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"Achieving 100 Percent Automation and Seamless Scalability with a Cost Saving of ~$1.34M in Single-Cell Data Compute and Processing"

This case study from Elucidata details how a US-based cancer diagnostics company partnered with Elucidata to streamline their data management workflow -- from ingestion, preprocessing, and curation to insight generation -- for raw single-cell multiome data to identify diagnostic markers for AML.

Their research team utilized the Google Cloud Platform (GCP) to store and use/reuse the raw data. However, the process of uploading large files from the sequencer to GCP, processing them, and subsequently downloading them for additional analysis proved to be laborious and error-prone, requiring redundant effort. Moreover, the absence of standardized naming conventions or classifiers for data storage made it challenging to meet the company's evolving data requirements. They were in dire need of a reliable and efficient data management system.

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