187,984 research outputs found
In Silico Synchronization of Cellular Populations Through Expression Data Deconvolution
Cellular populations are typically heterogenous collections of cells at
different points in their respective cell cycles, each with a cell cycle time
that varies from individual to individual. As a result, true single-cell
behavior, particularly that which is cell-cycle--dependent, is often obscured
in population-level (averaged) measurements. We have developed a simple
deconvolution method that can be used to remove the effects of asynchronous
variability from population-level time-series data. In this paper, we summarize
some recent progress in the development and application of our approach, and
provide technical updates that result in increased biological fidelity. We also
explore several preliminary validation results and discuss several ongoing
applications that highlight the method's usefulness for estimating parameters
in differential equation models of single-cell gene regulation.Comment: accepted for the 48th ACM/IEEE Design Automation Conferenc
Neural system identification for large populations separating "what" and "where"
Neuroscientists classify neurons into different types that perform similar
computations at different locations in the visual field. Traditional methods
for neural system identification do not capitalize on this separation of 'what'
and 'where'. Learning deep convolutional feature spaces that are shared among
many neurons provides an exciting path forward, but the architectural design
needs to account for data limitations: While new experimental techniques enable
recordings from thousands of neurons, experimental time is limited so that one
can sample only a small fraction of each neuron's response space. Here, we show
that a major bottleneck for fitting convolutional neural networks (CNNs) to
neural data is the estimation of the individual receptive field locations, a
problem that has been scratched only at the surface thus far. We propose a CNN
architecture with a sparse readout layer factorizing the spatial (where) and
feature (what) dimensions. Our network scales well to thousands of neurons and
short recordings and can be trained end-to-end. We evaluate this architecture
on ground-truth data to explore the challenges and limitations of CNN-based
system identification. Moreover, we show that our network model outperforms
current state-of-the art system identification models of mouse primary visual
cortex.Comment: NIPS 201
BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.
We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before
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