qpcrNorm

qpcrNorm normalizes raw cycle threshold (Ct) data from real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) experiments to correct technical variation and enable accurate gene expression analysis.

Key Features:

• Input data: Processes raw cycle threshold (Ct) values from real-time quantitative reverse transcriptase PCR (qPCR) assays.
• Normalization algorithms: Implements two data-driven normalization methods that directly correct technical variation as alternatives to housekeeping gene-based approaches.
• Quantile normalization: Includes quantile normalization to enforce consistent expression distributions across samples.
• Diagnostic plots: Provides functions to generate diagnostic plots for assessing data quality and normalization effects.
• Scalability: Handles moderate to extensive gene sets, reported for ranges from about 50 up to several thousand genes.

Scientific Applications:

• Gene expression profiling: Supports accurate normalization in experiments requiring precise quantification of gene expression levels.
• Large-scale qPCR studies: Applicable to moderate-to-large qPCR datasets spanning roughly 50 to several thousand genes.
• Alternative to housekeeping normalization: Useful when traditional housekeeping gene-based normalization may be unreliable due to regulation under experimental conditions.

Methodology:

Applies two data-driven normalization methods, including quantile normalization, to correct technical variation in raw Ct data and uses diagnostic plotting to evaluate normalization effectiveness as an alternative to housekeeping gene-based approaches.