elucidating tissue-specific immune checkpoint response modulation patterns.
2. Quantitative Data Interrogation: Interactive exploration of the PCPG
   (Pheochromocytoma and Paraganglioma) dataset (A) reveals:
   • Positive correlation coefficient: r = 0.2
   • Statistical significance: p = 0.00846
   • Biological interpretation: Elevated miR-152-3p expression correlates with enhanced predicted immunotherapeutic responsiveness in PCPG malignancies
3. Comprehensive Statistical Analysis: Selection of the PCPG bar (B) generates detailed bivariate scatter plot visualization, illustrating the quantitative relationship between hsa-miR-152-3p expression levels and corresponding TIDE predictive scores within PCPG patient cohorts. This analytical framework enables assessment of:
   • Association linearity and mathematical modeling appropriateness
   • Sample distribution characteristics and statistical assumptions
   • Identification of potential statistical outliers or distinct patient subpopulations
   • Confidence interval estimation and regression diagnostic parameters

This systematic analytical approach facilitates comprehensive evaluation of miRNA-mediated immune checkpoint response modulation across heterogeneous cancer contexts, enabling identification of potential therapeutic biomarkers and personalized immunotherapy stratification strategies for precision oncology applications.

Heatmap Overview:

The interactive heatmap visualizes correlation patterns between the selected miRNA and associated immune checkpoint genes across 32 cancer types, incorporating integrated pathway enrichment analysis. This analytical platform systematically displays quantitative relationships between miRNA expression and immune checkpoint gene targets, enabling identification of cancer-specific and pan-cancer regulatory mechanisms.

Data Representation:

• Main Heatmap:
  • Each cell represents a correlation between the selected miRNA and a specific immune checkpoint gene within a defined cancer type context, quantified using robust statistical methods to ensure analytical consistency across different gene-cancer combinations
  • Color-coded cells indicate correlation direction, significance, and biological
    

    interpretation:

    • Red cells: Positive correlations suggesting the miRNA and immune checkpoint gene expression covary positively, potentially indicating miRNA-mediated gene upregulation or co-regulatory mechanisms
    • Blue cells: Negative correlations suggesting inverse relationships where immune checkpoint gene expression decreases as miRNA expression increases (or vice versa), potentially indicating direct miRNA-mediated gene suppression or opposing regulatory pathways
    • Gray cells: Non-significant correlations falling below established statistical thresholds
    • Color intensity: Reflects correlation strength, with darker shades indicating stronger associations
  • Cancer types are displayed along the x-axis —hover over any type to view its full name and tissue origin
  • The y-axis presents immune checkpoint gene identifiers that are computationally predicted or experimentally validated as targets associated with the selected miRNA
• Pathway Enrichment Panel:
  • Right side displays corresponding biological pathways enriched for the miRNA-associated immune checkpoint genes
  • Provides functional context and systems-level understanding of the regulatory networks observed in the correlation patterns

Interactive Elements:

1. Hover over any cell to reveal detailed statistics including correlation coefficient, p-value, number of validated miRNA interactions, and prediction tool consensus count.
2. Click on any cell to generate detailed correlation plots that visualize the relationship between the selected miRNA expression and corresponding immune checkpoint gene expression in that specific cancer type through interactive scatter plots, enabling comprehensive examination of the regulatory dynamics and assessment of potential confounding factors such as tumor purity on observed correlations.
   Note: For non-linear relationships, correlation coefficient (Rho) may differ from regression line slope

Example Interpretation:

1. The interactive heatmap displays correlation patterns between hsa-miR-152-3p expression and associated immune checkpoint genes across 32 cancer types (A), providing a comprehensive overview of miRNA-mediated regulatory networks within diverse tumor contexts.
2. The selected cell (B) demonstrates a statistically significant negative correlation between hsa-miR-152-3p and CEACAM1 expression in diffuse large B-cell lymphoma (DLBC), indicating potential regulatory suppression within this hematological malignancy.
3. Interactive cell exploration reveals quantitative metrics including the Pearson correlation coefficient (-0.3976), statistical significance (p-value = 6.25e-03), and experimental validation status (0, denoting absence of direct experimental confirmation for this regulatory interaction in current literature databases).
4. Cell selection triggers generation of detailed scatter plot visualizations illustrating the inverse relationship between hsa-miR-152-3p and CEACAM1 expression levels in DLBC patient samples, enabling comprehensive assessment of the regulatory correlation and identification of potential biological or technical confounding factors.
   
5. The left panel presents immune checkpoint gene identifiers, with corresponding pathway annotations accessible through interactive exploration of the right-side color bar (C), facilitating biological context interpretation and pathway-level regulatory network analysis.

Table Overview:

This interactive table complements the heatmap visualization by providing comprehensive statistical information for correlations between the selected miRNA and immune checkpoint genes across multiple cancer types.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis
Immune Checkpoint Gene: Target immune checkpoint gene - key regulatory protein in immune response
Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)
P-value: Statistical significance of the correlation (lower values indicate higher confidence)
Cancer: Cancer type where the association was observed
Immune Checkpoint Pathway: Biological pathway context for the gene-miRNA interaction
Evidence: Classification of the miRNA-gene relationship (e.g., Predicted, Validated)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNAs, genes, cancer types, or significance thresholds
3. Row Selection: Click on any table row to generate corresponding correlation plots that provide scatter plot visualization of the selected miRNA-gene pair relationship within the specific cancer context:
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Example Interpretation:

1. The statistical table presents comprehensive correlation data between hsa-miR-152-3p and diverse immune checkpoint genes across multiple cancer types, providing quantitative support for patterns observed in the heatmap visualization.
2. The highlighted row (highlighted section) demonstrates a negative correlation between hsa-miR-152-3p expression and HLA-B in Ovarian Cancer (OV).
3. Row selection generates corresponding scatter plots that visualize the direct relationship between hsa-miR-152-3p expression levels and HLA-B expression within the OV cohort, enabling detailed examination of the statistical association and potential biological significance.

Unraveling Pathway-Centric Immune Checkpoint Networks
This module of ImmiR provides a specialized analytical framework for investigating the intricate regulatory networks within specific immune checkpoint pathways and their associations with miRNAs across multiple cancer contexts. By integrating pathway-specific gene sets, miRNA expression data, and cross-cancer comparative analysis, this section enables comprehensive characterization of pathway-level miRNA-mediated immune regulation patterns.

Steps:

1. Select Pathway: Choose the immune checkpoint pathway of interest from the curated pathway database (e.g., “PD1 & PD-L1 / PD-L2 immune checkpoint pathway”, “CTLA-4 & B7-1 / B7-2 immune checkpoint pathway," or other immune checkpoint regulatory circuits).
2. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed pathway-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships.
   Click on the info icon to view more information regarding purity adjustment.
3. Submit: Execute the analysis to generate interactive Sankey diagrams visualizing pathway-miRNA-gene networks and comprehensive statistical summary tables.

Sankey Diagram Overview:

The interactive Sankey diagram visualizes the multi-layered associations between immune checkpoint pathways, genes, miRNAs, and cancer types, providing a comprehensive view of regulatory networks.

Data Representation:

• Flow Structure: Four-tier visualization showing connections from:
  • Selected immune checkpoint pathway → Associated genes → Regulatory miRNAs → Cancer types
• Node Importance: Node size reflects connectivity strength and biological significance
  • Highly connected nodes indicate key regulatory components with broader influence across the immune checkpoint network
• Flow Width: Connection thickness represents association strength between components

Network Interpretation:

• Hub Nodes: Densely connected elements represent critical regulators with multi-cancer relevance
• Pathway Influence: Traces how pathway-level changes propagate through gene expression to miRNA regulation across different cancer contexts
• Cross-Cancer Patterns: Reveals which pathway-gene-miRNA interactions show consistent or cancer-specific associations

Interactive Elements:

• Click on specific flows to highlight pathway traces and related components

Table Overview:

This interactive table complements the heatmap visualization by providing comprehensive statistical information for correlations between the selected miRNA and immune checkpoint genes across multiple cancer types.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis

Immune Checkpoint Gene: Target immune checkpoint gene - key regulatory protein in immune response

Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)

P-value: Statistical significance of the correlation (lower values indicate higher confidence)

Q-value: Multiple testing corrected p-value that controls the false discovery rate (FDR) when analyzing numerous correlations simultaneously. This adjusted significance measure accounts for the increased probability of false positives when conducting multiple statistical comparisons across different cancer types and immune checkpoint genes. Lower q-values indicate stronger statistical confidence after correction for multiple hypothesis testing, with q < 0.05 typically considered statistically significant in genomic studies.

Cancer: Cancer type where the association was observed

Immune Checkpoint Pathway: Biological pathway context for the gene-miRNA interaction

Evidence: Classification of the miRNA-gene relationship (e.g., Predicted, Validated)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNAs, genes, cancer types, or significance thresholds
3. Row Selection: Click on any table row to generate corresponding correlation plots that provide scatter plot visualization of the selected miRNA-gene pair relationship within the specific cancer context:
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Example Interpretation:

1. The statistical table presents comprehensive correlation data between different miRNAs and CD244 across multiple cancer types, providing quantitative support for patterns observed in the heatmap visualization.
2. The highlighted row (highlighted section) demonstrates a statistically significant negative correlation between hsa-miR-125-5p expression and CD244 in ovarian cancer (OV), with a correlation coefficient of -0.114 (p-value = 2.85×10⁻², q-value = 2.63×10⁻¹).
3. Row selection generates corresponding scatter plots that provide detailed visualization of the inverse relationship between hsa-miR-125-5p and CD244 expression levels within the OV patient cohort. This interactive functionality enables comprehensive examination of the distribution patterns, statistical robustness, and potential clinical relevance of this negative association between the microRNA and the natural killer cell receptor CD244.

Pan-Cancer Profiling of Tissue-Specific Immune Regulation
This module of ImmiR provides a specialized analytical platform for investigating tissue-specific immune checkpoint regulatory patterns and their miRNA-mediated control mechanisms across related cancer types. By integrating tissue-specific expression profiles, cancer subtype datasets, and comparative genomic analysis, this section enables identification of tissue-specific immune regulatory networks and potential therapeutic targets within defined anatomical contexts.

Steps:

1. Select Tissue Type: Choose the tissue or organ system of interest from the anatomical classification system to focus analysis on cancers originating from specific anatomical sites.
2. Select Dataset: Refine the analysis by selecting a specific cancer dataset within the chosen tissue type, enabling investigation of particular cancer subtypes or patient cohorts.
3. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed pathway-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships.
   Click on the info icon to view more information regarding purity adjustment.
4. Submit: Execute the analysis to generate interactive Sankey diagrams and comprehensive statistical summary tables.

Sankey Diagram Overview:

The interactive Sankey diagram visualizes the multi-layered associations between cancer types, miRNAs, immune checkpoint pathways, and genes, offering a cancer-focused perspective on regulatory networks.

Data Representation:

• Flow Structure: Four-tier visualization showing connections from:
  • Selected cancer type → Regulatory miRNAs → Immune checkpoint pathways → Immune checkpoint genes
• Node Importance: Node size reflects connectivity strength and biological significance
  • Highly connected nodes indicate key regulatory components with broader influence across the immune checkpoint network
• Flow Width: Connection thickness represents association strength between components

Network Interpretation:

• Hub Nodes: connected miRNAs or pathways indicate central regulators in the immune checkpoint network for the selected cancer
• Pathway Influence: Highlights how miRNA activity may regulate immune checkpoint pathways and downstream gene expression in a specific cancer context
• Intra-Cancer Patterns: Reveals key miRNA–pathway–gene combinations that may underlie immune regulation within the selected tumor type

Interactive Elements:

• Click on specific flows to highlight pathway traces and related components

Table Overview:

This interactive table presents statistical associations between miRNAs and immune checkpoint genes within a selected cancer type. Each row reports the correlation coefficient (Rho), p-value, and q-value for a specific miRNA-gene correlation, with results grouped by immune checkpoint pathway for the chosen cancer type.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis

Immune Checkpoint Gene: Target immune checkpoint gene - key regulatory protein in immune response

Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)

P-value: Statistical significance of the correlation (lower values indicate higher confidence)

Q-value: Multiple testing corrected p-value that controls the false discovery rate (FDR) when analyzing numerous correlations simultaneously. This adjusted significance measure accounts for the increased probability of false positives when conducting multiple statistical comparisons across different cancer types and immune checkpoint genes. Lower q-values indicate stronger statistical confidence after correction for multiple hypothesis testing, with q < 0.05 typically considered statistically significant in genomic studies.

Cancer: Cancer type where the association was observed

Immune Checkpoint Pathway: Biological pathway context for the gene-miRNA interaction

Evidence: Classification of the miRNA-gene relationship (e.g., Predicted, Validated)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNAs, genes, cancer types, or significance thresholds
3. Row Selection: Click on any table row to generate corresponding correlation plots that provide scatter plot visualization of the selected miRNA-gene pair relationship within the specific cancer context:
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Example Interpretation:

1. The statistical table presents comprehensive correlation data between hsa-miR-34c-5p and KIR3DL3 within a specific cancer type, providing quantitative support for patterns observed in the heatmap visualization.
2. The highlighted row (highlighted section) demonstrates a statistically significant negative correlation between hsa-miR-34c-5p expression and KIR3DL3 in lung adenocarcinoma (LUAD), with a correlation coefficient of -0.193 (p-value = 1.29×10⁻⁵, q-value = 4.77×10⁻⁴).
3. Row selection generates corresponding scatter plots that provide detailed visualization of the inverse relationship between hsa-miR-34c-5p and KIR3DL3 expression levels within the LUAD patient cohort. This interactive functionality enables comprehensive examination of the distribution patterns, statistical robustness, and potential clinical relevance of this significant negative association between the microRNA and the killer cell immunoglobulin-like receptor.

Exploring miRNA Regulatory Networks in Tumor-Associated Immune Infiltration
The miRNA module of ImmiR enables comprehensive analysis of miRNA-immune cell infiltration relationships within the tumor microenvironment (TME) across 32 cancer types. By integrating multi-source datasets, this tool provides quantitative insights into how miRNAs potentially regulate immune cell abundance and composition, with direct relevance to cancer immunotherapy research.

Steps:

1. Input miRNA: Enter the miRNA identifier of interest (e.g., "hsa-miR-155-5p" or "hsa-miR-21-5p").
2. Select Immune Infiltrates: Choose specific immune cell types (T cells, B cells, macrophages) or broader immune categories (lymphocytes, myeloid cells) from the dropdown menu for targeted analysis.
3. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed gene-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships.
   Click on the info icon to view more information regarding purity adjustment.
4. Submit: Execute the analysis to generate interactive heatmaps and statistical summary tables.

Heatmap Overview:

The interactive heatmap displays correlation patterns between the selected miRNA and associated immune infiltrates across 32 cancer types, providing comprehensive visualization of miRNA-immune cell relationships within diverse tumor microenvironments.

Data Representation:

• Main Heatmap:
  • Each cell represents a correlation quantified using a specific computational method for immune cell abundance estimation, ensuring methodological transparency across different immune cell types
  • Color-coded cells indicate correlation direction, significance, and biological
    

    interpretation:

    • Red cells: Positive correlation suggesting the miRNA and immune cell infiltration covary positively, potentially indicating the miRNA promotes infiltration or activity of that immune cell type
    • Blue cells: Negative correlations suggesting inverse relationships where immune cell infiltration decreases as miRNA expression increases (or vice versa), potentially indicating the miRNA suppresses infiltration or activity of that immune cell type
    • Gray cells: Non-significant correlations
    • Color intensity: Reflects correlation strength, with darker shades indicating stronger associations
  • Cancer types are displayed along the x-axis —hover over any type to view its full name and tissue origin
  • The y-axis presents immune infiltrate types and their corresponding immune subtype tools (e.g., XCELL, CIBERSORT, TIMER) associated with the selected miRNA
• Immune Infiltrate Panel:
  • Right side displays detailed information about the immune cell types and infiltration patterns
  • Provides biological context for the correlation patterns observed in the heatmap

Interactive Elements:

1. Hover over any cell to reveal detailed statistics including correlation coefficient, p-value, and the specific immune subtype tool used for immune cell quantification
2. Click on any cell to generate detailed correlation plots that visualize the relationship between miRNA expression and immune cell infiltration in that specific cancer type through scatter plots, allowing closer examination of the relationship and revealing the potential influence of tumor purity on observed correlations
   Note: For non-linear relationships, correlation coefficient (Rho) may differ from regression line slope.

Example Interpretation:

1. The heatmap visualizes correlation patterns between hsa-miR-1306-3p and diverse immune infiltrates across multiple cancer types (A), providing a comprehensive overview of miRNA-immune cell associations in various tumor contexts.
2. The highlighted cell (B) demonstrates a positive correlation between hsa-miR-1306-3p expression and CD4+ memory T cell infiltration in breast invasive carcinoma (BRCA), as quantified using the XCELL deconvolution algorithm.
3. Interactive exploration through hover functionality reveals detailed statistical parameters including the correlation coefficient (0.249), significance level (p-value: 1.528e-16), and the computational method employed (XCELL) for immune cell abundance estimation.
4. ell selection generates corresponding scatter plots that visualize the direct relationship between hsa-miR-1306-3p expression levels and CD4+ memory T cell infiltration within the BRCA cohort, enabling detailed examination of the correlation pattern.
   

Table Overview:

This interactive table complements the heatmap visualization by providing comprehensive statistical information for correlations between the selected miRNA and immune infiltrates across multiple cancer types.

Column Descriptions:

Immune Subtype Tool: Provides a comprehensive classification system for investigating specific immune cell populations within the tumor microenvironment. This tool combines detailed immune cell type categorization with methodologically diverse quantification approaches, enabling precise analysis of immune infiltration patterns and their correlations with miRNA expression across cancer types.

Cancer: Cancer type where the association was observed

Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)

P-value: Statistical significance of the correlation (lower values indicate higher confidence)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific immune subtypes, cancer types, or significance thresholds
3. Row Selection: Click on any table row to generate corresponding correlation plots that provide scatter plot visualization of the selected miRNA-immune infiltrate relationship within the specific cancer context
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Example Interpretation:

1. The statistical table presents comprehensive correlation data between hsa-miR-1306-3p and diverse immune infiltrate populations across multiple cancer types, providing quantitative support for patterns observed in the heatmap visualization.
2. The highlighted row (highlighted section) demonstrates a positive correlation between hsa-miR-1306-3p expression and CD4+ T central memory cell infiltration in lung squamous cell carcinoma (LUSC), as quantified using the XCELL deconvolution algorithm.
3. Row selection generates corresponding scatter plots that visualize the direct relationship between hsa-miR-1306-3p expression levels and CD4+ T central memory cell abundance within the LUSC cohort, enabling detailed examination of the statistical association and potential biological significance.

Immune Infiltration Landscape
This module of ImmiR provides a specialized analytical platform for investigating the complex relationships between specific immune cell infiltration patterns and miRNA expression within the tumor microenvironment across diverse cancer types. By utilizing immune infiltrate selection tools and cell type/subtype classification systems, combined with miRNA expression profiling, this section enables targeted investigation of how specific immune cell populations correlate with miRNA regulatory networks across different cancer contexts.

Steps:

1. Select Immune Infiltrate: Choose the immune cell population of interest from the comprehensive immune cell classification system (e.g., T cell subsets, B cells, NK cells, macrophages, or other immune effector populations).
2. Select Cell Type: Refine the analysis by selecting specific cell subtypes within the chosen immune population, enabling focused investigation of particular immune cell phenotypes and functional states.
   Click on the info icon to view details about the immune subtype tools used for immune cell quantification.
3. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed gene-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships.
   Click on the info icon to view more information regarding purity adjustment.
4. Submit: Execute the analysis to generate interactive heatmaps and comprehensive statistical summary tables.

Heatmap Overview:

The interactive heatmap displays correlation patterns between associated miRNAs and the selected immune infiltrate across 32 cancer types, providing comprehensive visualization of miRNA-immune cell relationships within diverse tumor microenvironments.

Data Representation:

• Each cell represents a correlation quantified using the specific computational method for immune cell abundance estimation, ensuring methodological consistency for the selected immune cell type
• Color-coded cells indicate correlation direction, significance, and biological
  

  interpretation:

  • Red cells: Positive correlation suggesting the miRNA and immune cell infiltration covary positively, potentially indicating the miRNA promotes infiltration or activity of the selected immune cell type
  • Blue cells: Negative correlations suggesting inverse relationships where immune cell infiltration decreases as miRNA expression increases (or vice versa), potentially indicating the miRNA suppresses infiltration or activity of the selected immune cell type
  • Gray cells: Non-significant correlations
  • Color intensity: Reflects correlation strength, with darker shades indicating stronger associations
• Cancer types are displayed along the x-axis —hover over any type to view its full name and tissue origin
• The y-axis presents miRNAs associated with the selected immune infiltrate type and corresponding immune subtype tool

Interactive Elements:

1. Hover over any cell to reveal detailed statistics including correlation coefficient, p-value, and the specific immune subtype tool used for immune cell quantification
2. Click on any cell to generate detailed correlation plots that visualize the relationship between the specific miRNA expression and immune cell infiltration in that cancer type through scatter plots, allowing closer examination of the relationship and revealing the potential influence of tumor purity on observed correlations
   Note: For non-linear relationships, correlation coefficient (Rho) may differ from regression line slope.

Example Interpretation:

1. The correlation heatmap presents quantitative associations between multiple microRNAs (C) and CD3 T cell abundance across diverse cancer types (A), with immune cell infiltration levels determined through MCPCOUNTER computational methodology.
2. The highlighted cell (B) demonstrates a strong positive correlation between hsa-miR-330-5p expression and T cell CD3 infiltration in brain lower-grade glioma (LGG), indicating a robust statistical association between this microRNA and T lymphocyte presence within the tumor microenvironment.
3. Hovering functionality provides immediate access to comprehensive statistical parameters, including the correlation coefficient (0.747), statistical significance (p-value = 1.876×10⁻⁹¹), and the specific computational algorithm employed (MCPCOUNTER) for immune cell quantification.
4. Cell selection triggers the generation of dual scatter plot analyses that elucidate the relationship between hsa-miR-330-5p expression and T cell CD3 infiltration in LGG specimens. The left panel illustrates the association between tumor purity and microRNA expression levels, while the right panel displays the direct correlation between the microRNA and immune infiltrate abundance, enabling comprehensive assessment of potential confounding factors and biological relevance.
   

Table Overview:

This interactive table complements the heatmap visualization by providing comprehensive statistical information for correlations between the selected miRNA and immune infiltrates across multiple cancer types.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis

Cancer: Cancer type where the association was observed

Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)

P-value: Statistical significance of the correlation (lower values indicate higher confidence)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNA, cancer types, or significance thresholds
3. Row Selection: Click on any table row to generate corresponding correlation plots that provide scatter plot visualization of the selected miRNA-immune infiltrate relationship within the specific cancer context
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Example Interpretation:

1. The statistical correlation table displays quantitative associations between T cell CD3 abundance, as determined through MCPCOUNTER computational methodology, and various microRNA expression profiles across multiple cancer types.
2. The highlighted row (highlighted section) demonstrates a statistically significant positive correlation between hsa-miR-183-3p expression and T cell CD3 infiltration in ovarian serous cystadenocarcinoma (OV), with a correlation coefficient of 0.319 and p-value of 1.43×10⁻⁷, indicating a robust statistical association between this microRNA and T lymphocyte presence within the ovarian tumor microenvironment.
3. Row selection initiates the generation of comprehensive dual scatter plot analyses that elucidate the relationship between hsa-miR-183-3p expression and T cell CD3 infiltration in OV specimens. The left panel illustrates the association between tumor purity and microRNA expression levels, while the right panel displays the direct correlation between the microRNA and immune infiltrate abundance, enabling thorough assessment of potential confounding variables and biological significance of the observed association.

Cancer-Specific Immune-miRNA Regulatory Networks
This module of ImmiR provides a focused analytical framework for investigating cancer type-specific relationships between immune cell infiltration and miRNA expression within defined tumor contexts. By narrowing the analytical scope to individual cancer types, this section enables deep characterization of cancer-specific immune landscapes and identification of tumor-specific miRNA-mediated immune regulatory mechanisms with direct therapeutic implications.

Steps:

1. Select Cancer Dataset: Choose the specific cancer type of interest from the comprehensive cancer dataset collection (e.g., lung adenocarcinoma [LUAD], breast invasive carcinoma [BRCA], or other cancer types).
2. Select Immune Infiltrates: Choose one or multiple immune cell populations for investigation within the selected cancer context, enabling comprehensive or focused analysis of immune-miRNA interactions.
3. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed gene-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships.
   Click on the info icon to view more information regarding purity adjustment.
4. Submit: Execute the analysis to generate interactive heatmaps and comprehensive statistical summary tables.

Heatmap Overview:

The interactive heatmap displays correlation patterns between associated miRNAs and the selected immune infiltrate across 32 cancer types, providing comprehensive visualization of miRNA-immune cell relationships within diverse tumor microenvironments.

Data Representation:

• Main Heatmap:
  • Each cell represents a correlation quantified using a specific computational method for immune cell abundance estimation, ensuring methodological transparency across different immune cell types
  • Color-coded cells indicate correlation direction, significance, and biological
    

    interpretation:

    • Red cells: Positive correlation suggesting the miRNA and immune cell infiltration covary positively, potentially indicating the miRNA promotes infiltration or activity of that immune cell type
    • Blue cells: Negative correlations suggesting inverse relationships where immune cell infiltration decreases as miRNA expression increases (or vice versa), potentially indicating the miRNA suppresses infiltration or activity of that immune cell type
    • Gray cells: Non-significant correlations
    • Color intensity: Reflects correlation strength, with darker shades indicating stronger associations
  • miRNAs are displayed along the x-axis
  • The y-axis presents immune infiltrate types and their corresponding immune subtype tools (e.g., XCELL, CIBERSORT, TIMER)
• Immune Infiltrate Panel:
  • Right side displays detailed information about the immune cell types and infiltration patterns
  • Provides biological context for the correlation patterns observed in the heatmap

Interactive Elements:

1. Hover over any cell to reveal detailed statistics including correlation coefficient, p-value, and the specific immune subtype tool used for immune cell quantification
2. Click on any cell to generate detailed correlation plots that visualize the relationship between miRNA expression and immune cell infiltration through scatter plots, allowing closer examination of the relationship and revealing the potential influence of tumor purity on observed correlations
   Note: For non-linear relationships, correlation coefficient (Rho) may differ from regression line slope.

Example Interpretation:

1. The correlation heatmap presents quantitative associations between multiple microRNAs (A) and T cell CD4 abundance in lung adenocarcinoma (LUAD), with immune cell infiltration levels determined through ImmuCellAI computational methodology.
2. The highlighted cell (B) demonstrates a statistically significant positive correlation between hsa-miR-125a-5p expression and T cell CD4 infiltration in LUAD, indicating a robust association between this microRNA and CD4+ T lymphocyte presence within the lung adenocarcinoma tumor microenvironment.
3. Hovering functionality provides immediate access to comprehensive statistical parameters, including the correlation coefficient (0.402), statistical significance (p-value = 4.22×10⁻²¹), and the specific computational algorithm employed (ImmuCellAI) for immune cell quantification.
4. Cell selection triggers the generation of scatter plot analyses that elucidate the relationship between hsa-miR-125a-5p expression and T cell CD4 infiltration in LUAD specimens, enabling comprehensive assessment of the statistical association and biological relevance.
   

Table Overview:

This interactive table complements the heatmap visualization by providing comprehensive statistical information for correlations between the selected miRNA and immune infiltrates across multiple cancer types.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis

Immune Subtype Tool: Provides a comprehensive classification system for investigating specific immune cell populations within the tumor microenvironment. This tool combines detailed immune cell type categorization with methodologically diverse quantification approaches, enabling precise analysis of immune infiltration patterns and their correlations with miRNA expression across cancer types.

Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)

P-value: Statistical significance of the correlation (lower values indicate higher confidence)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNA, immune subtype tool, or significance thresholds
3. Row Selection: Click on any table row to generate corresponding correlation plots that provide scatter plot visualization of the selected miRNA-immune infiltrate relationship within the specific cancer context
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Example Interpretation:

1. The statistical correlation table displays quantitative associations between multiple microRNAs and CD4+ T central memory cell abundance in lung adenocarcinoma (LUAD), with immune cell infiltration levels determined through XCELL computational methodology.
2. The highlighted row (highlighted section) demonstrates a statistically significant negative correlation between hsa-miR-152-5p expression and CD4+ T central memory cell infiltration in LUAD, with a correlation coefficient of -0.130 and p-value of 3.52×10⁻³, indicating an inverse relationship between this microRNA and CD4+ T central memory lymphocyte presence within the lung adenocarcinoma tumor microenvironment.
3. Row selection initiates the generation of comprehensive scatter plot analyses that elucidate the inverse relationship between hsa-miR-152-5p expression and CD4+ T central memory cell infiltration in LUAD specimens, enabling thorough assessment of the statistical association and potential biological significance of this negative correlation.

Use this tool to explore associations between molecular features and immune-related characteristics across various cancer datasets. Choose between two complementary analysis approaches to investigate immune-molecular interactions:
Instructions :
Step1 - Analysis Type Selection
Choose your desired analysis type:
Immune Checkpoint Association Analysis: Unraveling the Regulatory Networks of Immune Checkpoint Genes
Immune Infiltration Association Analysis: Decoding miRNA-Immune Cell Population Relationships in Cancer

Analysis-Specific Instructions :
If you selected: Immune Checkpoint Association Analysis
This module provides a comprehensive analytical platform for investigating the complex regulatory interactions between immune checkpoint genes and various biological factors including miRNAs, pathways, and cancer-specific contexts. By integrating multi-dimensional datasets including gene expression profiles, miRNA target prediction algorithms, pathway activity scores, and tumor purity metrics, this section enables systematic identification of potential regulatory mechanisms governing immune checkpoint expression across diverse cancer types.
Step:

2. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed gene-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships. Click on the info icon to view more information regarding purity adjustment.
   
3. Layer 1 - Primary Input Selection: Choose your primary variable of interest from Gene, Pathway, Cancer Type, or miRNA, then input the specific identifier (e.g., "CD274" for PD-L1, "LUAD" for lung adenocarcinoma, or "hsa-miR-155" for miRNA analysis). The system will display the number of available records for your selection.
   
   
4. Layer 2 - Optional Secondary Filter: If desired, add an additional filter to refine your analysis. Select a complementary variable type and input the specific identifier. Note that Gene and Pathway cannot be combined as they are typically co-associated. The system will show updated record counts based on your combined selections.
   
5. View Result or Submit: Execute the analysis to generate comprehensive statistical results.

If you selected: Immune Infiltration Association Analysis
This module provides a specialized analytical framework for investigating the correlational patterns between miRNAs and immune cell population infiltration within specific cancer microenvironments. By leveraging computational immune subtype tools and integrating miRNA expression profiles with quantified immune cell populations, this section enables systematic exploration of miRNA-mediated immune regulation mechanisms across different cancer contexts.
Step:

2. Adjust for Tumor Purity: Enable tumor purity adjustment to control for stromal and normal cell contamination within tumor samples. This correction is strongly recommended as tumor heterogeneity can significantly confound observed gene-miRNA correlations, and adjustment facilitates identification of genuine biological regulatory relationships. Click on the info icon to view more information regarding purity adjustment.
   
3. Layer 1 - Primary Input Selection: Choose your primary variable of interest from miRNA, Cancer Type, or Immune Infiltrates, then input the specific identifier (e.g., "hsa-miR-155-5p" for miRNA analysis, "BRCA" for breast cancer, or "T cell CD8+" for immune cell populations). Multiple immune infiltrates can be selected simultaneously. The system will display the number of available records for your selection.
   
   
4. Layer 2 - Optional Secondary Filter: If desired, add an additional filter to enhance analysis specificity. Select a complementary variable type and input the specific identifier. For immune infiltrates, multiple selections remain available. The system will show updated record counts based on your combined selections.
   
5. View Result or Submit: Execute the analysis to generate comprehensive statistical results reporting immune subtype tool, miRNA identifier, cancer type, correlation coefficients, and p-values for each association.

Table Overview:

This results table displays statistical associations between miRNAs and immune checkpoint genes based on your selected analysis parameters. The table structure and content depend on your input selections, with some columns remaining fixed while others vary according to your query.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis

• Fixed when specific miRNA selected as input
• Variable when gene-focused, cancer dataset-focused, or pathway-focused analysis performed

Immune Checkpoint Gene: Target immune checkpoint gene - key regulatory protein in immune response

• Fixed when specific gene selected as input
• Variable when miRNA-focused or cancer dataset-focused analysis performed

Correlation Coefficient: Numerical measure of association strength (range: -1 to +1)

• Negative values indicate inverse relationships
• Values closer to -1 or +1 indicate stronger associations

P-value: Statistical significance of the correlation (lower values indicate higher confidence)
Cancer: Cancer type where the association was observed

• Fixed when specific dataset selected as input
• Variable when gene-focused, miRNA-focused, or pathway-focused analysis performed

Immune Checkpoint Pathway: Biological pathway context for the gene-miRNA interaction

• Fixed when specific pathway selected as input
• Variable when gene-focused, miRNA-focused, or cancer dataset-focused analysis performed

Evidence: Classification of the miRNA-gene relationship (e.g., Predicted, Validated)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNAs, genes, cancer types, or significance thresholds
3. Row Selection: Click on any row to generate detailed correlation plots for the selected miRNA-gene pair:
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Feature Interactions:

Analysis Types and Table Variations:

Single Input Analysis:
Gene-Focused Analysis: When you select a specific immune checkpoint gene

• Gene column remains consistent across all results
• Pathway column is automatically determined (each gene belongs to one specific pathway)
• miRNA and cancer columns vary, showing all miRNAs associated with your selected gene
• Reveals the regulatory landscape targeting your gene of interest

miRNA-Focused Analysis: When you select a specific miRNA

• miRNA column remains consistent across all results
• Gene, pathway, and cancer columns vary, showing all targets of your selected miRNA
• Reveals the regulatory scope of your miRNA of interest across different pathways

Pathway-Focused Analysis: When you select a specific immune checkpoint pathway

• Pathway column remains consistent across all results
• Gene column varies within the pathway (e.g., TLR pathway includes TLR4, TLR8, TLR9, TLR7, TLR3)
• miRNA and cancer columns vary, showing all miRNAs associated with genes in your selected pathway
• Reveals comprehensive miRNA-gene interactions within your pathway of interest across different cancer types

Dataset-Focused Analysis: When you select a specific cancer dataset

• Cancer column remains consistent across all results
• miRNA, gene, and pathway columns vary within the selected cancer context
• Shows all pathway-gene combinations and their miRNA associations in your chosen cancer type

Combination Analysis: When you select two or more input parameter

• Multiple columns remain fixed based on your selections
• Remaining columns vary, providing focused analysis within your specified constraints
• Examples:
  • Gene + Cancer: Specific gene in specific cancer (pathway automatically determined)
  • miRNA + Pathway: Specific miRNA across all genes within a pathway (e.g., miRNA interactions with TLR4, TLR8, TLR9, TLR7, TLR3 in TLR pathway)
  • Pathway + Cancer: All genes within a pathway in specific cancer type

Key Patterns to Observe:

1. Correlation Strength: Higher absolute correlation coefficient values indicate stronger associations between miRNA and gene expression
2. Statistical Significance: P-values <0.05 indicate statistically significant correlations
3. Consistency Patterns: Similar correlation directions across multiple results suggest robust regulatory relationships
4. Pathway Convergence: Multiple miRNAs or genes within the same pathway indicate coordinated regulation

Research Applications:

• Gene-focused: Identify regulatory miRNAs for therapeutic targeting
• miRNA-focused: Understand the regulatory scope and pathway involvement
• Pathway-focused: Map comprehensive regulatory networks
• Cancer-focused: Discover cancer-specific immune regulatory mechanisms

Table Overview:

This results table displays statistical associations between miRNAs and immune infiltrates based on your selected analysis parameters. The table structure and content depend on your input selections, with some columns remaining fixed while others vary according to your query.

Column Descriptions:

miRNA: MicroRNA identifier - the regulatory RNA molecule in the analysis

• Fixed when specific miRNA selected as input
• Variable when cancer dataset-focused, or immune infiltrate-focused analysis performed

Immune subtype tool: Specific immune cell infiltrate subtype and computational method used for correlation analysis

• Fixed when specific immune infiltrate selected as input
• Variable when miRNA-focused or cancer dataset-focused analysis performed

Cancer: Cancer type where the association was observed

• Fixed when specific dataset selected as input
• Variable when miRNA-focused, or immune infiltrate-focused analysis performed

Correlation Coefficient: Statistical significance of the correlation (lower values indicate higher confidence)

• Negative values indicate inverse relationships
• Values closer to -1 or +1 indicate stronger associations

P-value: Statistical significance of the correlation (lower values indicate higher confidence)

Interactive Features:

1. Column Sorting: Click on any column header to reorder the data based on the column’s values
   • Single click for ascending order, double click for descending order
   • Useful for prioritizing results by correlation strength, p-value significance, or alphabetical organization
2. Column Filtering: Use the search box located under each column name to filter data
   • Enter specific values or partial matches to narrow down results
   • Combine multiple column filters for precise data subset selection
   • Particularly useful for focusing on specific miRNAs, immune subtypes, cancer types, or significance thresholds
3. Row Selection: Click on any row to generate detailed correlation plots for the selected miRNA-immune infiltrate pair:
   • Log2 Expression Plot (Left): Standard correlation visualization using log2-transformed expression values
   • Purity-Adjusted Plot (Right): Correlation analysis adjusted for tumor purity to reduce confounding effects from non-tumor cells
   
4. Export: Download or copy filtered results for external analysis or reporting purposes.

Feature Interactions:

Analysis Types and Table Variations:
Single Input Analysis:
miRNA-Focused Analysis: When you select a a specific miRNA

• miRNA column remains consistent across all results
• Immune subtype tool and cancer columns vary, showing all immune infiltration associations of your selected miRNA
• Reveals the immunomodulatory scope of your miRNA of interest across different cancer types

Immune Infiltrate-Focused Analysis: When you select a specific immune infiltrate type

• Immune subtype tool column remains consistent across all results
• miRNA and cancer columns vary, showing all miRNAs associated with your selected immune infiltrate
• Reveals comprehensive miRNA regulatory networks affecting specific immune cell populations across different cancer types

Cancer Dataset-Focused Analysis: When you select a specific cancer dataset

• Cancer column remains consistent across all results
• miRNA and immune subtype tool columns vary within the selected cancer context
• Shows all immune infiltration patterns and their miRNA associations in your chosen cancer type

Combination Analysis: When you select two or more input parameter

• Multiple columns remain fixed based on your selections
• Remaining columns vary, providing focused analysis within your specified constraints
• Examples:
  • miRNA + Cancer: Specific miRNA in specific cancer type (immune subtype tool varies)
  • miRNA + Immune Infiltrate: Specific miRNA-immune cell association across cancer types
  • Immune Infiltrate + Cancer: Specific immune cell type in specific cancer (miRNA varies)

Key Patterns to Observe:

1. Correlation Strength: Higher absolute correlation coefficient values indicate stronger associations between miRNA and gene expression
2. Statistical Significance: P-values <0.05 indicate statistically significant correlations
3. Consistency Patterns: Similar correlation directions across multiple results suggest robust regulatory relationships
4. Pathway Convergence: Multiple miRNAs or genes within the same pathway indicate coordinated regulation

Research Applications:

• miRNAs-focused: Understand immunomodulatory effects and therapeutic potential for immune regulation
• Immune infiltrate-focused: Identify miRNA regulators of specific immune cell populations for targeted therapy
• Cancer-focused: Discover cancer-specific immune-miRNA regulatory mechanisms and biomarkers
• Biomarker Discovery: Identify miRNA signatures associated with immune infiltration patterns for prognosis and treatment response prediction

• miRNA-target interactions

The miRNA-target interactions in ImmiR are categorized into three types: "Validated," "Predicted," and "Without any evidence."
The "Validated" interactions are sourced from the miRTarBase database Release 6.1, which includes over 366,000 interactions.
Predicted miRNA targets are identified by using 12 miRNA target prediction tools, including DIANA-microT, MicroT4, miRBridge, miRDB, miRMap, PITA, RNAhybrid, TargetScan, PICTAR2, RNA22, miRWalk, and miRanda.
Only targets identified by at least 6 tools are retained to enhance the reliability of the prediction results.

• Reference of tools utilized in ImmiR

The below table provides the PMID of the tools we used in the Immune infiltration association section. You can use the PMID of each tool to search for more detailed information.

Tools		PMID
CIBERSORT		PMID: 25822800
XCELL		PMID: 29141660
TIMER		PMID: 32442275
MCP-counter		PMID: 27765066
EPIC		PMID: 29130882
QuanTIseq		PMID: 31126321
ImmuCellAI		PMID: 32274301
ESTIMATE		PMID: 24113773
TIDE		PMID: 30127393

In each heatmap and table of ImmiR, you can view the correlation of two variables by clicking on specific cells or rows. By doing so, correlation plots related to your selection will pop up. If the "Purity Adjustment" option has been chosen in the input data process, you will see two correlation plots.
To illustrate the correlation plots, consider the example of hsa-miR-1306-3p and T cells CD4 memory XCELL in BRCA under the miRNA section in the Immune infiltration association as below. The details of the two plots will be presented in the order indicated in the screenshot.

1. The plot on the right displays the correlation between selected immune infiltrates and log2 expression of miRNA. This plot examines whether the miRNA impacts specific immune infiltrates. By performing tumor purity correction, the influences of tumor purity can be eliminated.
2. The plot on the left illustrates the correlation between tumor purity and log2 miRNA expression. This plot primarily focuses on determining whether the source of the miRNA is from the tumor (positive correlation) or the surrounding stroma cells (negative correlation).

• Figure manipulation

As the mouse hovers over the interactive figure, the toolbar appears at the top of each figure.



1. Download figure. Press to download the figure as a png.
2. Zoom. Press to circle the area that you want to zoom in.
3. Zoom in. Press to zoom in the figure.
4. Zoom out. Press to zoom out the figure.
5. Reset axes. Press to back to the original setting of the figure.

• Table manipulation

The toolbar is on the top of each table.



1. Download. Press the “CSV” button on the top-left of the table to download the table as a CSV file.
2. Copy. Press the “Copy” button next to the “CSV” button on the top-left of the table to copy the whole table to the clipboard.
3. Search. Enter keywords on the top-right of the table to get a quick search over the table.
4. Click on the column name to reorder the table.
5. Search for specific values in the table.