Single-cell Data on Polly| Elucidata

1. Single-cell Data on Polly

2. Polly by Elucidata Elucidata’s data harmonization platform- Polly, delivers the highest quality single cell data to fit diverse analysis methods & pipelines. All datasets are Polly Verified, i.e harmonized with a configurable, granular & transparent curation process

3. Streamlined Journey to Improving Quality of Single-cell Data Data on Polly Polly Harmonization Processing Cell Annotation Data at Source Metadata Harmonization Quality Assurance Txt File Tabular file (MTX, CSV) • 50%Missing annotations • <2%Harmonized • Different access nuances • Formats vary across datasets & samples • <1% Missing annotations • 100%Harmonized • 4XNew fields added • ConsistentH5AD format

4. Single-Cell Data Options on Polly

5. Why Access Single-Cell Data on Polly? Data You Can Trust Complete Transparency Customizable Harmonization Flexible Ways to Consume Data ~50 QA checks performed on all data/metadata to ensure quality and provenance. Learn how each dataset was processed and annotated with comprehensive QA reports. Request custom metadata fields or cell type annotation with your own markers. Work with Polly’s data on tools and environments of your choice. No download restrictions applied!

6. How We Deliver: Data Concierge Data Audits • Experts identify datasets relevant to your research on/off Polly • Requirement gathering for curation & processing of found data Exploration and Analysis • Explore on Polly via CellxGene • Download data with Polly’s APIs or GUI, explore on tools of choice • Customized solutions as service: GSEA, Knowledge Graphs, ML Classifiers and Dashboards for analysis & visualization Store in your Atlas • Domain specific repository of Analysis-Ready data • All datasets are QC-ed, Custom Curated & Polly Verified

7. Target Identification & Validation with Curated Single-cell Data: Case-study About the Customer A therapeutics is an early stage startup based in Boston that is developing biologics for inflammatory and autoimmune diseases. The company was looking to identify potential targets for these indications. Objective Find and integrate single cell datasets specific to inflammatory diseases from public sources.Perform meta-analysis to arrive at fibroblast specific gene targets for further exploration.

8. Finding Relevant Datasets

9. How Was the Data Processed? Cell Type Annotation Store on Atlas Filtering & Normalization Unfiltered Raw Counts Data at Source mtx, csv, tsv, h5ad, seurat, h5 H5AD files with Hugo symbols, QC metrics, curated metadata fields Consistent filtering criteria, normalization & Batch effect correction Marker list from publications to derive cell annotations h5AD with curated metadata and consistently annotated cells

10. Meta-Analysis for Target Identification and Validation 13 datasets identified and 3 datasets merged B cells Integrated Cell Type Stem or Enterocyte cells T cells Plasma Mast cells Other Diseased Normal Fibroblast Other Fibroblast Myeloid cells Vascular cells Fibroblasts UMAP 2 Differential expression analysis of merged data to get top 250 DEGs UMAP 1 Gene 1 Refine results to top 20 genes with RF model and point biserial scores Examine expression and narrow down to 10 genes Review literature and perform pathway analysis to arrive at 5 targets

11. Single Cell Data Curation Impact 156 scRNA-Seq datasets, specific to inflammatory diseases were identified and annotated with relevant metadata information Target Identification & Validation Shortlisted 4 novel targets and validated 5 pre-identified targets using meta-analysis Time Savings 4X acceleration in the target identification process (from 8-10 months to 2.5 months)

12. Reach out to us at info@elucidata.io or Book a Demo with us to learn more.