Single cell analytics

Pros, cons & Future potential

• - Single-cell sequencing data is extremely rich in information, and this complexity comes with computational and analytical challenges.
• - BioLizard has developed customizable in-house pipelines that support the analysis of multi(modal)-omics single-cell data from a variety of different library preparation platforms. The modular nature of our platform allows for easy customization of the analysis workflow to suit your unique research setting.
• - Moreover, our data exploration platform allows you to interactively visualize and investigate your results through an intuitive interface. We always ensure to align regularly with our clients to ensure that it’s clear how your data has been handled, and we maintain the highest standards for data security and privacy.

Single-cell sequencing allows you to dive deeper into the biology of cells and research them on an individual level. This generates more data to process. To put things in perspective: bulk sequencing technology captures the average of a mixture of cells. Single-cell sequencing, on the other hand, can capture the transcriptome of up to 20 000 single cells. This implies a 20 000 fold increase in information to process, and this only considering transcriptomic data! This increase in data creates some challenges, like:

• (1) Computational challenges: Do you have the processing power? Or the storage? Depending on the size of the dataset, you’ll need 32 or up to 64 GB RAM and enough storage (sometimes up to 1TB). On top of that, it could be smart to have multiple CPUs to speed things up. We doubt the average office laptop could handle these requirements.
• (2) Challenges with analytics: One thing is that it gets harder to pinpoint what’s important and what’s not when you have more information. Another thing is the risk of using the wrong statistical assumptions, which form false conclusions. As opposed to bulk sequencing methods, workflows for single-cell analysis are complex. It requires a certain set of statistical skills to identify the right approach.
• (3) Challenges with experimental design: The experimental and computational departments often function as two separate entities within an organization. Both parties should interact and collaborate to obtain most of your single-cell sequencing data: starting from the experimental design! Regardless of the biological question that you might want to ask, computational and wet-lab professionals are there to help you build an approach to avoid lacking statistical power in the analysis.
• (4) Challenges with interpretation: A large amount of data also makes interpretation difficult. Once data is processed, cleaned, filtered, and suitable analytical algorithms have been applied, how can someone correctly interpret this information? The (bio)-informatician – while capable in coding and analytics – often lacks the deep knowledge into the biological matters provided by researchers.