Step 1 — Download Data

Sidebar: 1. Download Data

Goal: Download GEO series, map row IDs to gene symbols, and build a combined genes × samples matrix for downstream steps.

What to do:

1. Optionally enter a Disease / Condition label (used in reports and saved workspace names).
2. Under Select Analysis Platform, choose RNA-seq, Microarray, or Merged (Both).
3. Choose Datasets mode:
   • Single dataset (1 GSE) — Step 5 (batch correction) is skipped.
   • Multiple datasets (comma-separated) — batch correction is recommended in Step 5.
4. Select the DE method for Step 6 (this also controls Step 3 behaviour):
   • limma — microarray, merged, or normalized log-expression.
   • limma-voom, DESeq2, or edgeR — RNA-seq integer counts.
5. Enter comma-separated GSE IDs (e.g. GSE50760, GSE104836).
6. Click Start Processing and read the Download log.
7. Click Next: QC & Visualization.

For multiple microarray platforms, install org.Hs.eg.db and biomaRt (see Installation) so STEP 2b converts probe IDs to HGNC symbols before merging.

Success check: Download log lists samples per GSE; STEP 2b runs when needed; row names are symbols (TP53, BRCA1, …); Common genes > 0.

Step 1: Select platform, dataset mode, DE method, and GSE IDs.

Step 2 — QC & Visualization

Sidebar: 2. QC & Visualization

Goal: Check gene overlap, inspect raw expression, and detect outlier samples before normalization.

What to do:

1. Review Venn and UpSet plots (figure below, panel a).
2. Open Quality Control Plots — Boxplot and Density tabs on raw expression (panel b).
3. In Sample Outlier Detection, click Run Outlier Detection (panel c):
   • PCA + Mahalanobis distance (97.5% chi-squared threshold).
   • Sample connectivity (signed network; mean − 2×SD cutoff).
4. Exclude flagged samples if needed; keep ≥ 3 samples per group after Step 4.
5. Review Data Summary (datasets, samples, genes).
6. Proceed to Step 3.

Success check: Overlap and QC plots look reasonable; outliers reviewed.

Step 2a: Venn and UpSet gene overlap.

Step 2b: Raw expression boxplot.

Step 2c: PCA and connectivity outlier detection.

Step 3 — Normalize Data

Sidebar: 3. Normalize Data

Goal: Make expression values comparable across samples and datasets.

What to do:

Count-based DE (DESeq2 / edgeR / limma-voom from Step 1): the panel shows Normalization Auto-Handled — proceed with Go to Step 4: Select Groups. Background TMM + quantile still runs for WGCNA and heatmaps.

limma path (microarray or merged):

1. Choose Auto or Manual under Normalization Strategy.
2. Microarray: Quantile or RMA; RNA-seq: TMM + log2-CPM.
3. Toggle Apply global quantile normalization if appropriate.
4. Click Apply Normalization.
5. Scroll to Normalization Quality Assessment (panels a and b below):
   • Overall density and Q–Q plot (panel a).
   • Median/range alignment and distribution overlap (panel b).
6. Click Next: Select Groups.

Success check: Q–Q points follow the diagonal; sample medians align.

Step 3a: Overall density and Q-Q plots after normalization.

Step 3b: Median/range alignment and distribution overlap.

Step 4 — Select Groups

Sidebar: 4. Select Groups

Goal: Assign each sample to Normal, Disease, or Exclude.

What to do:

1. Browse Phenodata Browser for each GSE.
2. Under Select Phenotype Columns (panel a), pick the condition column per dataset (e.g. characteristics_ch1, or title when treatment is only in sample titles).
3. Click Extract Groups from Selected Columns.
4. Categorize Groups — map labels to reference (Normal) or comparison (Disease), or None.
5. Click Apply Groups.
6. In Group Summary (panel b), optionally rename groups (e.g. Control / Treated); click Update, or leave as Normal / Disease and continue.
7. Confirm sample counts, then click Next: Batch Correction.

Success check: Group Summary counts match your study design.

Step 4a: Select phenotype column per GSE.

Step 4b: Group Summary after Apply Categorization.

Step 5 — Batch Correction

Sidebar: 5. Batch Correction

Goal: Remove technical variation between datasets while keeping biological Normal vs Disease signal.

What to do:

1. Read Dataset × Condition confounding — if dataset and condition are confounded, use limma or SVA instead of ComBat.
2. Review Gene variance distribution.
3. Set Variance percentile cutoff (panel a, default 25%).
4. Choose Batch correction method (panel a, default ComBat-ref): ComBat-ref, SVA, limma, ComBat, Quantile + limma, or Hybrid.
5. Click Apply Batch Correction.
6. Compare before vs after PCA by dataset (panel b), plus PCA by condition, hierarchical clustering, and PVCA on the same panel.
7. Click Next: Differential Expression.

Note: Step 5 is skipped when only one GSE was loaded in Step 1.

Success check: After-correction PCA (panel b) shows datasets intermingled.

Step 5a: Variance filtering and batch correction method.

Step 5b: PCA by dataset before and after batch correction.

Step 6 — Differential Expression Analysis

Sidebar: 6. Differential Expression Analysis

Goal: Identify genes differing between Normal and Disease.

What to do:

1. Confirm the DE method banner matches your Step 1 choice (limma, limma-voom, DESeq2, or edgeR).
2. Set DE Parameters (panel a):
   • LogFC cutoff (default 0.5; e.g. 1.0 for 2-fold change).
   • Adj. P-value (default 0.05, Benjamini–Hochberg FDR).
   • Heatmap Genes (number of top DEGs for the heatmap, default 50).
3. Click Run DE Analysis.
4. Read Statistical model and Pipeline verification — design formula, filterByExpr gene counts, samples used, and batch-in-model note.
5. Review results (panel b):
   • Volcano plot (up / down / not significant).
   • Top DEGs table and heatmap below.
6. Optionally download Analysis report (TXT).
7. Click Next: WGCNA Analysis.

Success check: Volcano shows expected direction; DEG count is plausible for your contrast; pipeline verification lists the correct method and samples.

Step 6a: DE parameters, statistical model, and pipeline verification.

Step 6b: Volcano plot and top DEG table.

Step 7 — WGCNA Network Analysis

Sidebar: 7. WGCNA Analysis

Goal: Find co-expression modules associated with disease and extract hub genes from significant modules.

What to do:

The WGCNA tab has internal sub-steps; complete them in order:

1. Data Preparation & QC (panel a):
   • Choose All genes or Top variable genes (e.g. 5000).
   • Set Min samples per gene fraction (default 0.5).
   • Click Prepare Data.
   • Check the sample dendrogram; exclude outliers or click No outliers — proceed to Step 2 (within this tab).
2. Pick Soft Threshold Power (panel b):
   • Click Calculate Power; choose power where scale-free R² ≥ 0.80.
3. Network Construction & Module Detection (panel c):
   • Set minimum module size and merge cut height.
   • Click Build Network; inspect the module dendrogram and module colours.
4. Module–trait relationships (panel c):
   • Click Calculate Correlations & GS/MM; review the heatmap of module–condition correlations.
5. Identify significant modules (panel d):
   • Click Identify Significant Modules; note hub genes (GS/MM filters).
   • Optionally run GS vs MM plots for all modules.
6. Click Next: Common Genes (DEG & WGCNA).

Success check: Soft-threshold R² ≥ 0.80; at least one module correlates with Disease; hub genes are listed for significant modules.

Step 7a: WGCNA data preparation and gene selection.

Step 7b: Soft-threshold power selection.

Step 7c: Module dendrogram and module-trait heatmap.

Step 7d: Significant modules and hub gene summary.

Step 8 — Common Genes (DEG & WGCNA) & Enrichment

Sidebar: 8. Common Genes (DEG & WGCNA)

Goal: Intersect DEGs (Step 6) with genes in significant WGCNA modules (Step 7), then run GO/KEGG enrichment on the overlap.

What to do:

1. Click Compute Common Genes (panel a).
2. Review the summary counts and Venn diagram: DEG ∩ WGCNA.
3. Download the common-gene table (CSV) if needed.
4. Click Run GO Enrichment (panel b) on the common genes — dot plots for BP, MF, and CC.
5. Click Run KEGG Enrichment (panel c) — bar plot and chord diagram.
6. Choose next path at the bottom of the tab:
   • Path 1: PPI Interaction → then ML, or
   • Path 2: Direct to Machine Learning.

Requirements: Steps 6 and 7 (including Identify Significant Modules) must be complete.

Success check: Venn overlap > 0; GO/KEGG plots show interpretable terms.

Step 8a: Common genes Venn diagram (DEG intersect WGCNA).

Step 8b: GO enrichment of common genes.

Step 8c: KEGG enrichment of common genes.

Step 9 — PPI Interaction

Sidebar: 9. PPI Interaction

Goal: Map common genes onto a STRING protein–protein interaction network and identify hub proteins.

What to do:

1. Set STRING score threshold (default 400; 150–900).
2. Click Run PPI Analysis (panel a).
3. Review Interactive vs non-interactive gene counts and table.
4. Choose genes for network view: Hub genes only, Top N by degree, or Select genes manually; click Run.
5. Inspect PPI network layouts (panel b) and consensus hub table.
6. Click Extract Data for ML (required before Step 10).
7. Click Next: Machine Learning Process.

Requirements: Step 8 common genes computed.

Success check: Network builds with mapped edges; hub genes listed; ML extract succeeds.

Step 9a: Run PPI and interactive vs non-interactive gene table.

Step 9b: PPI network graph and hub gene metrics.

Step 10 — Machine Learning Process

Sidebar: 10. Machine Learning Process

Goal: Rank genes by predictive importance using multiple ML algorithms and find genes selected by ≥ 2 methods.

What to do:

1. Confirm Extract Data for ML was run on the PPI tab (Step 9).
2. Under Run ML Analysis (panel a), select one or more methods: LASSO, Elastic Net, Ridge, Random Forest, SVM-RFE, Boruta, sPLS-DA, XGBoost+SHAP.
3. Set Top N per method where shown.
4. Click Run ML Analysis.
5. Review Venn diagram (selected methods) and the common ML genes table (panel b) — genes in ≥ 2 methods form the candidate panel.
6. Inspect per-method importance plots (RF, Elastic Net, etc.).
7. Click Continue to Validation Setup.

Note: If glmnet fails after install, restart R and relaunch the app.

Success check: At least one common ML gene; Venn overlap across methods is non-empty.

Step 10a: Select ML methods and run analysis.

Step 10b: Method overlap Venn diagram and common ML genes.

Step 11 — Validation

Sidebar: 11. Validation Setup

Goal: Test generalization on an external GEO cohort or an internal 70/30 train–test split. Target accuracy ≥ 70%.

What to do:

1. Choose Validation Strategy (panel a):
   • External Validation — download an independent GSE, categorize groups, and run DE on the validation cohort.
   • Internal Validation — use a 70/30 stratified split of your current data (no extra download).
2. External path only:
   • Enter validation GSE IDs, select Platform Type and DE Method.
   • Click Download, then browse phenodata and select the group column.
   • Categorize labels as Normal / Disease and click Run Validation DE.
3. Review validation status and DE results (panel b).
4. Click Continue to ROC Curve Analysis.

Requirements: Step 10 ML analysis complete with at least one common ML gene.

Success check: External mode shows downloaded samples and validation DE results; internal mode confirms the 70/30 split is ready for ROC and nomogram.

Step 11a: Validation mode and external validation settings.

Step 11b: External validation DE results.

Step 12 — ROC Analysis

Sidebar: 12. ROC Curve Analysis

Goal: Per-gene AUC with 95% CI (pROC). Retain genes with AUC ≥ 0.80 in training and validation.

What to do:

1. Review AUC Summary — Training Data (panel a): table and bar plot for common ML genes with AUC ≥ 0.80.
2. Inspect ROC Curves — Training Data and per-gene expression boxplots.
3. If external validation was run in Step 11, compare training vs validation AUC in the summary and external ROC panels (panel b).
4. Review Multi-variable ROC (combined biomarker panel vs best single gene).
5. Under Select Final Biomarker Genes, choose genes to carry forward to nomogram and GSEA (defaults to all common ML genes if none selected).
6. Click Continue to Diagnostic Nomogram, Continue to GSEA Analysis, or View Results Summary.

Requirements: Step 10 common ML genes; Step 11 validation mode selected.

Success check: At least one gene with training AUC ≥ 0.80; validation AUC reported when external data are loaded.

Step 12a: Training ROC curves and AUC summary.

Step 12b: Training vs validation AUC comparison.

Step 13 — Nomogram

Sidebar: 13. Diagnostic Nomogram Model

Goal: Build a points-based clinical score (rms::lrm()), calibration curve, and decision curve analysis.

What to do:

1. Confirm the validation mode banner (external or internal 70/30 split).
2. Click Run Nomogram Analysis.
3. Review outputs in order:
   • Panel A: nomogram (points scale).
   • Panel B: training and validation ROC curves.
   • Panel C: calibration curves.
   • Panel D: decision curve analysis (DCA).
   • Panel E: clinical impact curves.
4. Inspect Model Diagnostics (VIF, coefficients) and Performance Comparison (training vs validation).
5. Click Continue to GSEA Analysis or View Results Summary.

Requirements: Batch correction (Step 5), group metadata, and common ML genes from Step 10 (or common DEG ∩ WGCNA genes from Step 8).

Success check: Nomogram renders; training AUC is reasonable; validation metrics match your chosen validation strategy.

Step 13: Nomogram, calibration, and decision curves.

Step 14 — GSEA

Sidebar: 14. GSEA Analysis (Signature Genes)

Goal: Pathway-level enrichment (GO, KEGG, MSigDB Hallmark/C2/C5) stratified by final gene expression.

What to do:

1. Select one or more Gene Set Collection(s) (Hallmark, GO BP/MF, KEGG, Reactome, etc.).
2. Optionally enter Custom target genes; leave blank to use common ML genes (or genes selected in Step 12).
3. Click Run GSEA.
4. For each target gene, review the enrichment plot and pathway list in the per-gene panels below.
5. Click Continue to Results Summary.

Requirements: Batch-corrected expression and common ML genes from Step 10 (or Step 8 overlap).

Success check: GSEA completes for each target gene; enrichment plots and pathway tables are interpretable.

Step 14: GSEA enrichment plots per signature gene.

Step 15 — Summary Report

Sidebar: Results Summary

Goal: Review the full pipeline narrative and key figures, then download exports where available.

What to do:

1. Read the Pipeline summary narrative at the top.
2. Scroll through step cards for normalization, batch correction, DE, WGCNA, common genes, GO/KEGG, PPI, ML, ROC, nomogram, and GSEA — each with a short description and figure.
3. Use download buttons on individual plots (JPG/PDF) where you need figures for a manuscript or report.
4. Expand Cite this analysis for package and method references.

Success check: Summary reflects your completed steps; key plots from earlier tabs appear in the ordered overview.

Step 15: Results summary overview.