3 Datasets

3.1 TCGA Cohorts

Database-driven workflow for TCGA cohort analysis, including data selection, signature scoring or TME deconvolution, clustering, visualization, survival analysis, correlation analysis, and group comparison.

This workflow loads processed TCGA cohort data from the internal database, matches clinical information, and passes the prepared dataset into multiple downstream analysis modules.

Overview

The workflow is organized into five parts:

  • Part 1 · Data Preparation
    • Data Selection
    • TME Cluster
  • Part 2 · Visualization
    • Heatmap
    • Box Plot
    • Percent Bar Plot
    • Cell Bar Plot
  • Part 3 · Survival Analysis
    • Batch Survival
    • Forest Plot
    • Heatmap
    • Survival Plot
    • Survival Group
    • Time ROC
    • Sig ROC
  • Part 4 · Correlation
    • Batch Correlation
    • Partial Correlation
    • Single Correlation
    • Correlation Matrix
  • Part 5 · Group Comparison
    • Wilcoxon Test
    • Kruskal Test
    • Heatmap
    • Box Plot

Data source

The workflow queries TCGA-derived data from the database and returns two matched tables:

  • Expression / Score Data
    • Signature score matrix or TME deconvolution matrix, depending on the selected mode
  • Clinical Data
    • Matched TCGA phenotype and clinical annotation table

Part 1 · Data Selection

This step prepares the input dataset for all downstream modules.

Parameters

  • Cancer Type (tcga_cancer) — TCGA cohort to load
    • TCGA Cancer Types
    • Pancancer
  • Analysis Mode (data_type)
    • Calculate Sigscore
    • Deconvolute TME

If Analysis Mode = Calculate Sigscore

  • Scoring Method (calculate_sig_score_method)
    • PCA
    • ssGSEA
  • Signature Set (calculate_sig_score_signature_pca) for PCA
    • TME
    • Metabolism
    • Collection
  • Signature Set (calculate_sig_score_signature_ssgsea) for ssGSEA
    • Go_bp
    • Go_cc
    • Go_mf
    • KEGG
    • Hallmark
    • Reactome

If Analysis Mode = Deconvolute TME

  • Algorithm (deconvo_tme_method)
    • CIBERSORT
    • EPIC
    • quanTIseq
    • xCell
    • ESTIMATE
    • TIMER
    • MCPcounter
    • IPS
    • Integration

Data Selection steps

  1. Select the Cancer Type.
  2. Select the Analysis Mode.
  3. If using signature mode, choose the scoring method and signature set.
  4. If using TME mode, choose the deconvolution algorithm.
  5. Click Submit.
  6. Review the loaded Data and Clinical Data tables.

Part 1 · TME Cluster

After data loading, the workflow can perform unsupervised clustering using selected TME-related or score-related features.

TME Cluster parameters

  • Features (features) — one or more numeric variables used for clustering
  • Min Clusters (min_nc) — minimum number of clusters evaluated
  • Max Clusters (max.nc) — maximum number of clusters evaluated

TME Cluster steps

  1. Open the TME Cluster tab.
  2. Select one or more clustering Features.
  3. Set Min Clusters and Max Clusters.
  4. Click Run Analysis.
  5. Review the cluster assignment table and cluster summary.

How downstream modules use the prepared data

After data preparation, the workflow automatically builds a combined dataset that includes:

  • clinical variables
  • selected TCGA signature or TME variables
  • optional cluster assignment from TME Cluster

This combined table is then used as the shared input for downstream modules in Parts 2–5.

Part 2 · Visualization

This section provides direct plotting modules for the prepared TCGA dataset:

  • Heatmap — visualize selected signatures or scores across groups
  • Box Plot — compare one signature across categorical groups
  • Percent Bar Plot — display proportions of categorical annotations
  • Cell Bar Plot — show deconvolution-based cell composition across samples

Part 3 · Survival Analysis

This section provides survival-related modules using matched TCGA clinical data:

  • Batch Survival — screen multiple variables by Cox analysis
  • Forest Plot — visualize hazard ratios from batch survival results
  • Heatmap — display selected survival-associated variables
  • Survival Plot — Kaplan–Meier curves for a selected signature
  • Survival Group — Kaplan–Meier curves for a categorical variable
  • Time ROC — time-dependent ROC for prognostic variables
  • Sig ROC — ROC analysis for selected variables against outcome

Part 4 · Correlation

This section provides correlation-based analyses:

  • Batch Correlation — correlate one target with multiple features
  • Partial Correlation — correlate variables while adjusting for a control variable
  • Single Correlation — visualize correlation between two variables
  • Correlation Matrix — compute and plot feature-set correlation matrices

Part 5 · Group Comparison

This section provides statistical comparison modules:

  • Wilcoxon Test — compare numeric variables between two groups
  • Kruskal Test — compare numeric variables across multiple groups
  • Heatmap — visualize selected group-associated variables
  • Box Plot — visualize group differences for one selected variable

Output

The workflow returns a matched TCGA dataset that can be reused across multiple downstream modules, including:

  • processed expression or score matrix
  • matched clinical table
  • optional cluster annotation
  • downstream plots and result tables generated in each part

Download

  • Tables generated in downstream modules can be exported from their corresponding Download panels.
  • Plots generated in downstream modules can be exported from their corresponding Download panels.
  • An initial plot size is provided, which can be adjusted if needed.
  • If needed, you can adjust the plot width and height before downloading to obtain a more suitable layout.

3.2 Cancer Cohorts

Database-driven workflow for cancer cohort analysis, including data selection, signature scoring or TME deconvolution, clustering, visualization, correlation analysis, and group comparison.

This workflow loads processed cancer cohort data from the internal database, subsets samples by cancer type and cohort, and passes the prepared dataset into multiple downstream analysis modules.

Overview

The workflow is organized into four parts:

  • Part 1 · Data Preparation
    • Data Selection
    • TME Cluster
  • Part 2 · Visualization
    • Heatmap
    • Box Plot
    • Cell Bar Plot
  • Part 3 · Correlation
    • Batch Correlation
    • Partial Correlation
    • Single Correlation
    • Correlation Matrix
  • Part 4 · Group Comparison
    • Wilcoxon Test
    • Kruskal Test
    • Heatmap
    • Box Plot

Data source

The workflow queries processed cancer cohort data from the database and returns a prepared data table for downstream analysis.

The loaded table typically includes:

  • CancerType
  • Dataset
  • ID
  • selected signature score columns or TME deconvolution columns, depending on the selected analysis mode

After clustering, an additional Cluster column can be appended and reused in downstream modules.

Part 1 · Data Preparation

This part prepares the input dataset for all downstream analyses.

Data Selection

Parameters

  • Cancer Type (sel_cancer) — cancer type used to subset the database
    • Dynamically populated from the database
  • Cohorts (sel_cohort) — one or more cohorts within the selected cancer type
    • Dynamically populated after selecting the cancer type
  • Analysis Mode (data_type)
    • Calculate Sigscore
    • Deconvolute TME

If Analysis Mode = Calculate Sigscore

  • Scoring Method (calculate_sig_score_method)
    • PCA
    • ssGSEA
  • Signature Set (calculate_sig_score_signature_pca) for PCA
    • TME
    • Metabolism
    • Collection
  • Signature Set (calculate_sig_score_signature_ssgsea) for ssGSEA
    • Go_bp
    • Go_cc
    • Go_mf
    • KEGG
    • Hallmark
    • Reactome

If Analysis Mode = Deconvolute TME

  • Algorithm (deconvo_tme_method)
    • CIBERSORT
    • CIBERSORT(abs)
    • EPIC
    • quanTIseq
    • ESTIMATE
    • TIMER
    • MCPcounter
    • xCell
    • Integration

Data Selection steps

  1. Select the Cancer Type.
  2. Select one or more Cohorts.
  3. Select the Analysis Mode.
  4. If using signature mode, choose the scoring method and signature set.
  5. If using TME mode, choose the deconvolution algorithm.
  6. Click Submit.
  7. Review the loaded dataset in the Data tab.

TME Cluster

After data loading, the workflow can perform unsupervised clustering using selected numeric features.

Parameters

  • Features (features) — one or more numeric variables used for clustering

  • Min Clusters (min_nc) — minimum number of clusters evaluated

    • Example: 2

  • Max Clusters (max.nc) — maximum number of clusters evaluated

    • Example: 6

TME Cluster steps

  1. Open the TME Cluster tab.
  2. Select one or more clustering Features.
  3. Set Min Clusters and Max Clusters.
  4. Click Run Analysis.
  5. Review the cluster assignment table and cluster summary.

How downstream modules use the prepared data

After data preparation, the workflow uses the loaded cohort data as the shared input table for downstream analyses.

If clustering is performed, the workflow merges the cluster assignment back into the main dataset and adds a Cluster column for reuse in subsequent modules.

Part 2 · Visualization

This part provides visualization modules for the prepared cancer cohort dataset.

Included modules:

  • Heatmap — visualize selected signatures, scores, or TME-related features across groups
  • Box Plot — compare one selected numeric feature across categorical groups
  • Cell Bar Plot — display cell composition or deconvolution results across samples

Part 2 steps

  1. Open one of the visualization tabs.
  2. Adjust module-specific parameters as needed.
  3. Click Run Analysis in the selected module.
  4. Review the generated plot.

Part 3 · Correlation

This part provides correlation-based analyses for the prepared cancer cohort dataset.

Included modules:

  • Batch Correlation — correlate one selected target with multiple features
  • Partial Correlation — correlate variables while adjusting for a control variable
  • Single Correlation — visualize correlation between two selected variables
  • Correlation Matrix — compute and display correlation matrices between feature sets

Part 3 steps

  1. Open one of the correlation tabs.
  2. Select the required variables or feature sets.
  3. Adjust module-specific parameters as needed.
  4. Click Run Analysis in the selected module.
  5. Review the resulting table or plot.

Part 4 · Group Comparison

This part provides statistical group-comparison analyses for the prepared cancer cohort dataset.

Included modules:

  • Wilcoxon Test — compare numeric variables between two groups
  • Kruskal Test — compare numeric variables across multiple groups
  • Heatmap — visualize selected group-associated variables
  • Box Plot — visualize group differences for one selected variable

Part 4 steps

  1. Open one of the group-comparison tabs.
  2. Select the grouping variable and analysis features.
  3. Adjust module-specific parameters as needed.
  4. Click Run Analysis in the selected module.
  5. Review the resulting table or plot.

Output

The workflow returns a prepared cancer cohort dataset that can be reused across multiple downstream modules, including:

  • selected signature score matrix or TME deconvolution matrix
  • sample annotation columns such as CancerType, Dataset, and ID
  • optional cluster annotation from TME Cluster
  • downstream plots and result tables generated in each part

Download

  • Tables generated in downstream modules can be exported from their corresponding Download panels.
  • Plots generated in downstream modules can be exported from their corresponding Download panels.
  • An initial plot size is provided, which can be adjusted if needed.
  • If needed, you can adjust the plot width and height before downloading to obtain a more suitable layout.

3.3 Immunotherapy Cohorts

Database-driven workflow for immunotherapy cohort analysis, including data selection, signature scoring or TME deconvolution, clustering, visualization, correlation analysis, and group comparison.

This workflow loads processed immunotherapy cohort data from the internal database, filters datasets by cancer type, treatment, drug, and timepoint, and passes the prepared dataset into multiple downstream analysis modules.

Overview

The workflow is organized into four parts:

  • Part 1 · Data Preparation
    • Data Selection
    • TME Cluster
  • Part 2 · Visualization
    • Heatmap
    • Box Plot
    • Cell Bar Plot
  • Part 3 · Correlation
    • Batch Correlation
    • Partial Correlation
    • Single Correlation
    • Correlation Matrix
  • Part 4 · Group Comparison
    • Wilcoxon Test
    • Kruskal Test
    • Heatmap
    • Box Plot

Data source

The workflow queries processed immunotherapy cohort data from the database and subsets samples according to metadata-defined cohort annotations.

The loaded table typically includes:

  • Dataset
  • ID
  • selected signature score columns or TME deconvolution columns, depending on the selected analysis mode

After clustering, an additional Cluster column can be appended and reused in downstream modules.

Part 1 · Data Selection

This part prepares the input dataset for all downstream analyses.

Parameters

  • Cancer Type (sel_cancer) — cancer type used to filter available immunotherapy datasets
    • Dynamically populated from cohort metadata
  • Treatment (sel_treatment) — one or more treatment categories under the selected cancer type
    • Dynamically updated after selecting cancer type
  • Drug (sel_drug) — one or more drugs under the selected filtering condition
    • Dynamically updated after selecting cancer type and treatment
  • Timepoint (sel_time) — one or more sampling timepoints under the selected filtering condition
    • Dynamically updated after selecting cancer type, treatment, and drug
  • Confirm Datasets (sel_datasets) — one or more final datasets to include in analysis
    • Dynamically updated after applying all filters
  • Analysis Mode (data_type)
    • Calculate Sigscore
    • Deconvolute TME

If Analysis Mode = Calculate Sigscore

  • Method (calculate_sig_score_method)
    • PCA
    • ssGSEA
    • Z-score

If Analysis Mode = Deconvolute TME

  • Method (deconvo_tme_method)
    • CIBERSORT
    • CIBERSORT(abs)
    • EPIC
    • quanTIseq
    • ESTIMATE
    • TIMER
    • MCPcounter
    • IPS
    • xCell
    • Integration

Data Selection steps

  1. Select the Cancer Type.
  2. Select one or more Treatment categories.
  3. Select one or more Drug options.
  4. Select one or more Timepoint values.
  5. Confirm one or more datasets in Confirm Datasets.
  6. Select the Analysis Mode.
  7. If using signature mode, choose the calculation Method.
  8. If using TME mode, choose the deconvolution Method.
  9. Click Submit.
  10. Review the loaded dataset in the Data tab.

Part 1 · TME Cluster

After data loading, the workflow can perform unsupervised clustering using selected numeric features.

Parameters

  • Features (features) — one or more numeric variables used for clustering

  • Min Clusters (min_nc) — minimum number of clusters evaluated

    • Example: 2

  • Max Clusters (max.nc) — maximum number of clusters evaluated

    • Example: 6

TME Cluster steps

  1. Open the TME Cluster tab.
  2. Select one or more clustering Features.
  3. Set Min Clusters and Max Clusters.
  4. Click Run Analysis.
  5. Review the cluster assignment table and cluster summary.

How downstream modules use the prepared data

After data preparation, the workflow uses the loaded immunotherapy cohort data as the shared input table for downstream analyses.

If clustering is performed, the workflow merges the cluster assignment back into the main dataset and adds a Cluster column for reuse in subsequent modules.

Part 2 · Visualization

This part provides visualization modules for the prepared immunotherapy cohort dataset.

Included modules:

  • Heatmap — visualize selected signatures, scores, or TME-related features across groups
  • Box Plot — compare one selected numeric feature across categorical groups
  • Cell Bar Plot — display cell composition or deconvolution results across samples

Part 2 steps

  1. Open one of the visualization tabs.
  2. Adjust module-specific parameters as needed.
  3. Click Run Analysis in the selected module.
  4. Review the generated plot.

Part 3 · Correlation

This part provides correlation-based analyses for the prepared immunotherapy cohort dataset.

Included modules:

  • Batch Correlation — correlate one selected target with multiple features
  • Partial Correlation — correlate variables while adjusting for a control variable
  • Single Correlation — visualize correlation between two selected variables
  • Correlation Matrix — compute and display correlation matrices between feature sets

Part 3 steps

  1. Open one of the correlation tabs.
  2. Select the required variables or feature sets.
  3. Adjust module-specific parameters as needed.
  4. Click Run Analysis in the selected module.
  5. Review the resulting table or plot.

Part 4 · Group Comparison

This part provides statistical group-comparison analyses for the prepared immunotherapy cohort dataset.

Included modules:

  • Wilcoxon Test — compare numeric variables between two groups
  • Kruskal Test — compare numeric variables across multiple groups
  • Heatmap — visualize selected group-associated variables
  • Box Plot — visualize group differences for one selected variable

Part 4 steps

  1. Open one of the group-comparison tabs.
  2. Select the grouping variable and analysis features.
  3. Adjust module-specific parameters as needed.
  4. Click Run Analysis in the selected module.
  5. Review the resulting table or plot.

Output

The workflow returns a prepared immunotherapy cohort dataset that can be reused across multiple downstream modules, including:

  • selected signature score matrix or TME deconvolution matrix
  • dataset annotation columns such as Dataset and ID
  • optional cluster annotation from TME Cluster
  • downstream plots and result tables generated in each part

Download

  • Tables generated in downstream modules can be exported from their corresponding Download panels.
  • Plots generated in downstream modules can be exported from their corresponding Download panels.
  • An initial plot size is provided, which can be adjusted if needed.
  • If needed, you can adjust the plot width and height before downloading to obtain a more suitable layout.

3.4 Other Cohorts

Database-driven workflow for CPTAC and TARGET cohort analysis, including data selection, signature scoring or TME deconvolution, clustering, visualization, survival analysis, correlation analysis, and group comparison.

This workflow loads processed cohort data from the internal database, matches clinical information, and passes the prepared dataset into multiple downstream analysis modules.

Overview

The workflow is organized into five parts:

  • Part 1 · Data Preparation
    • Data Selection
    • TME Cluster
  • Part 2 · Visualization
    • Heatmap
    • Box Plot
    • Percent Bar Plot
    • Cell Bar Plot
  • Part 3 · Survival Analysis
    • Batch Survival
    • Forest Plot
    • Heatmap
    • Survival Plot
    • Survival Group
    • Time ROC
    • Sig ROC
  • Part 4 · Correlation
    • Batch Correlation
    • Partial Correlation
    • Single Correlation
    • Correlation Matrix
  • Part 5 · Group Comparison
    • Wilcoxon Test
    • Kruskal Test
    • Heatmap
    • Box Plot

Data source

The workflow queries processed CPTAC or TARGET cohort data from the internal database and returns two matched tables:

  • Expression / Score Data
    • Signature score matrix or TME deconvolution matrix, depending on the selected mode
  • Clinical Data
    • Matched phenotype and clinical annotation table for the selected cohort and cancer type

Part 1 · Data Selection

This step prepares the input dataset for all downstream modules.

Parameters

  • Select Cohort (cohort_selector)
    • CPTAC
    • TARGET
  • Cancer Type (cptac_cancer) when cohort = CPTAC
    • BRCA
    • COAD
    • GBM
    • HNSC
    • KIRC
    • LUAD
    • LUSC
    • OV
    • PAAD
    • UCEC
  • Cancer Type (target_cancer) when cohort = TARGET
    • GNB
    • LAML
    • NBL
  • Analysis Mode (data_type)
    • Calculate Sigscore
    • Deconvolute TME

If Analysis Mode = Calculate Sigscore

  • Method (calculate_sig_score_method)
    • PCA
    • ssGSEA
    • Z-score

If Analysis Mode = Deconvolute TME

  • Method (deconvo_tme_method)
    • CIBERSORT
    • CIBERSORT(abs)
    • EPIC
    • quanTIseq
    • ESTIMATE
    • TIMER
    • MCPcounter
    • IPS
    • Integration

Data Selection steps

  1. Select the Cohort.
  2. Select the corresponding Cancer Type.
  3. Select the Analysis Mode.
  4. If using signature mode, choose the scoring Method.
  5. If using TME mode, choose the deconvolution Method.
  6. Click Submit.
  7. Review the loaded Data and Clinical Data tables.

Part 1 · TME Cluster

After data loading, the workflow can perform unsupervised clustering using selected TME-related or score-related features.

TME Cluster parameters

  • Features (features) — one or more numeric variables used for clustering
  • Min Clusters (min_nc) — minimum number of clusters evaluated
  • Max Clusters (max.nc) — maximum number of clusters evaluated

TME Cluster steps

  1. Open the TME Cluster tab.
  2. Select one or more clustering Features.
  3. Set Min Clusters and Max Clusters.
  4. Click Run Analysis.
  5. Review the cluster assignment table and cluster summary.

How downstream modules use the prepared data

After data preparation, the workflow automatically builds a combined dataset that includes:

  • clinical variables
  • selected cohort signature or TME variables
  • optional cluster assignment from TME Cluster

This combined table is then used as the shared input for downstream modules in Parts 2–5.

Part 2 · Visualization

This section provides direct plotting modules for the prepared cohort dataset:

  • Heatmap — visualize selected signatures or scores across groups
  • Box Plot — compare one signature across categorical groups
  • Percent Bar Plot — display proportions of categorical annotations
  • Cell Bar Plot — show deconvolution-based cell composition across samples

Part 3 · Survival Analysis

This section provides survival-related modules using matched clinical data:

  • Batch Survival — screen multiple variables by Cox analysis
  • Forest Plot — visualize hazard ratios from batch survival results
  • Heatmap — display selected survival-associated variables
  • Survival Plot — Kaplan–Meier curves for a selected signature
  • Survival Group — Kaplan–Meier curves for a categorical variable
  • Time ROC — time-dependent ROC for prognostic variables
  • Sig ROC — ROC analysis for selected variables against outcome

Part 4 · Correlation

This section provides correlation-based analyses:

  • Batch Correlation — correlate one target with multiple features
  • Partial Correlation — correlate variables while adjusting for a control variable
  • Single Correlation — visualize correlation between two variables
  • Correlation Matrix — compute and plot feature-set correlation matrices

Part 5 · Group Comparison

This section provides statistical comparison modules:

  • Wilcoxon Test — compare numeric variables between two groups
  • Kruskal Test — compare numeric variables across multiple groups
  • Heatmap — visualize selected group-associated variables
  • Box Plot — visualize group differences for one selected variable

Output

The workflow returns a matched cohort dataset that can be reused across multiple downstream modules, including:

  • processed expression or score matrix
  • matched clinical table
  • optional cluster annotation
  • downstream plots and result tables generated in each part

Download

  • Tables generated in downstream modules can be exported from their corresponding Download panels.
  • Plots generated in downstream modules can be exported from their corresponding Download panels.
  • An initial plot size is provided, which can be adjusted if needed.
  • If needed, you can adjust the plot width and height before downloading to obtain a more suitable layout.