Multi-Sample Comparison Analysis

Introduction

SCEVAN enables comparative analysis of copy number alterations across multiple samples. This is particularly useful for:

Longitudinal studies: Comparing primary tumor vs metastasis
Treatment response: Pre- and post-treatment samples
Patient cohorts: Identifying shared vs patient-specific alterations

Setup

library(SCEVAN)
library(ggplot2)
library(dplyr)

Preparing Multi-Sample Data

Data Structure

Multi-sample analysis requires a named list of count matrices:

# Example structure
listCountMtx <- list(
  "Sample1" = count_mtx_1,
  "Sample2" = count_mtx_2,
  "Sample3" = count_mtx_3
)

Download Example Data

# Load glioblastoma multi-sample data
load(url("https://www.dropbox.com/s/esqvnltucdqajg1/listCountMtx.RData?raw=1"))

# Examine structure
names(listCountMtx)

# Sample sizes
sapply(listCountMtx, ncol)

Running Multi-Sample Analysis

Basic Comparison

multiSampleComparisonClonalCN(
  listCountMtx,
  analysisName = "GBM_comparison",
  organism = "human",
  par_cores = 4
)

With Known Normal Cells

# Optionally provide known normal cells per sample
listNormCells <- list(
  "MGH102" = c("cell_a", "cell_b"),
  "MGH104" = c("cell_c", "cell_d"),
  "MGH105" = NULL,  # Auto-detect
  "MGH106" = NULL
)

multiSampleComparisonClonalCN(
  listCountMtx,
  listNormCells = listNormCells,
  analysisName = "GBM_with_normals",
  par_cores = 4
)

Output Interpretation

Generated Files

list.files("./output", pattern = "GBM_comparison")

File	Description
`*_allOncoHeat.png`	Combined OncoPrint across samples
`*_comparison.png`	Side-by-side CN profiles
`*_CloneTree.png`	Cross-sample phylogeny

Comparative Heatmap

The multi-sample comparison generates combined visualization files:

*_allOncoHeat.png - Combined OncoPrint showing shared and sample-specific alterations
*_CloneTree.png - Cross-sample phylogenetic tree

Visualization Functions

Plot All Clonal Profiles

# Generate combined clonal CN plot
plotAllClonalCN(
  sampleNames = names(listCountMtx),
  pathOutput = "./output"
)

Plot Subclonal Profiles

# Generate combined subclonal CN plot
plotAllSubclonalCN(
  sampleNames = names(listCountMtx),
  pathOutput = "./output"
)

Advanced Analysis

Identifying Shared Alterations

# Load individual results
results_list <- lapply(names(listCountMtx), function(s) {
  seg_file <- paste0("./output/", s, "_Clonal_CN.seg")
  if(file.exists(seg_file)) {
    read.table(seg_file, header = TRUE, sep = "\t")
  }
})
names(results_list) <- names(listCountMtx)

# Find alterations present in all samples
find_shared_alterations <- function(results_list) {
  # Get altered regions per sample
  altered_regions <- lapply(results_list, function(df) {
    if(!is.null(df)) {
      df[df$CN != 2, c("Chr", "Pos", "End", "CN")]
    }
  })
  
  # Find overlaps (simplified example)
  # In practice, use GenomicRanges for proper overlap detection
  altered_regions
}

shared <- find_shared_alterations(results_list)

Custom Comparison Plots

# Create custom comparison visualization
create_cn_comparison <- function(sample_names, output_path) {
  
  cn_data <- lapply(sample_names, function(s) {
    load(paste0(output_path, "/", s, "_CNAmtx.RData"))
    data.frame(
      sample = s,
      mean_cn = colMeans(CNA_mtx_relat)
    )
  })
  
  cn_df <- do.call(rbind, cn_data)
  
  ggplot(cn_df, aes(x = sample, y = mean_cn, fill = sample)) +
    geom_boxplot() +
    theme_minimal() +
    labs(
      title = "Global CNA Burden Comparison",
      x = "Sample",
      y = "Mean CNA Score"
    ) +
    theme(legend.position = "none")
}

# Generate plot
create_cn_comparison(names(listCountMtx), "./output")

Case Study: Head & Neck Cancer

Primary vs Lymph Node Metastasis

# Load HNSCC data
load(url("https://www.dropbox.com/s/6zns12amobs39g8/HNSCC26_data.RData?raw=1"))

# Examine samples
names(listCountMtx)  # Should show "Primary" and "LN"

# Run comparison
multiSampleComparisonClonalCN(
  listCountMtx,
  analysisName = "HNSCC26_comparison",
  organism = "human",
  par_cores = 4,
  plotTree = TRUE
)

Interpreting Primary vs Metastasis

Key questions to address:

Clonal evolution: Which alterations are clonal (present in both)?
Metastasis-specific: New alterations in lymph node
Lost alterations: Present in primary but not metastasis

Statistical Considerations

Sample Size Requirements

Analysis Type	Minimum Cells/Sample	Recommendation
Cell classification	100	500+
Subclone detection	200	1000+
Cross-sample comparison	300	1000+

Batch Effect Considerations

When comparing samples:

Process with same protocol if possible
Consider batch correction methods
Verify normal cell detection consistency

Best Practices

Workflow Checklist

Verify consistent gene annotation across samples
Check cell quality metrics per sample
Run individual analyses first
Review normal cell detection
Run multi-sample comparison
Validate shared alterations

Common Pitfalls

Issue	Cause	Solution
No shared alterations	Different tumor types	Verify sample identity
All alterations shared	Contamination	Check for cross-sample mixing
Inconsistent segmentation	Different cell counts	Normalize comparison

Session Info

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] rmarkdown_2.31
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.39    R6_2.6.1         fastmap_1.2.0    xfun_0.59       
#>  [5] maketools_1.3.2  cachem_1.1.0     knitr_1.51       htmltools_0.5.9 
#>  [9] buildtools_1.0.0 lifecycle_1.0.5  cli_3.6.6        sass_0.4.10     
#> [13] jquerylib_0.1.4  compiler_4.6.1   sys_3.4.3        tools_4.6.1     
#> [17] evaluate_1.0.5   bslib_0.11.0     yaml_2.3.12      otel_0.2.0      
#> [21] jsonlite_2.0.0   rlang_1.2.0