Single cell RNA-Seq (scRNA-Seq) is a powerful tool for measuring gene expression across the transcriptome at the single cell level, allowing the detection of changes occurring in disease states, in response to therapeutics, under different environmental conditions, and across a broad range of other study designs.
Our standard service includes alignment of short reads to an appropriate genome, data quality control, feature selection, normalization, identifying dimensionality of datasets, marker-based cell population identification, differentially expressed genes between cell populations (clusters), and recommendations for downstream analysis.
Due to the complex nature of scRNA-Seq data, our service includes a consultation just prior to identifying cell populations in order to determine whether to proceed or not. In addition, based on this consultation, we will be able to determine what downstream analysis options are available based on the state of the data.
- Quality control of raw sequencing reads1
- Alignment to standard reference genome and mapping to appropriate gene annotations1
- Gene-level abundance measurements1
- Determination of sample outliers and/or batch effects, as well as downstream strategy
- Data normalization (read depth and transcript length), on per-sample or per-dataset
- Identification of cell populations, based on expected cell markers provided by PI
- List of most-variant genes per cluster
- Differential expression testing for cluster pairs (to be determined after identification and group review)
Extended services are available, which supplement our standard/fixed workflow described above:
- Acquiring data from 3rd party services, such as ArrayExpress or GEO
- Iterative customization of results and plots for presentations, publications, grants, etc...
- Batch correction and modeling
- Integration with other data
Footnotes:
- 1. Optional data processing step depending on scRNA-Seq platform