High dimensional single cell gene expression data and batch effect corrections

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Laleh Haghverdi

EMBL Heidelberg, Germany

: J Comput Eng Inf Technol

Abstract

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Emerging about a decade ago, single cell genes expression measurement technologies have facilitated the study of heterogeneous populations of cells such as in development and cell differentiation. Single cell ribonucleic acid sequencing (scRNA-seq) techniques can measure the expression level of several thousand genes at the single cell level for millions of cells. Increasingly used by several laboratories, the technique provides a big amount of data which opens new opportunities for knowledge extraction using new machine learning and computational methods. The author will discuss the properties of highdimensional data which needs to be taken care of when dealing with such big expression data, and discuss in an instance on how high-dimension properties allowed us to develop a new method for batch effects correction and data integration across several laboratories.

Biography

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