Bulk RNA-seq Data Preprocessing and Quality Control Function

This function performs comprehensive preprocessing and quality control analysis for bulk RNA-seq data, including data validation, filtering, batch effect detection, principal component analysis, and visualization.

Usage

BulkPreProcess(
  data,
  sample_info = NULL,
  gene_symbol_conversion = TRUE,
  check = TRUE,
  min_count_threshold = 10,
  min_gene_expressed = 3,
  min_total_reads = 1e+06,
  min_genes_detected = 10000,
  min_correlation = 0.8,
  n_top_genes = 500,
  show_plot_results = TRUE,
  verbose = TRUE
)

Arguments

data: Expression matrix with genes as rows and samples as columns, or a list containing count_matrix and sample_info
sample_info: Sample information data frame (optional), ignored if data is a list
gene_symbol_conversion: Whether to convert Ensembles version IDs and TCGA version IDs to genes with IDConverter, default TRUE.
check: Whether to perform detailed quality checks, default TRUE
min_count_threshold: Minimum count threshold for gene filtering, default 10
min_gene_expressed: Minimum number of samples a gene must be expressed in, default 3
min_total_reads: Minimum total reads per sample, default 1e6
min_genes_detected: Minimum number of genes detected per sample, default 10000
min_correlation: Minimum correlation threshold between samples, default 0.8
n_top_genes: Number of top variable genes for PCA analysis, default 500
show_plot_results: Whether to generate visualization plots, default TRUE
verbose: Whether to output detailed information, default TRUE

Value

Filtered count matrix

Details

The function performs the following operations:

Data validation and format conversion
Basic statistics calculation (missing values, read depth, gene detection)
Optional detailed quality checks including:
- Sample correlation analysis
- Principal Component Analysis (PCA)
- Outlier detection using Mahalanobis distance
- Batch effect detection using ANOVA
Data filtering based on count thresholds and quality metrics
Optional gene symbol conversion
Visualization generation (PCA plots)

Quality Metrics

The function calculates and reports several quality metrics:

Data Integrity: Number of missing values
Gene Count: Total number of genes after filtering
Sample Read Depth: Total reads per sample
Gene Detection Rate: Number of genes detected per sample
Sample Correlation: Pearson correlation between samples
PCA Variance: Variance explained by first two principal components
Batch Effects: Proportion of genes significantly affected by batch

Sample Information Format

The sample_info data frame should contain:

sample: Character vector of unique sample identifiers
condition: Character vector of experimental conditions
batch: Optional character vector of batch identifiers

Filtering Criteria

Genes are retained if:

They have counts >= min_count_threshold in >= min_gene_expressed samples

Samples are retained if:

Total reads >= min_total_reads
Detected genes >= min_genes_detected
Mean correlation >= min_correlation (if check=TRUE)