Single Cell Datasets
PandaOmics hosts a comprehensive collection of over 1,000 single-cell RNA sequencing (scRNA-seq) datasets, covering a wide spectrum of therapeutic areas. These datasets enable researchers to explore cellular heterogeneity and identify targets with greater precision than bulk sequencing alone.
Why Use Single-Cell Data in PandaOmics?
While traditional bulk sequencing provides an average of gene expression across a tissue, single-cell analysis offers high-resolution insights that are critical for modern drug discovery:
- Single-cell techniques reveal distinct cell subtypes and their gene expression profiles, which are unavailable in bulk measurements. This allows to identify genes with differential expression specific to certain cell types or disease states.
- The technology enables the unbiased detection of rare cell types that may drive pathobiology, such as cancer stem cells or specific immune subsets. This is essential for understanding the tumor microenvironment and mechanisms of immune evasion.
- The technology enables the unbiased detection of rare cell types that may drive pathobiology, such as cancer stem cells or specific immune subsets. This is essential for understanding the tumor microenvironment and mechanisms of immune evasion.
- By profiling patient samples at the single-cell level, it is possible to identify cell-specific biomarkers. This facilitates precise patient stratification, helping to target populations most likely to respond to a particular treatment.
Seamless Integration into Workflow
Single-cell datasets are fully integrated into the PanSearch. When searching for a specific indication, relevant single-cell studies appear in the results alongside traditional bulk datasets.
Single-cell datasets are treated with the same intuitive workflow as standard datasets. Users can:
Single-cell datasets are treated with the same intuitive workflow as standard datasets. Users can:
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Define Sample Groups based on specific cell types or metadata.
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Build Comparisons (e.g., Disease vs. Healthy)
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Perform downstream tasks such as identification of affected pathways and Target ID.
Pseudobulking
for Differential Expression
for Differential Expression
To ensure robust differential gene expression analysis, PandaOmics utilizes pseudobulking. This technique aggregates gene expression values from individual cells within a defined cluster (such as a cell type or donor) to create virtual pseudobulk samples. This approach reduces technical noise, lowers computational load, and increases the statistical power of the analysis.