2024-07-29

Introduction

  • Comparing automatic and manual methods for LORCA analysis
  • Key aspects of comparison:
    • Accuracy of key metrics (EImin, EImax, PoS)
    • Consistency of results (Coefficient of Variation)
    • Time efficiency

LORCA Analyzer App Overview

The LORCA Analyzer app streamlines the analysis process with several key features:

  1. File Upload and Processing
  2. Results Visualization
  3. CV Analysis
  4. Sample Lookup
  5. Data Export
  6. Master Data Management
  7. File Renaming

File Upload and Processing

  • Upload multiple CSV files
  • Automatic processing and validation
  • Invalid filename detection

Results Visualization

  • Display processed results in a table

  • Graphs for each sample

  • Compare key metrics across samples ## CV Analysis

  • Select specific samples for analysis

  • Calculate and display Coefficient of Variation

  • Identify best combination of runs

Sample Lookup

  • Search for specific samples
  • View detailed sample information
  • Compare multiple samples

Data Export and Master Data Management

  • Export processed data as CSV
  • View and manage master data
  • Update master database with new results

File Renaming

  • Rename files with invalid names
  • Automatic reprocessing after renaming
  • Maintain data integrity

Data Overview

Summary Statistics by Method
Method Samples Avg_EImin Avg_EImax Avg_PoS
Auto 6 0.153 0.451 53.9
Manual 6 0.152 0.454 53.6

Key Metrics Comparison: EImin

Key Metrics Comparison: EImax

Key Metrics Comparison: PoS

Coefficient of Variation: EImin

Coefficient of Variation: EImax

Coefficient of Variation: PoS

Statistical Analysis

Paired t-test Results
Metric t_statistic p_value
t EImin 0.792 0.464
t1 EImax -1.029 0.351
t2 PoS 0.999 0.364
  • All p-values > 0.05, indicating no statistically significant differences

Time Efficiency

  • Manual method: 25 minutes
  • Automatic method: 4.5 minutes
  • Time saved: 20.5 minutes (82% reduction)

Conclusions

  1. Accuracy: Automatic method produces statistically similar results to manual method
  2. Efficiency: 82% reduction in processing time
  3. Scalability: Improved throughput for large-scale analyses