Thu, 09/04/2014
Customer asks a general question about what methods should be used to analyze RNA-seq data.
Thu, 09/04/2014 at 6:01 AM
Customer: We would be interested in bioinformatics support for single cell genomics for defined research questions (e.g. based on single cell mRNA seq data).
Thu, 09/04/2014 at 10:36 AM
AccuraScience LB: Could you help me understand the domain of interest a little more? The term "single-cell genomics" is often used to refer to metagenomics domains where difficult-to-culture strains are amplified using non-PCR-based procedures... But you mentioned mRNA seq, so I tend to guess that you are referring to single-cell RNA-seq, the analysis of which involves separating the cells into subpopulations (PCA-based methods are frequently used), and more "advanced" analysis could involve the tracing of the "evolutionary" paths for the cells within a heterogeneous population. Are these the types of analysis you are interested in having us help with?
Thu, 09/04/2014 at 10:42 AM
Customer: Spot on - quantification of tumour cell heterogeneity, lineage tracing , evolutionary paths, and genetic networks (based on correlation hierarchies, e.g. influenced by different levels of knockdown in may individual cells rather than a population) is what we are interested in – of course, to start with, we would need an excellent primary analysis (from raw Illumina seq. data).
Thu, 09/04/2014 at 11:16 AM
AccuraScience LB: Indeed many of the techniques used in this domain have been used in other related and unrelated areas, e.g, those applied to build genetic networks were around in the microarray era - I suspect some new methods are being developed, however, and they will show up in the literature in the next few months. There are challenges specific to single-cell RNA-seq, e.g., frequently some genes are completely dropped out in the expression profiles... Generally, we are confident in handling this line of work.
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Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
Disclaimer: This text was selected and edited based on genuine communications that took place between a customer and AccuraScience data analysis team at specified dates and times. The editing was made to protect the customer’s privacy and for brevity. The edited text may or may not have been reviewed and approved by the customer. AccuraScience is solely responsible for the accuracy of the information reflected in this text.