Data forms the very basis of life science research and development (R&D). From uncovering novel insights to driving informed decision-making and strategic planning, data fuels the engine of progress in early-stage R&D endeavors. However, the sheer volume and diversity of data generated across various stages of the R&D pipeline pose significant challenges. In this blog post, we discuss the essential function of data harmonization—a strategic approach to integrating, standardizing, and optimizing disparate datasets—in early-stage R&D. 

What is Data Harmonization?

Data harmonization is the process of standardizing data from different sources to make them compatible and suitable for downstream analysis. This process is essential to progress in early-stage R&D, to explore and validate novel ideas, concepts and technologies. Research at this stage involves bulky data arising from different experiments performed in-house or obtained from public data repositories. 

Harmonization for these datasets involves the completion of missing annotations and establishing uniform data structures to result in high-quality data which can accelerate downstream analyses. This process is key to facilitating biological research into disease mechanisms, potential drug targets, and the discovery of new therapeutic approaches.

Benefits of Data Harmonization in Early Stage R&D

Data harmonization offers a multitude of benefits that drive innovation and propel projects forward. At the forefront of these benefits lies accelerated research and development timelines. By streamlining data integration and standardization processes, data harmonization minimizes the time spent on data pre-processing and maximizes the time allocated for analysis and research insights. Data harmonization not only expedites the pace of research but also enables researchers to iterate and refine their hypotheses with greater agility. Data harmonization offers several advantages that are instrumental in streamlining data integration, optimizing analysis workflows, and maximizing the utility of diverse datasets. Here are five key advantages of data harmonization:

1. Improved Data Consistency and Quality:

Data harmonization ensures consistency across diverse datasets by standardizing formats, structures, and metadata. By enforcing uniform data conventions and semantics, discrepancies and inconsistencies are minimized, resulting in higher data quality. High-quality data is suited for machine learning algorithms and drives real patient outcomes. Standardized data facilitates accurate comparisons, reduces errors in analysis, and enhances the reliability of research outcomes.

2. Data Integration

Data for early-stage R&D often come from diverse sources, including public repositories and proprietary data generated in-house. These datasets differ in formats, metadata completeness, and structures. Data harmonization provides for sound integration across data sources. Integrated data provides more statistical power for downstream analysis through analysis pipelines and algorithms.

3. Enhanced Interoperability:

One of the primary benefits of data harmonization is its ability to facilitate interoperability and integration across disparate datasets. By aligning data from different sources to a common framework, harmonization enables seamless data exchange and integration. This interoperability fosters collaboration among researchers, promotes knowledge sharing, and facilitates cross-functional analysis, leading to a more comprehensive understanding of complex biological phenomena.

4. Streamlined Data Analysis Workflows:

Harmonized data simplifies and streamlines data analysis workflows, reducing the time and effort required for data processing and interpretation. Standardized formats and structures enable researchers to automate data manipulation tasks, leverage analytical tools more effectively, and focus on deriving meaningful insights rather than wrestling with data inconsistencies. This streamlined workflow accelerates research timelines and enhances productivity in data-driven endeavors.

5. Facilitated Data Accessibility and Exploration:

Data harmonization unlocks the full potential of datasets for data accessibility and exploration. By consolidating and standardizing diverse datasets, researchers gain access to a broader spectrum of data, enabling comprehensive analysis and exploration. This facilitates the identification of hidden patterns, trends, and correlations within the data, leading to novel discoveries, and prioritization of potential biomarkers and insights that may not have been apparent when working with fragmented datasets.

Data Harmonization leads to Accelerated Analysis and Improved Insights

Ultimately, data harmonization empowers organizations to remove redundancies in their data processing workflows. Existing datasets can be put through accelerated timelines and maximally leveraged for research progress. Data harmonization also facilitates regulatory compliance, ensuring quicker passage through regulatory processes and expediting research discovery and application.

Challenges in Early-stage R&D Data

Despite its transformative potential, early-stage R&D data poses several challenges that impede progress and hinder innovation. Data harmonization offers numerous benefits, but it also presents several challenges, particularly in the context of life science research. Here are some key challenges in data harmonization:

1. Data Heterogeneity:

Life science research encompasses a wide range of experimental techniques, platforms, and data types, resulting in diverse data sources. From genomics and transcriptomics to proteomics, metabolomics, and clinical data, researchers must contend with heterogeneous datasets generated from various sources, public repositories, and in-house data. Harmonizing these diverse datasets requires overcoming differences in data formats, structures, and semantics, posing significant challenges in integration and standardization.

2. Data Silos and Fragmentation:

In large research organizations and multi-disciplinary teams, data silos often emerge as barriers to collaboration and knowledge sharing. Fragmentation of data across different departments, platforms, and public and private repositories further exacerbates this issue, hindering efforts to harmonize and integrate data. Overcoming data silos and fragmentation requires establishing robust data management practices and fostering a culture of collaboration and data sharing across organizational boundaries.

3. Inconsistencies in Data Formats and Standards:

Another challenge in data harmonization stems from inconsistencies in data formats, standards, and metadata schemas. Different research groups and organizations may adopt varying conventions for data representation, making it difficult to reconcile and standardize disparate datasets. For example, single-cell RNA sequencing data may be accessible in formats such as loom, h5, rds, or mtx files. Harmonizing these inconsistencies requires developing interoperable data formats, aligning metadata standards, and implementing data governance policies to ensure consistency and compliance with best practices.

4. Data Quality and Metadata Completeness:

Ensuring data quality and completeness is paramount in data harmonization efforts. However, heterogeneous datasets may vary in terms of data quality, reliability, and completeness, posing challenges in integrating and reconciling disparate datasets. Many public repositories, for example, hold data with missing annotations and essential metadata which are important for data accessibility and applicability. Missing data and incomplete metadata introduce long delays in research timelines as addressing them is an arduous process.  Addressing data quality issues requires rigorous data validation and quality control measures, including data cleaning, normalization, and validation, to ensure the integrity and reliability of harmonized datasets.

5. Complexity of Data Analysis:

Harmonized datasets may present challenges in data analysis, particularly due to the complexity of the data involved. Analyzing integrated datasets requires advanced techniques like analytical pipelines, computational resources, and domain expertise to navigate and interpret the data effectively. Selecting the right analytical methods and algorithms for downstream analysis is an important step in research. Moreover, integrating data from multiple sources may introduce biases, confounding factors, and technical challenges that complicate data analysis workflows, requiring careful consideration and validation of analytical results.

6. Data Volume:

Data harmonization efforts must also address large data volumes routinely generated in life science R&D. Handling bulky data files with multiple missing values, merging experimental and clinical data into a single file, and meeting diverse input and output criteria are a few issues associated with such data. Effectively dealing with these issues requires good data infrastructure and computational resources. Analytical solutions that might be effective for one small dataset fail at processing tens of terabytes of data in typical datasets. Scalability requires robust, large-scale data infrastructure and computational expertise.

Overcoming Challenges in Data Harmonization

In light of these challenges, implementing data harmonization in early-stage R&D requires a strategic, systematic approach. By adopting innovative solutions, organizations can overcome these challenges and accelerate research progress. 

Polly by Elucidata is a robust data harmonization platform that mitigates these challenges. Polly serves as a one-stop solution for all early-stage R&D data needs, offering a suite of bioinformatics analysis, visualization, data processing, machine learning, and data management tools. From integrating diverse datasets to standardizing data formats and streamlining downstream analysis, Polly empowers researchers to drive research progress and deliver real-world applications faster.

Data Harmonization on Polly:

Polly harmonizes data from public and in-house data, using a configurable, granular, and transparent curation process. Polly's powerful harmonization engine processes measurements, links to ontology-backed metadata, and transforms datasets into a consistent data schema.

By doing so, it accelerates downstream analysis by ~24 times. The data harmonization process completes metadata annotations with 99.99% accuracy and annotates them with 30+ metadata fields. All data is checked for quality and completeness with around 50 QA/QC checks. The machine learning algorithms ensure uniformity across data formats, structures, and semantics making it fit for downstream analysis.  

Accelerate Path to Clinic with Polly's Suite of Custom Solutions

Polly’s harmonized multi-modal biomedical data and suite of IND-enabling solutions are designed to help R&D teams expedite their journey toward clinical trials.

Pipeline Development:

Data processing pipelines can be customized to data and analysis requirements, chosen from a suite of 30+ scientifically validated pipelines, or further optimized to reduce costs and runtimes. Polly also offers to develop and deploy customized pipelines tailored to your omics data type & analysis requirements. Our platform runs complex, multi-threaded pipelines at a fraction of the cost and runtime of typical high-throughput data pipelines. 

ML Solutions & Bioinformatics Analysis:

‘Polly Verified’ data is delivered ready for functional models or analysis pipelines using machine learning (ML-ready). You can build, fine-tune, train, and deploy foundational ML models on top of your own harmonized data to drive insights 75% faster. 

Polly also helps unlock powerful bioinformatics use cases through its harmonized datasets and Polly’s Data Concierge services. Our experts can help you find relevant datasets from Polly’s expansive data corpus with detailed annotations to best match your inclusion or exclusion criteria. You can match indications to target, predict biomarkers, compare signatures, cell type annotation, and more. We also have domain experts who can help with metadata-based exploration, differential expression, knowledge graphs, and interactive dashboards according to specific research needs. 

Visualization & Analysis:

Polly offers a host of data visualization tools, including web applications and custom dashboards. Native web apps can be integrated on Polly - like Phantasus or CellxGene to analyze data arrays. Polly also provides data in extendable data models that can be streamed into applications of choice like Spotfire, Tableau, etc. Our experts help build your own or customize and deploy production-ready proprietary applications to run research-specific analyses on our secure cloud platform. 

Case Studies

Let’s visit two compelling case studies that illustrate the real-world impact of Polly in early-stage R&D. 

Case-study 1: Biomarker Data Curation and Management with Polly

1. A Boston-based clinical-stage company working on novel immunotherapies against cancer approached Elucidata with a data curation problem. Their team receives biomarker data from CROs and other assays, which lack consistent formatting.
2. Their specific challenges included dealing with high data volume, data heterogeneity lacking consistent metadata, and inconsistent data storage formats.
3. Elucidata applied robust data harmonization to these challenges to aggregate diverse datasets into a central location, and developed interactive dashboards for cross-cohort comparisons and other downstream analyses. All the datasets were cleaned and linked with relevant clinical metadata to produce analysis-ready data. 
4. This multi-pronged approach reduced the analysis time by 25 times. The dashboards reduced the intense curation process to weeks instead of months. 

Read the full case-study here. 

Case-study 2: Polly for Gene Perturbation Target Identification and Validation

1. An early-stage US-based pharmaceutical company approached Elucidata to study the effects of gene perturbation on cell fate conversion in connection with their research to develop a novel treatment for widespread health disorders. 
2. They required to curate datasets from diverse sources and experiments - single cell, transcriptomics, proteomics, and metabolomics data, to identify regulatory switches, analyze the effects of relevant transcription factors, and validate those specific targets in cell fate reprogramming. 
3. Elucidata collated and curated metadata fields of interest at the dataset level (13 fields) and sample level (15 fields) for all 50 datasets, and harmonized each dataset, to make them ready for downstream analysis. We extracted relevant regulatory switches from the sequencing data, and processed data to create a CellOracle object with information of different cell types. We analyzed this object to study Gene Regulatory Networks and predicted targets of cell fate reprogramming.
4. In this case, data harmonization and analysis accelerated the research timeline - custom curation of 50 datasets in a brief period of 2 months. Elucidata identified 2 targets for cell fate reprogramming, and our custom infrastructure and pipelines allowed validation of the targets in only 5-6 months.

Read the full case-study here. 

Polly, a Leader in Data Harmonization

The benefits of data harmonization in early-stage R&D are endless. Polly, as a pioneering platform by Elucidata, not only accelerates research timelines but also expands the possibilities of high-throughput molecular research. From eliminating redundant efforts to fostering collaborative analysis and facilitating breakthroughs in diagnostics and therapeutics, Polly's impact resonates across every stage of omics research. As we continue to explore and leverage the power of data harmonization, new possibilities unfold, transforming the way we approach and advance scientific discoveries in the dynamic field of molecular research.

Join the community of researchers who have embraced Polly and experience the power of unified, harmonized RNA-seq data analysis. Connect with us or reach out to us at info@elucidata.io to learn more.