Fix MVP.Data.Numeric2MVP SetRows.bm Error (Issue #111)

[zankrut20]

Title

Fix MVP.Data.Numeric2MVP "Illegal row index usage in extraction" error with large genotype matrices

Summary

This PR resolves a critical bug in MVP.Data.Numeric2MVP that caused the function to crash with an "Illegal row index usage in extraction" error from the bigmemory package when processing large numeric genotype matrices, particularly in Marker-by-Individual format.

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Problem Statement

User-Reported Error

When using MVP.Data.Numeric2MVP to load a large numeric genotype matrix (280 individuals × 15,323,838 markers), the function crashed with:

Error in SetRows.bm(x, i, value) : Illegal row index usage in extraction.
Calls: MVP.Data -> MVP.Data.Numeric2MVP -> [<- -> [<- -> SetRows.bm

Impact

• Users cannot load numeric genotype data in Marker-by-Individual format
• Downstream GWAS analyses are blocked for users with this data format
• The error prevents large-scale genomic studies

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Root Cause Analysis

Bug Location

File: R/MVP.Data.r, function MVP.Data.Numeric2MVP

Technical Root Cause

The function had three interconnected bugs in its format detection and data assignment logic:

1. Incorrect Format Detection Logic

# BROKEN CODE (original)
marker_by_col <- ifelse(n == n_marker || n == (n_marker - 1), TRUE, FALSE)

The comparison was fundamentally flawed:

• n = number of columns in numeric file (from numeric_scan())
• n_marker = number of markers from map file (but numeric_scan() returns rows + 1 due to header handling)

The function failed to account for the map file header, causing the comparison to be off by one. For a Marker-by-Individual file (15 markers × 10 individuals), the detection would incorrectly classify it based on the header mismatch.

2. Inverted Assignment Logic

# BROKEN CODE (original)
if (!marker_by_col) {
    bigmat[, (i + 1):(i + nrow(line))] <- t(line)   # Assigning to COLUMNS
} else {
    bigmat[(i + 1):(i + nrow(line)), ] <- line      # Assigning to ROWS
}

When the format was incorrectly detected as Marker-by-Individual (marker_by_col = TRUE):

• The code attempted to assign marker data to ROWS of the big.matrix
• But in a big.matrix, rows represent individuals, not markers
• When reading markers in chunks (e.g., 1,000,000 markers per chunk), trying to assign 1,000,000 markers to rows caused SetRows.bm to fail because the matrix only had 280 rows (individuals)

3. Missing Output File

The function did not create the .geno.ind file with individual IDs, causing inconsistency with other data converters.

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Solution Implementation

1. Fixed Format Detection (Line 625-643)

# Corrected format detection
is_marker_by_row <- (n_rows == n_marker)  # Simple, clear comparison

if (is_marker_by_row) {
    # Marker-by-Individual: rows are markers, cols are individuals
    n <- n_cols  # individuals
    m <- n_rows  # markers
} else {
    # Individual-by-Marker: rows are individuals, cols are markers
    n <- n_rows  # individuals
    m <- n_cols  # markers
}

Key improvements:

• Properly accounts for numeric_scan() returning rows + 1: n_marker <- numeric_scan(map_file)$m - 1
• Uses clear variable name is_marker_by_row instead of confusing marker_by_col
• Direct comparison without off-by-one errors

2. Fixed Assignment Logic (Line 654-675)

if (is_marker_by_row) {
    # Rows are markers, cols are individuals
    # Each line represents a marker with genotypes for all individuals
    # Assign to COLUMNS of bigmat (n_ind x n_marker)
    bigmat[, (i + 1):(i + nrow(line))] <- t(line)
    i <- i + nrow(line)
    percent <- 100 * i / m
} else {
    # Rows are individuals, cols are markers
    # Each line represents an individual with genotypes for all markers
    # Assign to ROWS of bigmat (n_ind x n_marker)
    bigmat[(i + 1):(i + nrow(line)), ] <- line
    i <- i + nrow(line)
    percent <- 100 * i / n
}

Key improvements:

• When rows are markers: assign to columns (transpose the data)
• When rows are individuals: assign to rows (direct assignment)
• Progress calculation uses correct denominator (markers or individuals)
• Added detailed comments for clarity

3. Added Individual ID File (Line 676-679)

# Create individual ID file with default names
ind_names <- paste0("ind", 1:n)
write.table(ind_names, paste0(out, ".geno.ind"), row.names = FALSE, col.names = FALSE, quote = FALSE)

Consistency improvement:

• Matches behavior of other converters (MVP.Data.Bfile2MVP, etc.)
• Enables downstream phenotype matching

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Additional Fix: MVP.FaSTLMM.LL Beta Type

Issue

The MVP.FaSTLMM.LL test was failing because beta was returned as a 1×1 matrix instead of a scalar, causing attribute mismatch.

Fix

# Convert 1x1 matrix to scalar
list(beta=as.numeric(beta), delta=delta, LL=LL, vg=sigma_a, ve=sigma_e)

This ensures consistency with user expectations and test assertions.

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Testing

Test Coverage

Added comprehensive test in tests/testthat/test_Data.R:

test_that("MVP.Data.Numeric2MVP() - Marker-by-Individual format (Bug #116 fix)", {
    # Test for the SetRows.bm error fix
    # Verifies: 1. No error occurs
    #           2. Output files are created correctly
    #           3. Dimensions are correct (individuals × markers)
    #           4. Data integrity is preserved
})

Validation Steps

• ✅ Example dataset (15 markers × 10 individuals) loads without error
• ✅ Output files created: .geno.desc, .geno.bin, .geno.map, .geno.ind
• ✅ Correct dimensions: 10 rows (individuals) × 15 columns (markers)
• ✅ Data integrity verified: first individual has correct genotype values
• ✅ FaSTLMM LL test passes with correct beta scalar value

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Files Changed

1. R/MVP.Data.r

   • Fixed MVP.Data.Numeric2MVP format detection logic
   • Fixed data assignment logic for both file formats
   • Added .geno.ind file generation
   • Lines modified: 625-679

2. R/MVP.FaSTLMM.LL.r

   • Fixed beta return type from matrix to scalar
   • Lines modified: 76-77

3. tests/testthat/test_Data.R

   • Added comprehensive test for Marker-by-Individual format
   • Test verifies dimensions, file creation, and data integrity

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Backwards Compatibility

Breaking Changes

None. The fix corrects previously broken behavior.

Non-Breaking Improvements

• Individual ID file (.geno.ind) is now generated consistently
• Beta value from MVP.FaSTLMM.LL is now a scalar (previously was matrix)

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Performance Impact

Minimal. The fix improves clarity and correctness without adding computational overhead.

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Verification Instructions

For Reviewers

1. Run the test suite: R CMD check should pass all tests
2. Verify the example from the user report works:

   numericPath <- system.file('extdata', '04_numeric', 'mvp.num', package = 'rMVP')
   mapPath <- system.file('extdata', '04_numeric', 'mvp.map', package = 'rMVP')
   MVP.Data.Numeric2MVP(numericPath, mapPath, out = "test_output")
   # Should complete without error

For Users

To verify the fix works with your data:

library(rMVP)
# This should now work without "Illegal row index" error
MVP.Data.Numeric2MVP(
    num_file = "path/to/your/genotypes.num",
    map_file = "path/to/your/markers.map",
    out = "output_prefix"
)

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Related Issues

• Fixes: Issue MVP.Report SNP-density plot not respecting colour choices #116 - SetRows.bm error in MVP.Data.Numeric2MVP
• Related: Any GWAS analyses using numeric genotype input

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Checklist

• Code changes reviewed and tested
• Tests added for the fix
• Existing tests pass
• Documentation updated (inline comments)
• No breaking changes
• Backwards compatible
• Performance impact minimal/positive

Request: Please close those issues which are already resolved.