Chapter 10 TPC Modules

In the “Custom TPC modules” page, there are totally 10 series of modules to quickly explore TPC (TCGA, PCAWG, CCLE) datasets with easy steps, based on the functions introduced in Chapter 5.

All Custom TPC modules in shiny app

Figure 10.1: All Custom TPC modules in shiny app

10.1 TCGA

Table 10.1: Custom modules for TCGA analysis
Database Type Module
TCGA Comparison Tumor VS Normal (Box plot)
TCGA Comparison Tumor VS Normal (Anatomy plot)
TCGA Comparison Mutation VS Wild
TCGA Correlation Molecule-Molecule
TCGA Correlation Molecule-Tumor Immune Infiltration
TCGA Correlation Molecule-Immune Signature
TCGA Correlation Molecule-TMB/Stemness/MSI
TCGA Correlation Molecule-Pathway
TCGA Survival Kaplan-Meier
TCGA Survival Cox regression
TCGA Dimension Reduction Dimension reduction

10.1.1 Comparison

10.1.1.1 TCGA Tumor VS Normal (Box plot)

Compare molecular values between tumor and normal samples combing TCGA and GTEx datasets based on vis_toil_TvsN() and vis_toil_TvsN_cancer() functions.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select the mode: Pan-cancer or Single-Cancer (For the latter, one tumor type needs to be specified);
  4. Decide that Whether to include normal samples from GTEx;
  5. Click the “Go!” button
  • Right panel
  1. Observe the visualization plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The layout of module "Tumor VS Normal (Box plot)"

Figure 10.2: The layout of module “Tumor VS Normal (Box plot)”

10.1.1.2 TCGA Tumor VS Normal (Anatomy plot)

Observe the difference of molecular values between pan-cancer tumor and normal samples based on vis_pancan_anatomy() functions.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Click the “Go!” button
  • Right panel
  1. Observe the anatomy plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The layout of module "Tumor VS Normal (Anatomy plot)"

Figure 10.3: The layout of module “Tumor VS Normal (Anatomy plot)”

10.1.1.3 TCGA Mutation VS Wild

Compare molecular values between gene-mutant and gene-wild tumor samples based on vis_toil_Mut() and vis_toil_Mut_cancer() functions.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature to be compared;
  3. Select one gene and group according to its mutation status;
  4. Select the mode: Pan-cancer or Single-Cancer (For the latter, one tumor type needs to be specified);
  5. Click the “Go!” button
  • Right panel
  1. Observe the visualization plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Mutation VS Wild"

Figure 10.4: The steps of module “Mutation VS Wild”

10.1.2 Correlation

10.1.2.1 TCGA Molecule-Molecule

Compute and visualize the correlation between two molecules of tumor samples based on vis_gene_cor() and vis_gene_cor_cancer() functions.

  • Left panel
  1. Select two respective omics types;
  2. Select two specific identifiers as X-axis and Y-axis;
  3. Select one or more TCGA types;
  4. Select correlation method;
  5. Decide that whether the correlation is corrected based on tumor purity;
  6. Click the “Go!” button.
  • Right panel
  1. Observe the scatter plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Molecule-Molecule"

Figure 10.5: The steps of module “Molecule-Molecule”

10.1.2.2 TCGA Molecule-Tumor Immune Infiltration

Compute the correlation of pan-cancers between one molecule and tumor immune infiltration of pan-cancer samples based on vis_gene_TIL_cor() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select multiple TIL estimation;
  4. Select correlation method;
  5. Click the “Go!” button.
  • Right panel
  1. Observe the heatmap plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Molecule-Tumor Immune Infiltration"

Figure 10.6: The steps of module “Molecule-Tumor Immune Infiltration”

10.1.2.3 TCGA Molecule-Immune Signature

Compute the correlation of pan-cancers between one molecule and tumor Immune Signature based on vis_gene_immune_cor() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select one category of immune cell type scores;
  4. Select correlation method;
  5. Click the “Go!” button.
  • Right panel
  1. Observe the heatmap plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Molecule-Immune Signature"

Figure 10.7: The steps of module “Molecule-Immune Signature”

10.1.2.4 TCGA Molecule-TMB/Stemness/MSI

Compute the correlation of pan-cancers between one molecule and TMB/Stemness/MSI index based on vis_gene_tmb_cor(), vis_gene_msi_cor() and vis_gene_stemness_cor() functions.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select one of collected tumor index;
  4. Select correlation method;
  5. Click the “Go!” button.
  • Right panel
  1. Observe the radar plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Molecule-TMB/Stemness/MSI"

Figure 10.8: The steps of module “Molecule-TMB/Stemness/MSI”

10.1.2.5 TCGA Molecule-Pathway

Compute the correlation of between one molecule and pathway score of tumor samples based on vis_gene_pw_cor() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select one of calculated pathway scores (ssGSEA);
  4. Select correlation method;
  5. Click the “Go!” button.
  • Right panel
  1. Observe the scatter plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Molecule-Pathway"

Figure 10.9: The steps of module “Molecule-Pathway”

10.1.3 Survival analysis

10.1.3.1 TCGA Kaplan-Meier

Perform one molecular Log-rank survival analysis based on tcga_surv_plot() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select one TCGA cancer and select one endpoint as survival event;
  4. Filter samples by Gender, Age, Stage;
  5. Select the grouping method;
  6. Click the “Go!” button.
  • Right panel
  1. Observe the survival curves of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Kaplan-Meier"

Figure 10.10: The steps of module “Kaplan-Meier”

10.1.3.2 TCGA Cox regression

Perform one molecular Log-rank survival analysis across pan-cancers based on vis_unicox_tree() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select one endpoint as survival event;
  4. Click the “Go!” button.
  • Right panel
  1. Observe the forest plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Cox regression"

Figure 10.11: The steps of module “Cox regression”

10.1.4 Dimension Reduction

10.1.4.1 TCGA Dimension reduction

Perform molecular dimension reduction analysis based on vis_dim_dist() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select multiple identifiers via one of three ways, then click the “Cache data” button;
    • “Select”: One-by-one selection;
    • “Pathway”: batch selection under one pathway;
    • “File”: Upload of identifier file.
  3. Set the sample range and grouping;
    • “Preset Group”: select one or more TCGA cancers and one clinical phenotype as grouping criteria.
    • “Custom Group”: upload files with custom samples and group columns. See details by downloading example file.
  4. Select the algorithm of dimension reduction;
  5. Click the “Go!” button.
  • Right panel
  1. Observe the scatter plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Dimension reduction"

Figure 10.12: The steps of module “Dimension reduction”

10.2 PCAWG

Table 10.2: Custom modules for PCAWG analysis
Database Type Module
PCAWG Comparison Tumor VS Normal
PCAWG Correlation Molecule-Molecule
PCAWG Survival Kaplan-Meier
PCAWG Survival Cox regression

10.2.1 Comparison analysis

10.2.1.1 PCAWG Tumor VS Normal

Compare molecular values between tumor and normal samples of PCAWG projects based on vis_pcawg_dist() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Click the “Go!” button.
  • Right panel
  1. Observe the visualization plot of analytical result (Some of projects only have tumor samples);
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Tumor VS Normal"

Figure 10.13: The steps of module “Tumor VS Normal”

10.2.2 Correlation analysis

10.2.2.1 PCAWG Molecule-Molecule

Compute and visualize the correlation between two molecules based on vis_pcawg_gene_cor() function.

  • Left panel
  1. Select two respective omics types;
  2. Select two specific identifiers as X-axis and Y-axis;
  3. Select one or more PCAWG projects;
  4. Select correlation method;
  5. Decide that whether the correlation is corrected based on tumor purity;
  6. Click the “Go!” button.
  • Right panel
  1. Observe the scatter plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
    The steps of module "Molecule-Molecule"

    Figure 10.14: The steps of module “Molecule-Molecule”

10.2.3 Survival analysis

Notes: Only OS endpoint for PCAWG samples.

10.2.3.1 PCAWG Kaplan-Meier

Perform one molecular Log-rank survival analysis in one PCAWG project.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Select one PCAWG project;
  4. Filter samples by Gender, Age;
  5. Select the grouping method;
  6. Click the “Go!” button.
  • Right panel
  1. Observe the survival curves of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Kaplan-Meier"

Figure 10.15: The steps of module “Kaplan-Meier”

10.2.3.2 PCAWG Cox regression

Perform one molecular Log-rank survival analysis in All PCAWG projects based on vis_pcawg_unicox_tree() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Click the “Go!” button.
  • Right panel
  1. Observe the forest plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Cox regression"

Figure 10.16: The steps of module “Cox regression”

10.3 CCLE

Table 10.3: Custom modules for CCLE analysis
Database Type Module
CCLE Comparison Cancer Primary Sites
CCLE Correlation Molecule-Molecule

10.3.1 Comparison analysis

10.3.1.1 CCLE Cancer Primary Sites

Compare molecular values of cancer cell lines from different primary site based on vis_ccle_tpm() function.

  • Left panel
  1. Select one of supported multi-omics types;
  2. Select an identifier or define a signature;
  3. Click the “Go!” button.
  • Right panel
  1. Observe the visualization plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Cancer Primary Sites"

Figure 10.17: The steps of module “Cancer Primary Sites”

10.3.2 Correlation analysis

10.3.2.1 CCLE Molecule-Molecule

Compute and visualize the correlation between two molecules based on CCLE database based on vis_ccle_gene_cor() function.

  • Left panel
  1. Select two respective omics types;
  2. Select two specific identifiers as X-axis and Y-axis;
  3. Select one or more primary sites;
  4. Select correlation method;
  5. Click the “Go!” button.
  • Right panel
  1. Observe the scatter plot of analytical result;
  2. Pull the sidebar to adjust plot parameters or download results through the top-right widget.
The steps of module "Molecule-Molecule"

Figure 10.18: The steps of module “Molecule-Molecule”