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Single-cell service: Frequently Asked Questions

1. What is included in a standard package?

Data sent to us will be processed through standard pipeline (DropSeqPipe or equivalent). We will also conduct preliminary clustering and preliminary differential expression analysis for you. You will receive raw data, QC metric data, and all graphs generated.

2. What are the steps taken for QC?

Our pipelines contains a strict QC protocol.

Over all, you can expect the following QC metrics:

    • Cell counts (number of unique UMIs)
    • UMI count per cell
    • Genes detected per cell
    • UMIs vs genes detected
    • Mitochondrial count ratio
    • Novelty
    • Reads per cell

3. How much time will it take to get my results back?

The exact timeline will be based on individual experimental goals, file size, and the scheduling queue on our side. At the minimum, please give us one week.

4. Is there an option for additional analysis beyond what is offered in the package?

Yes! We charge hourly rate for additional analyses should you need to collaborate with us on something that is not yet included in the standard package. Email us at so you could be referred to one of the bioinformaticians on the team.

5. What happens if the raw data is not up to standard?

Fret not! This is why we need QC. When we convert your FASTQ file to a count matrix, we also conduct multiple steps of QC to make sure the data is acceptable. In the event that some mishaps happened during your sequencing and the data is not up to standard, we would show you the QC report during the mid-point of the project to inform you of the situation and our recommendation.

If both our team and you agree that the data is not suitable to move forward, we would stop the project at this checkpoint. You will only be charged a partial price, covering the services rendered.

If both our team and you agree that despite its limited quality, the data should be moved forward in the pipeline, you will need to agree to our Services Disclaimer before we proceed. This document marks that we have had a discussion on the matter and have reached a mutual agreement on the best course for the data.

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