Best Practices for Biomarker Discovery Using Transcriptomics Data

Molecular biomarkers have the potential to greatly enhance efficiency and precision in clinical decision-making. Common methods for deriving these biomarkers include feature selection, machine learning (ML), and statistical modeling. Yet, training these models necessitates high-quality data—clean, accompanied by essential metadata, and sourced from human samples. Models built on faulty data risk generating inaccurate predictions, resulting in significant resource wastage.

Understanding the Power of Transcriptomics

At the heart of transcriptomics lies the study of RNA molecules, the messengers that convey genetic information from DNA to proteins. By analyzing transcriptomics data, researchers can paint a detailed picture of which genes are active, to what extent, and under what conditions. This dynamic snapshot provides invaluable insights into the molecular machinery of cells and tissues, offering a nuanced understanding of diseases at the molecular level.

The Quest for Biomarkers

Biomarkers, in the context of transcriptomics, are specific RNA molecules whose levels correlate with certain biological processes or disease states. They serve as molecular signatures, indicating the presence, progression, or severity of a disease. Identifying these biomarkers is crucial for early detection, personalized medicine, and monitoring treatment responses.

Best Practices in Biomarker Discovery

Biomarker discovery using transcriptomics data involves several key steps, including data quality control, sample size consideration, differential expression analysis, feature selection, cross-validation, biomarker validation, and interpretation of results in the context of biological relevance. By following the best practices in each of these steps, researchers can effectively leverage transcriptomics data for biomarker discovery, leading to improved disease diagnosis, prognosis, and treatment.

1. Data Processing for Biomarker Extraction

To ensure the suitability of transcriptomics data for biomarker extraction, it is crucial to process the data effectively. The following steps are recommended for the same:

Normalization: Correcting technical biases such as differences in library size and RNA composition is essential. Normalization methods such as TPM (Transcripts Per Million) or FPKM (Fragments Per Kilobase Million) can be employed for this purpose.
Filtering: Removing lowly expressed genes and samples with poor quality is necessary as genes with low counts across samples or samples with low sequencing depth may introduce noise into the analysis.
Batch Correction: Adjusting for technical variation introduced by experimental batches is crucial because batch effects can confound downstream analyses. Methods such as ComBat or surrogate variable analysis (SVA) can be employed to correct for these effects.

2. Sample Size Consideration

Adequate sample size is crucial for the statistical power of biomarker discovery studies. While there is no fixed rule for sample size determination and it may vary depending on the study design and the desired effect size, a larger sample size generally improves the reliability and generalizability of the findings.

3. Differential Expression Analysis

Identifying genes that are differentially expressed between different conditions (e.g., disease vs. control) is a fundamental step in biomarker discovery. Some key points to consider during this analysis are:

Statistical methods: Use appropriate statistical tests, such as t-tests, ANOVA, or linear models, to identify differentially expressed genes. Tools like DESeq2, edgeR, or limma are commonly used for this purpose.
Multiple testing correction: Correct for multiple hypothesis testing to control the false discovery rate. Methods like the Benjamini-Hochberg procedure can be used to adjust p-values.
Fold change threshold: Set a threshold for fold change to focus on genes with biologically significant changes in expression.

4. Feature Selection

With thousands of genes in transcriptomics data, feature selection is crucial to reduce dimensionality and focus on the most informative genes. Efficient techniques for feature selection include:

Filter methods: Select features based on statistical measures such as variance or correlation with the outcome. Genes with low variability across samples or with low correlation with the phenotype of interest may be filtered out.
Wrapper methods: Use machine learning algorithms such as random forests or support vector machines to evaluate subsets of features based on their predictive performance.
Dimensionality reduction: Apply techniques such as principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) to visualize and reduce the dimensionality of the data.

5. Cross-Validation

Cross-validation is one of the most widely used data resampling methods to assess the generalization ability of a predictive model and to prevent overfitting. The best practices include:

Choose the Right Cross-Validation Technique: Common techniques include k-fold cross-validation and leave-one-out cross-validation. Choose the method that best suits your dataset size and computational resources. For smaller datasets, leave-one-out cross-validation might be preferred, while k-fold cross-validation is suitable for larger datasets.
Stratification: Ensure that each fold of the cross-validation retains the class distribution of the original dataset. This is crucial, especially in biomarker discovery where imbalanced classes are common.
Nested Cross-Validation: For hyperparameter tuning and model selection, consider using nested cross-validation. This approach prevents overfitting to the validation set and provides a more unbiased estimate of model performance.
Performance Metrics: Select appropriate performance metrics tailored to the specific objectives of biomarker discovery. Common metrics include accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and precision-recall curves.
Multiple Runs: Perform multiple runs of cross-validation to ensure robustness of results. Random variations in the data splitting process can affect model performance, so averaging results over multiple runs provides a more reliable estimate.
Time Series Data Considerations: When dealing with time series data, it's crucial to pay attention to the temporal order of observations. Employ time-based splitting techniques such as time series cross-validation to preserve the temporal sequence.

6. Biomarker Validation

Biomarker validation is a complex process that necessitates coordination among multiple clinical centers and entails substantial financial investment. Ideally, only the most promising candidates, validated during the verification phase and supported by robust quantitative assays, proceed to the validation stage.Here are a few key points to consider:

Independent Validation Cohort: Validate biomarkers using an independent dataset distinct from the one used for discovery. This helps assess the generalizability of biomarkers across different populations or experimental conditions.
Blinded Validation: Conduct validation studies in a blinded manner to minimize bias. Researchers analyzing the validation data should be unaware of the biomarker status to prevent subjective interpretations.
Quality Assurance of Assays: High-quality quantitative assays are essential for reliable biomarker performance assessment. Assays must achieve a coefficient of variation less than 30% for adequate diagnostic sensitivity.
Replication: Replicate the validation study across multiple independent cohorts, if possible. Consistent performance across different cohorts enhances confidence in the robustness and reliability of the biomarkers.
Functional Validation: Assess the biological relevance of identified biomarkers through functional validation studies. This may involve in vitro experiments, animal models, or pathway analysis to elucidate the underlying biological mechanisms and validate the clinical significance of the biomarkers.
Longitudinal Studies: In the context of disease prognosis or treatment response prediction, consider conducting longitudinal studies to evaluate the stability and predictive power of biomarkers over time.

7. Interpretation and Biological Relevance

Finally, it is essential to interpret the results in the context of biological relevance:

Pathway analysis: Use gene set enrichment analysis (GSEA) or over-representation analysis (ORA) to identify biological pathways enriched with differentially expressed genes. Understanding the biological pathways associated with the biomarkers can provide insights into the underlying molecular mechanisms of the disease.
Biological context: Interpret biomarker candidates in the context of known biological mechanisms and pathways associated with the disease or condition of interest. One should consider the functional role of the genes and their relevance to the phenotype being studied.

Uncover Biomarkers More Effectively With Polly by Elucidata

Predict potential prognostic or diagnostic biomarkers using ML-ready omics samples on Polly.

Uncover Markers Contributing to Diseases
1. Perform feature selection exercises using well-annotated data on Polly. Polly’s comprehensive metadata annotations help you efficiently deduce important features being studied in the experiment (for instance, genes, proteins, or metabolites affecting disease progression).
2. Perform feature subsetting via differential gene expression and principle component analysis.
3. Prioritize subsetted features using commonly used ML techniques like Random Forest.
Classify Markers According to Their Function
1. Optimize biomarker classification using clinical metadata information. Perform complex network analysis to segregate biomarkers according to their function (prognostic, diagnostic, predictive).
2. Perform complex network analysis on Polly to segregate different types of novel biomarkers.
Validate Identified Markers With Evidence From the Public Domain
1. Fast-track the validation of identified biomarkers using ML-ready, public datasets on Polly. Validate the detected markers' credibility by comparing your rsults with published studies on related biomarkers.
2. Evaluate biomarkers for sensitivity, specificity, and clinical utility through rigorous statistical analysis.

Read how Elucidata helped a Boston-based clinical-stage therapeutics company- Hookipa in Biomarker Data Curation & Management with Polly.

By adhering to best practices in data acquisition, analysis, and validation, researchers are unraveling the mysteries encoded within our RNA. Each biomarker uncovered brings us closer to more personalized, effective treatments and a deeper understanding of the intricate dance of life at the molecular level.