Mon. 10 Mar, 2014 - Sun. 16 Mar, 2014
Mon. 10 Mar, 2014
Post-doc Seminar Kerstin Richau: Frankia & me: An atypical relationship
Mon. 10 Mar, 2014 10:00 - 11:00
UPSC Seminar Series 2014
Post-doc Seminar
Kerstin Richau
postdoc
Title:
Frankia & me:
An atypical relationship
Room KB3A9
Host AS
Post-doc Seminar
Kerstin Richau
postdoc
Title:
Frankia & me:
An atypical relationship
Room KB3A9
Host AS
Wed. 12 Mar, 2014
Seminar - John Marioni: Computational challenges in single-cell transcriptomics – from immune cells to neurons
Wed. 12 Mar, 2014 14:00 - 15:00
UPSC Seminar/CLiC/BILS seminar
Speaker:
John Marioni
EMBL-EBI/Wellcome Trust Sanger Institute/Single-Cell Genomics Centre (http://www.ebi.ac.uk/research/marioni
Title:
Computational challenges in single-cell transcriptomics – from immune cells to neurons
Room:
Lilla Hörsalen KB3A9
Abstract:
Recent technical developments, facilitated in large part by work at the Karolinska Institutet, have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner. These approaches have enabled heterogeneity in gene expression levels across populations of cells to be characterized as well as facilitating the identification of new, and potentially physiologically relevant, sub-populations of cells.
However, to fully exploit such data and to answer these questions, it is necessary to develop robust computational methods that take account of both technical noise and underlying, potentially confounding, variables such as the cell cycle.
In this presentation I will begin by briefly describing how we used spike-ins to quantify technical noise in single-cell RNA-seq data, thus facilitating identification of genes with more variation in expression levels across cells than expected by chance. Subsequently, I will discuss a computational approach that uses latent variable models to account for potentially confounding factors such as the cell cycle before applying it to study the differentiation of Th2 cells. I will show that accounting for cell-to-cell correlations due to the cell cycle allows identification of otherwise obscured sub-populations of cells that correspond to different stages along the path to fully differentiated Th2 cells.
To conclude, I will discuss further applications of single-cell RNA-seq in the context of studying neuronal cell types, including olfactory neurons. I will also describe how we are studying heterogeneity in gene expression levels whilst taking into account the spatial location of cells from the tissue under study.
Welcome!
If you’re interested in meeting personally with John, contact Jeanette Tångrot and Nicolas Delhomme ( jeanette.tangrot at molbiol.umu.se and nicolas.delhomme at umu.se) as we have a few time slots available during the day.
Speaker:
John Marioni
EMBL-EBI/Wellcome Trust Sanger Institute/Single-Cell Genomics Centre (http://www.ebi.ac.uk/research/marioni
Title:
Computational challenges in single-cell transcriptomics – from immune cells to neurons
Room:
Lilla Hörsalen KB3A9
Abstract:
Recent technical developments, facilitated in large part by work at the Karolinska Institutet, have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner. These approaches have enabled heterogeneity in gene expression levels across populations of cells to be characterized as well as facilitating the identification of new, and potentially physiologically relevant, sub-populations of cells.
However, to fully exploit such data and to answer these questions, it is necessary to develop robust computational methods that take account of both technical noise and underlying, potentially confounding, variables such as the cell cycle.
In this presentation I will begin by briefly describing how we used spike-ins to quantify technical noise in single-cell RNA-seq data, thus facilitating identification of genes with more variation in expression levels across cells than expected by chance. Subsequently, I will discuss a computational approach that uses latent variable models to account for potentially confounding factors such as the cell cycle before applying it to study the differentiation of Th2 cells. I will show that accounting for cell-to-cell correlations due to the cell cycle allows identification of otherwise obscured sub-populations of cells that correspond to different stages along the path to fully differentiated Th2 cells.
To conclude, I will discuss further applications of single-cell RNA-seq in the context of studying neuronal cell types, including olfactory neurons. I will also describe how we are studying heterogeneity in gene expression levels whilst taking into account the spatial location of cells from the tissue under study.
Welcome!
If you’re interested in meeting personally with John, contact Jeanette Tångrot and Nicolas Delhomme ( jeanette.tangrot at molbiol.umu.se and nicolas.delhomme at umu.se) as we have a few time slots available during the day.