GSE133744 Processing Pipeline

Publication

The Journal of biological chemistry (2020) — PMID 32647008

Dataset

Expression Profile Analysis of Rat Motoneurons During Myelination

1. 1

   Analysis and normalization were performed using two methods: dChip 1.3 (Li and Wong, 2001) and Drop Method (Aimone and Gage, 2004) software packages.

   dChip v1.3

   $ Bash example

   # --- dChip 1.3 (Li and Wong, 2001) ---
   # dChip is primarily a Windows-based GUI software for microarray analysis.
   # Direct command-line execution in a typical bash environment is not common.
   # The following represents the conceptual steps for normalization and analysis
   # that would typically be performed using dChip.
   
   # # Installation of dChip (typically a Windows executable download and install)
   # # No direct bash installation command is generally available for dChip.
   
   # Conceptual command for dChip normalization and analysis:
   # This command is illustrative and not directly executable in a standard bash environment.
   # It represents the actions performed by dChip on Affymetrix CEL files.
   # dchip_process \
   #   --input-celfiles "path/to/raw_data/*.CEL" \
   #   --normalization-method "model_based" \
   #   --expression-index-method "PM_only" \
   #   --output-dir "dchip_normalized_output" \
   #   --log-file "dchip_run.log"
   
   # --- Drop Method (Aimone and Gage, 2004) ---
   # The Drop Method is a normalization technique, often implemented in statistical environments like R or MATLAB.
   # This example assumes an R script implementation.
   
   # # Install R if not already installed
   # # sudo apt-get update && sudo apt-get install -y r-base
   
   # Create a placeholder R script for the Drop Method
   cat << 'EOF' > drop_method_normalization.R
   # R script to perform Drop Method normalization
   # Based on Aimone and Gage, 2004 (J Neurosci Methods)
   
   # Function to implement a simplified Drop Method (conceptual)
   # In a real scenario, this would involve more complex statistical modeling
   # and handling of microarray data structures (e.g., AffyBatch object).
   perform_drop_normalization <- function(raw_data_matrix) {
     # raw_data_matrix: A matrix where rows are probes and columns are samples
     
     cat("Performing conceptual Drop Method normalization...\n")
     
     # The Drop Method involves identifying and removing outlier probes/arrays
     # based on their distribution relative to other arrays.
     # This is a highly simplified representation for demonstration.
     
     # Example: Simple quantile normalization as a stand-in for a normalization step
     # A true Drop Method implementation would be more sophisticated.
     normalized_data <- apply(raw_data_matrix, 2, function(x) {
       rank_x <- rank(x, ties.method = "average")
       sorted_x <- sort(x)
       return(sorted_x[rank_x])
     })
     
     cat("Drop Method normalization complete.\n")
     return(normalized_data)
   }
   
   # Load example raw data (replace with actual data loading, e.g., from CEL files)
   # For demonstration, create a dummy matrix
   set.seed(123)
   num_probes <- 1000
   num_samples <- 5
   raw_microarray_data <- matrix(rnorm(num_probes * num_samples, mean = 10, sd = 2),
                                 nrow = num_probes, ncol = num_samples)
   colnames(raw_microarray_data) <- paste0("Sample_", 1:num_samples)
   rownames(raw_microarray_data) <- paste0("Probe_", 1:num_probes)
   
   # Perform normalization
   normalized_data_drop <- perform_drop_normalization(raw_microarray_data)
   
   # Save normalized data
   write.csv(normalized_data_drop, "drop_method_normalized_data.csv", row.names = TRUE)
   
   cat("Normalized data saved to drop_method_normalized_data.csv\n")
   EOF
   
   Rscript drop_method_normalization.R

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