CAC3 Antibody

What is CACNB3 and what are its primary functions in cellular systems?

CACNB3 (Calcium Voltage-Gated Channel Auxiliary Subunit Beta 3) is a protein-coding gene that plays a critical regulatory role in voltage-dependent calcium channel function. The protein is predominantly involved in the regulation of surface expression and gating of calcium channels. CACNB3 functions as a regulatory beta subunit of voltage-dependent calcium channels and contributes to five distinct domains that collectively regulate calcium transport mechanisms . The protein exhibits several important cellular localizations including the cytosol, plasma membrane, and specifically within the L-type voltage-gated calcium channel complex. CACNB3 also demonstrates protein kinase binding and high voltage-gated calcium channel activity according to GO annotations .

Among its significant pathway involvements, CACNB3 participates in CCR5 Pathway in Macrophages and PEDF Induced Signaling, suggesting broader implications beyond simple ion channel regulation. The protein has multiple synonyms in the literature including CAB3, CACB3, and CACNLB3, which researchers should be aware of when conducting literature searches .

What are the recommended applications for CACNB3 polyclonal antibodies in research?

CACNB3 polyclonal antibodies are primarily optimized for immunohistochemistry (IHC) applications with a recommended dilution range of 1:40-1:200 . The antibody has been verified for detection in human esophagus cancer tissue samples, making it particularly valuable for oncology research. While IHC represents the validated application, researchers should conduct preliminary testing when adapting the antibody for other techniques such as Western blotting, immunoprecipitation, or flow cytometry.

How should researchers properly store and handle CACNB3 antibodies to maintain optimal activity?

CACNB3 antibodies require specific storage and handling protocols to maintain their functionality and specificity. Commercial antibodies are typically supplied in a phosphate buffered solution (pH 7.4) containing 0.05% stabilizer and 50% glycerol . This formulation enhances stability during freeze-thaw cycles and extends shelf-life.

For optimal performance, store CACNB3 antibodies at -20°C where they typically remain valid for 12 months. It is critical to avoid repeated freeze-thaw cycles which can progressively degrade antibody quality through protein denaturation and aggregation . When shipped with ice packs, researchers should immediately transfer antibodies to appropriate storage conditions upon receipt. For working solutions, aliquot the stock antibody into single-use volumes to minimize freeze-thaw cycles of the main stock.

Before experimental use, allow antibodies to equilibrate to room temperature gradually and mix gently by inversion rather than vortexing, which can damage the antibody structure. For diluted working solutions, prepare fresh on the day of experimentation whenever possible or store at 4°C for no more than one week to prevent microbial contamination and performance degradation.

How can computational modeling enhance CACNB3 antibody design and epitope prediction?

Advanced computational modeling offers significant advantages for CACNB3 antibody design through multiple complementary approaches. Researchers can utilize fully guided homology modeling workflows incorporating de novo CDR loop conformation prediction to generate reliable 3D structural models directly from CACNB3 antibody sequences . This approach is particularly valuable when crystal structures are unavailable, allowing researchers to predict antibody-antigen interactions with high confidence.

For CACNB3 antibodies, computational tools enable:

• Batch homology modeling to accelerate construction of parent sequences and variants

• Prediction of antibody-antigen complex structures through ensemble protein-protein docking

• Enhancement of experimental epitope mapping resolution from peptide to residue-level detail

• Identification of favorable antibody-antigen contacts through fast protein-protein docking

These computational approaches allow researchers to derisk CACNB3 antibody development by identifying potential liabilities earlier in the research pipeline. Specifically, researchers can detect potential hotspots for aggregation using computational protein surface analysis and highlight potential surface sites for post-translational modification and chemical reactivity that might affect antibody performance .

What strategies can researchers employ to address cross-reactivity concerns with CACNB3 antibodies?

Cross-reactivity remains a significant challenge in CACNB3 antibody research, requiring systematic validation approaches. To address potential cross-reactivity, researchers should implement a multi-faceted validation strategy:

• Knockout/knockdown validation: Generate CACNB3 knockout or knockdown cell lines to confirm antibody specificity. The complete absence of signal in knockout systems provides compelling evidence for antibody specificity.

• Peptide competition assays: Pre-incubate the CACNB3 antibody with excess immunizing peptide before application. Significant signal reduction indicates specific binding to the target epitope.

• Testing across multiple paralogs: Test antibody reactivity against important CACNB3 paralogs, particularly CACNB4, which has been identified as a significant paralog with potential structural similarity .

• Orthogonal detection methods: Validate findings using alternative detection methods targeting CACNB3 (such as mass spectrometry) to confirm antibody specificity.

• Computational epitope analysis: Utilize protein sequence alignment tools to identify regions of high similarity between CACNB3 and other proteins in the experimental system, then evaluate whether the antibody epitope overlaps with these regions.

Cross-reactivity concerns are particularly relevant with polyclonal antibodies that recognize multiple epitopes. When absolute specificity is required, researchers may need to isolate specific antibody fractions through affinity purification against the immunizing peptide or consider monoclonal alternatives with demonstrably higher specificity.

How can researchers optimize immunohistochemistry protocols for CACNB3 detection in different tissue types?

Optimizing immunohistochemistry (IHC) protocols for CACNB3 detection requires systematic adaptation to specific tissue types and experimental questions. While the recommended dilution range for CACNB3 polyclonal antibodies is 1:40-1:200 for IHC , researchers should conduct antibody titration experiments to determine optimal concentration for each specific tissue type and fixation method.

For consistent results across experiments, researchers should standardize the following parameters:

• Fixation protocol: Optimize fixation time and fixative composition to preserve CACNB3 epitopes while maintaining tissue morphology.

• Antigen retrieval: Test multiple antigen retrieval methods (heat-induced vs. enzymatic) and buffers to maximize CACNB3 epitope accessibility.

• Blocking conditions: Implement robust blocking protocols using serum from the same species as the secondary antibody to minimize non-specific binding.

• Controls: Always include positive controls (tissues known to express CACNB3, such as neural tissues) and negative controls (omission of primary antibody and ideally CACNB3 knockout tissues).

• Detection system: Compare chromogenic vs. fluorescent detection systems based on the experimental requirement for sensitivity vs. multiplexing.

For tissues with high endogenous biotin or peroxidase activity, incorporate specific blocking steps to minimize background. When studying tissues with autofluorescence, consider spectral unmixing or using fluorophores with emission spectra distinct from the autofluorescence profile.

What approaches can be used to stratify experimental samples based on CACNB3 antibody levels?

Stratification of samples based on CACNB3 antibody levels enables more refined analysis of experimental data and can reveal correlations that might be obscured in unstratified analysis. Researchers can adapt methodologies from antibody stratification studies to develop a systematic approach for CACNB3 antibody research.

A tiered stratification approach typically includes:

• Qualitative stratification: Categorize samples as antibody-positive or antibody-negative based on detection threshold.

• Semi-quantitative stratification: Further classify positive samples into relative concentration categories:

  • Low: Barely detectable levels (e.g., <100 ng/mL)

  • Medium: Moderate levels (e.g., 100-500 ng/mL)

  • High: Strong signal (e.g., >500 ng/mL)

• Temporal stratification: For longitudinal studies, categorize samples based on antibody persistence:

  • Preexisting (positive before experimental intervention)

  • Developing (becoming positive after intervention)

  • Transient (returning to negative status)

  • Persistent (remaining positive at the final timepoint)

This multi-dimensional stratification can reveal relationships between antibody levels and experimental outcomes that would remain hidden in binary positive/negative classifications. For instance, in pharmacokinetic studies, high antibody concentrations (>500 ng/mL) at later time points have been associated with significantly lower drug concentrations, demonstrating the importance of both concentration and temporal stratification .

Statistical analysis of stratified data should employ appropriate methods such as ANOVA models with antibody status or concentration as fixed effects, and the Jonckheere-Terpstra trend test to evaluate statistically significant trends across increasing antibody concentration categories .

How can researchers address batch effects and variability in CACNB3 antibody experiments?

Batch effects and experimental variability represent significant challenges in CACNB3 antibody research that can confound data interpretation. Implementing systematic approaches to minimize and account for these variations is essential for reproducible research:

• Experimental design strategies:

  • Include internal reference standards across all experimental batches

  • Randomize samples across batches to prevent systematic bias

  • Process technical and biological replicates in different batches

  • Maintain detailed records of lot numbers, storage conditions, and handling procedures

• Technical standardization:

  • Standardize antibody handling protocols including thawing procedures and dilution methods

  • Prepare master mixes of reagents whenever possible to minimize pipetting errors

  • Calibrate equipment regularly including pipettes, plate readers, and imaging systems

  • Use automated systems where available to reduce operator variability

• Computational correction approaches:

  • Apply batch effect correction algorithms (e.g., ComBat, Surrogate Variable Analysis) during data analysis

  • Utilize mixed-effects statistical models that account for batch as a random effect

  • Implement normalization using internal controls present across all batches

  • Consider probabilistic modeling approaches that explicitly account for technical variability

• Quality control metrics:

  • Establish acceptance criteria for experimental runs including signal-to-noise ratios and coefficient of variation thresholds

  • Implement Levey-Jennings charts to monitor assay performance over time

  • Conduct regular proficiency testing among laboratory personnel

  • Document environmental conditions during experiments including temperature and humidity

For longitudinal studies, researchers should consider incorporating inter-occasion variability (IOV) in their statistical models, as this approach has proven valuable in pharmacokinetic studies involving antibody measurements . When absolute quantification is necessary, develop a standard curve using recombinant CACNB3 protein to convert relative measurements to absolute concentrations.

What are the optimal methods for quantifying CACNB3 antibody affinity and specificity?

Rigorous characterization of CACNB3 antibody affinity and specificity requires a multi-method approach that captures different aspects of antibody-antigen interactions. Researchers should employ complementary techniques to establish comprehensive antibody performance profiles:

• Surface Plasmon Resonance (SPR):

  • Provides real-time, label-free measurement of binding kinetics

  • Determines association rate (kon), dissociation rate (koff), and equilibrium dissociation constant (KD)

  • Enables comparison of binding to CACNB3 versus structurally related proteins (e.g., CACNB4)

  • Requires purified recombinant CACNB3 protein immobilized on sensor chips

• Bio-Layer Interferometry (BLI):

  • Alternative optical technique for measuring binding kinetics

  • Particularly useful for crude samples where SPR might be problematic

  • Can be used to measure antibody concentration through binding rates to immobilized CACNB3

• Enzyme-Linked Immunosorbent Assay (ELISA):

  • Competitive ELISA comparing IC50 values for CACNB3 versus related proteins

  • Titration ELISA to establish relative affinities across antibody batches

  • Epitope binning using pairs of antibodies to map binding regions

• Flow Cytometry:

  • Measures binding to native CACNB3 in cell-based systems

  • Quantifies median fluorescence intensity to compare relative binding strengths

  • Particularly valuable for confirming antibody performance in physiological contexts

• Computational Analysis:

  • Protein Mutation FEP+ to predict the impact of residue substitutions on binding affinity

  • Residue Scan FEP+ with lambda dynamics to rapidly identify high-quality protein variants

  • Ensemble docking to predict binding modes and interfaces

For specificity assessment, testing cross-reactivity against a panel of related calcium channel subunits provides the most comprehensive evaluation. Results should be presented as a matrix of binding affinities across tested proteins, with fold-differences in affinity clearly indicated.

When evaluating commercial antibodies where affinity data may be limited, researchers should conduct their own validation using at minimum Western blotting against recombinant CACNB3 alongside cell lysates from tissues known to express (positive control) or not express (negative control) the target protein.

How should researchers interpret contradictory results in CACNB3 antibody experiments?

Contradictory results in CACNB3 antibody experiments require systematic investigation and reconciliation through a structured analytical approach. When faced with experimental contradictions, researchers should:

• Evaluate antibody characteristics:

  • Compare epitope specificity between different antibodies used (monoclonal vs. polyclonal)

  • Assess recognition of different protein domains or post-translational modifications

  • Verify antibody lot consistency through in-house validation protocols

  • Consider potential cross-reactivity with CACNB3 paralogs, particularly CACNB4

• Analyze experimental conditions:

  • Compare protein denaturation states across methods (native vs. denatured conditions)

  • Assess buffer compositions that might affect epitope accessibility

  • Evaluate fixation protocols that could mask or alter epitopes

  • Review sample preparation methods that might affect protein conformation

• Consider biological variables:

  • Examine cell or tissue type differences that might affect CACNB3 expression levels

  • Assess potential splice variant expression that could affect antibody recognition

  • Investigate post-translational modifications that might be tissue or condition-specific

  • Evaluate developmental or physiological states that might affect protein expression

• Apply orthogonal validation approaches:

  • Implement antibody-independent detection methods (e.g., mass spectrometry)

  • Utilize genetic approaches (siRNA knockdown, CRISPR knockout) to confirm specificity

  • Employ multiple antibodies targeting different epitopes to validate observations

  • Consider mRNA expression data to corroborate protein detection results

When presenting contradictory results, transparently report all experimental conditions and antibody specifications. This approach enables the research community to evaluate potential sources of variation and contributes to improved experimental designs in future studies.

What statistical approaches are recommended for analyzing the impact of CACNB3 antibody variability on experimental outcomes?

Statistical analysis of CACNB3 antibody experiments requires approaches that account for the unique characteristics of antibody-based detection methods and their inherent variability. Recommended statistical frameworks include:

• Analysis of Variance (ANOVA) Models:

  • Implement ANOVA models with antibody status or concentration as fixed effects

  • Incorporate residual variance heterogeneity across different antibody statuses or concentrations

  • Transform data (typically logarithmic transformation) to improve normality when appropriate

  • Report differences in experimental outcomes using confidence intervals rather than p-values alone

• Trend Analysis:

  • Apply the Jonckheere-Terpstra (JT) trend test to evaluate statistically significant trends across increasing antibody concentration categories

  • Implement Spearman's correlation test to evaluate nonlinear correlations between antibody concentrations and experimental outcomes

  • Use these approaches when linear relationships cannot be assumed

• Mixed-Effects Modeling:

  • Incorporate random effects to account for batch-to-batch variability

  • Include inter-occasion variability (IOV) for longitudinal studies

  • Develop hierarchical models that account for nested experimental designs

  • Use likelihood ratio tests to evaluate the significance of random effects

• Bayesian Approaches:

  • Implement Bayesian hierarchical models to incorporate prior knowledge about antibody performance

  • Utilize Markov Chain Monte Carlo (MCMC) methods for parameter estimation

  • Report credible intervals rather than confidence intervals

  • Consider model averaging when multiple models have similar explanatory power

How can researchers effectively integrate computational models with experimental CACNB3 antibody data?

Integrating computational models with experimental CACNB3 antibody data creates a powerful research framework that leverages the strengths of both approaches. Effective integration strategies include:

• Model Refinement Using Experimental Data:

  • Use experimental binding affinity measurements to validate and refine computational binding predictions

  • Incorporate epitope mapping data to constrain antibody-antigen docking simulations

  • Utilize experimental conformational data (e.g., hydrogen-deuterium exchange) to validate predicted structural models

  • Implement iterative refinement cycles where experimental insights guide computational model enhancement

• Experimental Design Guided by Computational Insights:

  • Use computational epitope predictions to guide the design of CACNB3 antibody panels

  • Apply computational stability predictions to prioritize antibody candidates for experimental validation

  • Employ molecular dynamics simulations to identify potential conformational epitopes for antibody development

  • Leverage computational batch homology modeling to accelerate experimental characterization of antibody variants

• Integrated Data Analysis Frameworks:

  • Develop machine learning models that incorporate both computational predictions and experimental measurements

  • Implement Bayesian networks to formalize relationships between computational and experimental variables

  • Utilize principal component analysis to identify patterns across computational and experimental datasets

  • Develop ensemble methods that combine multiple computational models calibrated with experimental data

• Visualization and Interpretation Tools:

  • Create integrated dashboards that display computational predictions alongside experimental results

  • Develop structural visualization tools that map experimental findings onto computational models

  • Implement interactive analysis platforms that allow researchers to explore relationships between computational and experimental parameters

  • Design statistical frameworks that formally quantify agreement between computational predictions and experimental observations

This integrated approach enables researchers to derisk CACNB3 antibody development by identifying potential liabilities earlier in the research pipeline . By combining computational and experimental methodologies, researchers can more comprehensively characterize antibody candidates, enhance the probability of experimental success, and accelerate the development of high-performance CACNB3 antibodies for research applications.

How can CACNB3 antibodies be utilized in neurological disease research?

CACNB3 antibodies offer valuable tools for neurological disease research due to the protein's critical role in voltage-dependent calcium channel regulation. Strategic applications include:

• Calcium Channelopathy Investigations:

  • Characterize CACNB3 expression patterns in neurological disorders associated with calcium signaling dysregulation

  • Compare CACNB3 distribution between healthy and pathological tissues using immunohistochemistry

  • Evaluate co-localization with other channel components to assess complex assembly in disease states

  • Investigate potential alterations in post-translational modifications using specific antibodies

• Signaling Pathway Analysis:

  • Examine CACNB3's role in CCR5 Pathway in Macrophages, which has implications for neuroinflammation

  • Investigate PEDF Induced Signaling connections to neuroprotective mechanisms

  • Study CACNB3's protein kinase binding properties in the context of neuronal signal transduction

  • Assess interactions with other signaling molecules through co-immunoprecipitation approaches

• Therapeutic Target Validation:

  • Utilize antibodies to validate CACNB3 as a potential therapeutic target in neurological disorders

  • Employ blocking antibodies to modulate CACNB3 function in cellular and animal models

  • Implement proximity ligation assays to identify protein interaction partners as complementary therapeutic targets

  • Assess changes in CACNB3 expression following treatment with candidate therapeutics

• Biomarker Development:

  • Evaluate CACNB3 as a potential biomarker for specific neurological conditions

  • Develop quantitative assays for CACNB3 detection in cerebrospinal fluid or blood

  • Correlate CACNB3 levels with disease progression or treatment response

  • Compare CACNB3 expression patterns across different neurological disorders

When designing neurological disease studies using CACNB3 antibodies, researchers should carefully consider the specific cellular localization of the protein (cytosol, plasma membrane, apical plasma membrane, L-type voltage-gated calcium channel complex) , as these different pools may have distinct functional implications in disease states.

What are the current challenges in developing selective CACNB3 antibodies for research applications?

Developing highly selective CACNB3 antibodies presents several significant challenges that researchers must address to advance the field:

• Structural Homology with Related Proteins:

  • CACNB3 shares substantial sequence similarity with paralogs, particularly CACNB4

  • Identifying truly unique epitopes requires detailed sequence analysis and structural modeling

  • Antibody cross-reactivity testing against all calcium channel beta subunits is essential

  • Computational epitope prediction can help identify regions with maximal sequence divergence

• Conformational Epitope Recognition:

  • CACNB3's function in different cellular compartments may involve distinct conformational states

  • Native conformation preservation during immunization and screening is technically challenging

  • Different experimental applications require antibodies recognizing different conformational states

  • Developing antibodies that distinguish between free and channel-complexed CACNB3 remains difficult

• Validation in Complex Physiological Contexts:

  • CACNB3 function must be studied in the context of assembled calcium channel complexes

  • Validation in knockout systems is complicated by potential compensatory mechanisms

  • Distinguishing between direct and indirect effects of antibody binding requires sophisticated controls

  • Confirming specificity across diverse experimental conditions and biological systems is resource-intensive

• Technical Limitations in Antibody Development:

  • Generating antibodies against specific post-translational modifications requires specialized approaches

  • Producing antibodies with both high specificity and high affinity often involves trade-offs

  • Developing antibodies that work consistently across multiple applications (IHC, WB, IP, etc.) is challenging

  • Ensuring lot-to-lot consistency for polyclonal antibodies remains problematic

To address these challenges, researchers are increasingly turning to integrated approaches combining computational antibody design with high-throughput screening methodologies. These approaches leverage computational tools for antibody structure prediction, CDR loop conformation modeling, and binding affinity prediction to accelerate the development of improved CACNB3-specific antibodies .

How can researchers optimize multiplexed detection protocols involving CACNB3 antibodies?

Multiplexed detection protocols involving CACNB3 antibodies require careful optimization to ensure specificity, sensitivity, and reproducibility across multiple targets. Researchers should implement the following strategies:

• Antibody Selection and Validation:

  • Choose antibodies from different host species to enable simultaneous detection

  • Validate each antibody individually before attempting multiplexed protocols

  • Test for potential cross-reactivity between detection systems

  • Confirm performance in the specific buffer conditions used for multiplexing

• Signal Optimization Strategies:

  • Titrate each antibody independently to determine optimal concentrations in the multiplex context

  • Establish signal-to-noise ratios for each target and adjust concentrations accordingly

  • Implement sequential detection protocols when direct multiplexing causes interference

  • Consider tyramide signal amplification for targets with low expression levels

• Cross-Talk Mitigation:

  • Employ spectral unmixing algorithms for fluorescent detection systems

  • Implement appropriate blocking steps between sequential detection rounds

  • Use carefully selected fluorophore combinations with minimal spectral overlap

  • Consider chromogenic multiplexing with distinct precipitates for brightfield applications

• Validation and Controls:

  • Include single-stained controls alongside multiplexed samples

  • Implement fluorescence minus one (FMO) controls to assess bleed-through

  • Use tissues or cells with known expression patterns as biological reference standards

  • Validate findings with orthogonal detection methods when possible

For complex multiplexing involving CACNB3 alongside other targets, researchers should carefully consider the cellular localization of each target to ensure appropriate interpretation of co-localization signals. CACNB3's distribution across multiple cellular compartments (cytosol, plasma membrane, L-type voltage-gated calcium channel complex) requires particularly careful analysis to distinguish biologically meaningful co-localization from coincidental spatial proximity.

Advanced multiplexing applications may benefit from computational protein-protein interaction predictions to identify potential binding partners for co-localization studies. Tools that predict antibody-antigen complex structures through ensemble protein-protein docking can guide the development of multiplexed detection protocols by identifying antibody combinations that target non-overlapping epitopes .

What are the emerging trends in CACNB3 antibody research methodologies?

The field of CACNB3 antibody research is evolving rapidly with several emerging methodological trends that promise to enhance specificity, reproducibility, and application versatility:

• Computational-Experimental Integration:

  • Increasing adoption of computational antibody design tools for rational epitope selection

  • Implementation of de novo CDR loop conformation prediction to optimize binding properties

  • Development of batch homology modeling approaches to accelerate variant characterization

  • Application of computational protein surface analysis to identify potential liabilities early

• Advanced Validation Frameworks:

  • Transition from qualitative to quantitative antibody validation metrics

  • Implementation of CRISPR/Cas9 engineered knockout controls as gold standard validation

  • Development of multiplexed validation approaches that simultaneously assess specificity across multiple tissues

  • Adoption of systematic cross-reactivity testing against all paralogs, particularly CACNB4

• Single-Cell Applications:

  • Development of protocols for CACNB3 detection at single-cell resolution

  • Integration with single-cell transcriptomics to correlate protein and mRNA levels

  • Application in spatial proteomics to map CACNB3 distribution within tissue microenvironments

  • Implementation in high-content imaging systems for phenotypic screening

• Standardization Initiatives:

  • Establishment of minimum reporting standards for CACNB3 antibody characterization

  • Development of reference materials for inter-laboratory standardization

  • Creation of community databases documenting antibody performance across applications

  • Implementation of standard operating procedures for key CACNB3 antibody applications

These methodological advances are enabling more sophisticated research applications and improving the reliability of CACNB3 antibody-based studies. As computational tools become more accessible and validation frameworks more rigorous, researchers can expect continued improvements in antibody performance and experimental reproducibility.

How can researchers contribute to improving CACNB3 antibody standardization across the field?

Improving standardization of CACNB3 antibody research requires coordinated efforts from individual researchers, laboratories, and the broader scientific community. Researchers can contribute through:

• Comprehensive Reporting Practices:

  • Document detailed antibody specifications including catalog numbers, lot numbers, and RRID identifiers

  • Report complete validation protocols including positive and negative controls

  • Publish detailed experimental conditions that affect antibody performance

  • Share raw data alongside processed results to enable independent analysis

• Validation and Benchmarking:

  • Participate in multi-laboratory validation studies comparing antibody performance

  • Contribute to round-robin testing of standardized protocols

  • Share validation resources such as knockout cell lines or recombinant proteins

  • Benchmark new antibodies against established reference standards

• Community Resource Development:

  • Contribute experimental results to antibody validation databases

  • Share optimized protocols through repositories like protocols.io

  • Participate in the development of consensus guidelines for CACNB3 research

  • Contribute to community-driven resource development (e.g., knockout mice, plasmids)

• Implementation of Statistical Best Practices:

  • Apply rigorous statistical frameworks appropriate for antibody data analysis

  • Implement stratification approaches that account for antibody level variations

  • Utilize trend analysis methods such as the Jonckheere-Terpstra test for dose-response relationships

  • Apply appropriate statistical corrections for multiplicity in complex experimental designs