11am, GHC 6115
Junhyong Kim, University of Pennsylvania
Single Cell Variation and Cellular Phenotype
Recently, single cell RNA sequencing has revealed large variations in the molecular states of individual cells of seemingly the same type. We have been investigating single cell biology for the past five years and have collected over 1000 datasets from various organisms including human, mouse, rat, zebrafish, etc. Here, I will discuss some of the technical aspects of single cell RNA sequencing, our analysis of five different mouse cell types, and then noise and technical resolution problems with single cell transcriptome profiling. I will conclude with a discussion of the origins of single cell variation, suggesting that individual cells are more like individuals of an ecological community rather than uniform modular units.
11am, GHC 6115
Robert E. Kass, Carnegie Mellon University
Problems in the Analysis of Spiking Neuron Networks
Knowledge about the link between brain and behavior rests, in large part, on electrophysiological investigation of neural activity recorded from one or more electrodes that have been inserted into the brain of an animal. Technological advances have provided vastly improved data collection and storage capabilities, which present both opportunities and challenges. It is now common to record from dozens to hundreds of electrodes simultaneously, and it is also possible for these electrodes to maintain their position well enough to record the same neurons across hours or even days. Because many disorders, such as ADHD, autism, and schizophrenia, as well as stroke and various neurodegenerative diseases, are thought to involve dysfunction of network connectivity, a great hope has been that multi-electrode recording could reveal the way network activity evolves in healthy and diseased states, and thereby supply an important mechanistic description of pathophysiology. However, while the number of recording electrodes used in a single brain has been increasing exponentially fast, statistical methods for handling the complexity of multi-electrode data have lagged behind. In addition to the general problem of handling large-scale electrode recordings, a second major challenge comes from the striking observation that neural interactions occur at multiple timescales, including those involving oscillations and synchrony (the tendency of two or more neurons to fire at nearly the same time), which could provide an essential mechanism of neural network information flow and be a marker that distinguishes normal from diseased states.
Neurons communicate through rapid electrical discharges known as “spikes,” and sequences of spikes are known as “spike trains.” Because each spike occurs over the course of roughly 1 millisecond while behavior occurs over hundreds of milliseconds, it is reasonable to consider a spike train to be a stochastic sequence of isolated points in time, i.e., a point process. I will review the use of point processes to represent interactions of multiple neurons across different timescales. I will also go over a new method that is applicable to many network analyses: false discovery rate regression.