CASE STUDY

Accelerate Cell-Type Annotation of scRNA-seq Data

Key Highlights

Cell type identification, a vital step for scRNA-seq experiments, can be tedious and erroneous.
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Retrieval of raw data from public repositories and literature search for cell-type markers are both cumbersome.
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Manual annotation of cell types is slow and it offers limited reproducibility.
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A reproducible bioinformatics pipeline to identify cell types was built using Polly and validated.

All Case Studies

Oncology Company Achieves ~80% Acceleration in Gene Target ID/ Validation

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Polly PeakML Accelerates Biological Insight Derivation from Untargeted Metabolomics Analysis

Cutting- Edge Cancer Tx Accelerated Target ID by 75% using ML & Curated Biomolecular Data

Cloud Processing of Metabolomics Data

Accelerate Cell-Type Annotation of scRNA-seq Data

Predicting Survivability of Patients with Liver Hepatocellular Carcinoma

Integrated Metabolomics and Transcriptomics Profiling

Network Integration of Parallel Metabolic and Transcriptional Data Reveals Metabolic Modules that Regulate Macrophage Polarization

Why Polly CRISPR Screening is Better than MAGeCK VISPR

Other Resources

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