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Using the app, biopharmaceutical developers can break through the data wrangling and programming challenges associated with the analysis of large-scale, single-cell datasets.
The REVEAL: Single Cell app from Paradigm4, a provider of scientific information solutions, allows for biopharmaceutical developers to address the data wrangling and programming challenges associated with the analysis of large-scale, single-cell datasets.
Using Paradigm4’s Agile Science engine, SciDB, the app uses vectors and multidimensional arrays to support scientific data modeling, storage, and large-scale computation. Its elastic computing platform is a parallel, transaction-safe, array-oriented, analytics solution which offers users access to disease biology, samples from patients with more cells, key biological hypotheses for target evaluation, disease progression, and precision medicine.
Additionally, users have the ability to select cells of interest across all studies using individual metadata tags to assess tissue distribution, variance in response to treatment, and for comparisons of normal to diseased cells. Datasets can also be organized according to individual users’ data models.
“When Covid-19 hit earlier this year, we used our REVEAL: Single Cell app to identify tissues expressing the key SARS-CoV-2 entry associated genes in seconds,” said Zachary Pitluk, vice-president of Life Sciences and Healthcare at Paradigm4, in a Sept. 15, 2020 press release. “We found they were expressed in multiple tissue types, thus explaining the multi-organ involvement in infected patients observed worldwide during the ongoing pandemic.”
“With the launch of REVEAL: Single Cell, we support scientists in breaking through the complexities of working with massive single cell, multi-patient datasets,” added Marilyn Matz, co-founder and CEO of Paradigm4, in the press release. “Accelerating drug and biomarker discovery is a key driver for our customers. Our Agile Science engine, SciDB, with its REVEAL apps, transforms the way researchers integrate, share, and gain insights from multimodal scientific data.”