Analysis methods
A catalogue of the analyses K-Pro can run on your data, the statistical methods behind them, and which datasets support each modality.
K-Pro runs published, peer-reviewed statistical methods on patient-level data across seven modalities: bulk RNA sequencing, whole exome sequencing (WES), single-cell RNA sequencing, spatial transcriptomics, H&E histomics, clinical variables, and multimodal combinations of these.
This page catalogues what each analysis does and when to use it. For the plots these analyses produce — with parameters, screenshots, and example prompts — see Example: Visualisations by modalities.
Bulk RNA sequencing
Bulk RNA-seq measures average gene expression across all cells in a sample, giving a robust and cost-effective basis for transcriptomic profiling, biomarker discovery, and molecular subtyping.
Differential expression
Differential expression (DESeq2): identifies genes expressed differently between two clinical groups using the DESeq2 negative binomial model. Supports covariate adjustment for confounders such as age, gender, or tumour stage.
Differential expression (t-test): a modality-agnostic alternative using Welch's t-test, suitable for normalized expression data or when DESeq2 assumptions do not hold.
Survival and prognosis
Survival analysis (Cox): Cox proportional hazards modelling of the effect of gene expression on overall survival (OS) or progression-free survival (PFS). Supports univariate models, multivariate models combining genes with clinical covariates, and interaction analyses across expression-defined subgroups.
Kaplan-Meier analysis: survival curves stratified by expression level (for example high vs. low on a median cutoff) or another grouping variable.
Covariate-adjusted survival: Kaplan-Meier-style curves that control for clinical covariates, isolating the independent effect of expression on survival.
Pathways and signatures
ssGSEA enrichment: single-sample gene set enrichment, scoring each sample for pathway or signature activity so groups can be compared without a predefined contrast.
GSEA (preranked): gene set enrichment on a ranked gene list, such as the output of a differential expression analysis, returning a normalized enrichment score per pathway.
Gene signature scoring: custom signature scores across samples, for comparing biological programs such as immune signatures, metabolic pathways, or stemness scores.
Clustering and subtyping
Consensus agglomerative clustering: discovers patient subgroups from expression features, using bootstrap aggregation for robust subtype identification.
Feature extraction: identifies the minimal gene set that distinguishes subgroups or predicts an outcome, via Orthogonal Matching Pursuit or elastic-net stability selection. Reports per-gene selection frequency alongside ROC-AUC, accuracy, and F1.
Complex heatmap: clustered gene-panel heatmaps annotated with clinical variables, mutation status, or other categorical tracks.
Expression distributions
Single-gene, multi-gene, and signature expression: distributions across patient groups, for one gene, a panel, or a signature.
Expression variability: average variability of genes across groups, identifying those with high inter-patient heterogeneity.
Correlation analysis: pairwise Pearson or Spearman correlation between genes, proteins, or signatures, revealing co-expression and regulatory relationships.
Scatter/UMAP: sample embeddings via UMAP or PCA, coloured by metadata or continuous expression.
Whole exome sequencing (WES)
WES captures protein-coding regions, enabling identification of somatic mutations and copy number variations (CNVs).
Mutation frequency analysis: per-position mutation frequency across a cohort, identifying hotspots within a gene. Supports variant allele frequency (VAF) display per patient, distinguishing clonal from subclonal mutations.
Oncoprint analysis: a genes × samples alteration matrix showing mutations and CNVs across a gene panel, characterising the mutational landscape and revealing co-occurrence and mutual exclusivity.
Alteration co-occurrence: co-occurrence of alterations (for example TP53 and ERBB2), stratified by clinical variables such as gender or stage.
Survival with genomic stratification: mutation status against OS or PFS via Cox models or Kaplan-Meier curves, with multivariate adjustment for clinical covariates.
Expression-alteration integration: expression heatmaps annotated by WES-derived CNV status, showing how genomic alterations track with transcriptional change.
Single-cell RNA sequencing
Single-cell RNA-seq resolves cellular heterogeneity, revealing cell states, rare populations, and intercellular communication.
Sparse aggregation: percent-expressing cells and mean expression per group for a gene panel across cell types — the basis for single-cell dotplots.
Proportion analysis: cell-type composition per sample, showing how populations vary across patient groups or conditions.
Cell-cell communication: ligand-receptor signalling between cell types, inferred with LIANA and supporting CellChat, CellPhoneDB, and NATMI scoring.
Co-expression analysis: Jaccard-index overlap between two genes or signatures, quantifying the fraction of cells expressing only A, only B, or both.
Sparse correlation: pairwise Pearson or Spearman correlation between features, optionally stratified by cell type, to find co-regulated modules.
UMAP visualisation: single-cell embeddings coloured by metadata or continuous expression.
Pseudobulk differential expression: aggregates single-cell counts to patient-level profiles, then runs DESeq2 between clinical groups — across all cells or within one cell type.
InferCNV: infers copy-number variation from single-cell expression, clustering CNV profiles into clones via Leiden clustering.
Single-cell enrichment (ssGSEA): pathway and signature activity scored per cell or per pseudobulk sample.
Consensus clustering: patient subgroups from aggregated single-cell features.
Spatial transcriptomics
Spatial transcriptomics preserves tissue architecture while measuring expression, enabling study of cellular niches and the tumour microenvironment in situ.
Spatial slide display: Visium spots on tissue sections, coloured by metadata, gene expression, deconvolution results, or colocalization.
Spatial deconvolution: cell-type composition estimated per spot, displayed as pie charts overlaid on the tissue.
Gene-gene colocalization: spatial co-occurrence of two genes, identifying regions of coordinated expression.
Gene-cell type colocalization: where a gene's expression spatially overlaps deconvolved cell-type abundance.
Spatial InferCNV: copy-number variation inferred by comparing tumour against normal regions defined by spatial annotation.
Sparse aggregation, proportion, co-expression, and correlation: as for single-cell, computed per spatial region or tissue compartment.
Single-cell compared with spatial
Resolution
Individual cells
~1-10 cells per spot
Tissue context
Dissociated (lost)
Preserved architecture
Key insight
Cell identity and states
Spatial organisation and niches
Deconvolution
Not required
Required for cell-type inference
Unique analyses
Trajectory inference
Colocalization, region comparison
H&E histomics
Histomics applies deep learning to whole-slide images, extracting quantitative features from standard pathology slides.
Cell-type density proportions: proportions of AI-detected cell densities — tumour cells, lymphocytes, fibroblasts, macrophages — compared across clinical groups.
Aggregation analysis: distributions of histomics features (cell densities, morphological metrics, spatial features) across groups.
Correlation analysis: between histomics features, or between histomics and molecular features, linking morphological and molecular phenotypes.
Survival analysis: Cox modelling and Kaplan-Meier curves stratified by histomics features such as high vs. low tumour-infiltrating lymphocyte (TIL) density.
Multivariate prognostic models: histomics features combined with clinical variables and molecular markers, assessing independent prognostic value.
Complex heatmap: clustered heatmaps of histomics features annotated by clinical variables or molecular subtypes.
Clustering and feature extraction: morphologically-defined subtypes, and minimal interpretable feature panels.
Feature categories
Cell densities
Tumour cell, lymphocyte, fibroblast, macrophage density
Cell proportions
TIL proportion, stromal proportion, tumour proportion
Morphological features
Nuclear size, nuclear shape, pleomorphism
Spatial features
Cell clustering, spatial heterogeneity, tumour-stroma interface
Tissue architecture
Gland formation, necrosis fraction, inflammatory infiltrate patterns
Pathology Explorer
A dedicated module adds slide-level and tile-level capabilities: thumbnail display and an interactive pan-and-zoom viewer; filtering slides or tiles by any histomics feature; cohort feature statistics; AI model predictions overlaid on tiles; Kaplan-Meier analysis stratified by histomics feature; and export to Parquet.
Clinical variables
Clinical data gives molecular findings their context, supporting survival modelling, cohort characterisation, and treatment response analysis.
Clinico-pathological characterization: descriptive "Table 1" statistics — mean (SD) or median [IQR] for continuous variables, n (%) for categorical — with between-group hypothesis testing.
Survival and Kaplan-Meier analysis: Cox modelling and stratified curves for clinical variables such as stage, treatment, or biomarker status.
Timeline analysis: per-patient treatment histories, including therapy sequences, durations, and responses.
Treatment flow diagrams: patient flow across treatment sequences, such as 1L to 2L to 3L transitions.
Trajectory analysis: longitudinal measurements over time, such as tumour size, PSA, or biomarkers.
Population overview: patient count matrices by condition and dataset, for quick availability assessment.
Multi-variable and indication summaries: value distributions of several variables per group, and per-indication summaries of the relevant features.
Consensus clustering and feature extraction: patient subgroups and minimal actionable feature sets from clinical numerics.
Timeline, treatment flow, and trajectory analyses need treatment and measurement event data, which MOSAIC provides.
Variable types
Categorical
Gender, stage, grade, histology
Frequency tables, chi-square, stratified survival
Continuous
Age, BMI, tumour size, lab values
Mean/median comparison, t-tests, correlation
Time-to-event
OS, PFS, DFS
Kaplan-Meier, Cox regression, log-rank
Longitudinal
Treatment sequences, measurement trajectories
Timelines, flow diagrams, trajectory plots
Ordinal
ECOG status, tumour grade, line of therapy
Trend tests, stratified analyses
Multimodal integration
Combining modalities is where K-Pro's analytical range matters most, supporting cross-platform validation and robust biomarker development.
Cross-modality concordance
Bulk RNA-seq can be compared against single-cell, spatial, or proteomics data; single-cell against spatial; and histomics against either expression or single-cell composition. These comparisons validate findings across platforms — for example confirming that a bulk expression signal localises to the expected tissue compartment, or that H&E-derived TIL density agrees with CD8A expression.
Integration analyses
Expression by mutation or CNV status: transcriptional programs associated with an alteration, and dosage-sensitive genes.
Transcriptomics with histomics: expression correlated against cell densities, expression stratified by histomics-defined groups, and joint clustering.
Proteomics with transcriptomics: protein-RNA correlation, differential expression concordance, and multi-omics clustering.
Single-cell with spatial: deconvolution validation, ligand-receptor analysis under spatial proximity constraints, and signature mapping onto spatial coordinates.
Clinical with molecular: Table 1 across molecularly-defined groups, survival by molecular subtype, covariate-adjusted molecular effects, and treatment response by biomarker.
Advanced: consensus clustering, feature extraction, and multivariate prognostic models over mixed features from several modalities at once.
Statistical methods
DESeq2
Negative binomial model for count-based differential expression
Welch's t-test
Two-group comparison for normalized expression
Wilcoxon rank-sum
Non-parametric two-group comparison, for example of histomics features
Pearson / Spearman
Pairwise correlation between features
Cox proportional hazards
Multivariate survival modelling
Kaplan-Meier estimation
Survival probability curves
Log-rank test
Survival curve comparison between groups
Chi-square / Fisher's exact
Categorical variable comparison
ANOVA / Kruskal-Wallis
Multi-group comparison
ssGSEA
Per-sample pathway enrichment scoring
GSEA
Ranked list enrichment against gene sets
Consensus clustering
Robust unsupervised patient subtyping
Elastic-net / OMP
Sparse feature selection for biomarker discovery
LIANA
Ligand-receptor inference for cell-cell communication
InferCNV
Copy-number inference from expression data
Dataset coverage by modality
Which analyses you can run depends on the modalities present in your dataset. Availability varies by plan and by data access agreement — see Browse the dataset catalog and What's included in your plan.
MOSAIC
Multi-indication oncology
Bulk RNA, single-cell, spatial, WES, histomics, clinical
TCGA
Pan-cancer atlas
Bulk RNA, WES, histomics, clinical
CPTAC
Proteogenomics
Bulk RNA, proteomics, WES (variants), clinical
GTEx
Normal tissue
Bulk RNA, clinical
CellxGene Breast
Breast cancer
Single-cell, clinical
CellxGene HNSCC
Head and neck cancer
Single-cell, clinical
CellxGene NSCLC
Lung cancer
Single-cell, clinical
CellxGene Intestinal
GI tract
Single-cell, clinical
MOSAIC offers the broadest multimodal coverage and is the only dataset supporting spatial transcriptomics.
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