Study design and planning
In-depth guidance on experimental and clinical study designs and sample size estimation.
Data analysis
Statistical analysis of study results to help interpret and gain biological insights. Identification of significantly increased (or decreased) signals associated with SOMAmer™ Reagents. Statistical techniques may include univariate and/or multivariate analysis, depending on experimental design.
QC and pre-analytics
Ensure high-quality SomaScan Assay results with rigorous quality control (QC) measures, including sample integrity checks, signal normalization and batch effect correction. Pre-analytics support includes best practices for sample handling, processing and ensuring reproducibility across experiments.
Biomarker discovery
Identify proteins of interest that exhibit a significant change in expression levels across study groups to support biomarker discovery and validation efforts
Pathway analysis
Determine which biological pathways are enriched or altered based on your SomaScan data, helping connect protein expression changes to biological functions
Merging datasets
Combine multiple SomaScan Assay datasets from different studies or cohorts into a single dataset. This enables more comprehensive analysis, improves statistical power and facilitates cross-study comparisons.
Custom normalization
Apply custom normalization techniques to correct for batch effects, technical variability and sample-specific biases, ensuring accurate and comparable SomaScan Assay data across different conditions, time points or cohorts
Study design and planning
In-depth guidance on experimental and clinical study designs and sample size estimation.
Data analysis
Statistical analysis of study results to help interpret and gain biological insights. Identification of significantly increased (or decreased) signals associated with SOMAmer™ Reagents. Statistical techniques may include univariate and/or multivariate analysis, depending on experimental design.
QC and pre-analytics
Ensure high-quality SomaScan Assay results with rigorous quality control (QC) measures, including sample integrity checks, signal normalization and batch effect correction. Pre-analytics support includes best practices for sample handling, processing and ensuring reproducibility across experiments.
Biomarker discovery
Identify proteins of interest that exhibit a significant change in expression levels across study groups to support biomarker discovery and validation efforts
Pathway analysis
Determine which biological pathways are enriched or altered based on your SomaScan data, helping connect protein expression changes to biological functions
Merging datasets
Combine multiple SomaScan Assay datasets from different studies or cohorts into a single dataset. This enables more comprehensive analysis, improves statistical power and facilitates cross-study comparisons.
Custom normalization
Apply custom normalization techniques to correct for batch effects, technical variability and sample-specific biases, ensuring accurate and comparable SomaScan Assay data across different conditions, time points or cohorts
Data analysis
Statistical analysis of study results to help interpret and gain biological insights. Identification of significantly increased (or decreased) signals associated with SOMAmer™ Reagents. Statistical techniques may include univariate and/or multivariate analysis, depending on experimental design.
QC and pre-analytics
Ensure high-quality SomaScan Assay results with rigorous quality control (QC) measures, including sample integrity checks, signal normalization and batch effect correction. Pre-analytics support includes best practices for sample handling, processing and ensuring reproducibility across experiments.
Biomarker discovery
Identify proteins of interest that exhibit a significant change in expression levels across study groups to support biomarker discovery and validation efforts
Pathway analysis
Determine which biological pathways are enriched or altered based on your SomaScan data, helping connect protein expression changes to biological functions
Merging datasets
Combine multiple SomaScan Assay datasets from different studies or cohorts into a single dataset. This enables more comprehensive analysis, improves statistical power and facilitates cross-study comparisons.
Custom normalization
Apply custom normalization techniques to correct for batch effects, technical variability and sample-specific biases, ensuring accurate and comparable SomaScan Assay data across different conditions, time points or cohorts
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