37,785 research outputs found
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.
BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs
State-of-the-art image-set matching techniques typically implicitly model
each image-set with a Gaussian distribution. Here, we propose to go beyond
these representations and model image-sets as probability distribution
functions (PDFs) using kernel density estimators. To compare and match
image-sets, we exploit Csiszar f-divergences, which bear strong connections to
the geodesic distance defined on the space of PDFs, i.e., the statistical
manifold. Furthermore, we introduce valid positive definite kernels on the
statistical manifolds, which let us make use of more powerful classification
schemes to match image-sets. Finally, we introduce a supervised dimensionality
reduction technique that learns a latent space where f-divergences reflect the
class labels of the data. Our experiments on diverse problems, such as
video-based face recognition and dynamic texture classification, evidence the
benefits of our approach over the state-of-the-art image-set matching methods
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
We introduce a data-driven forecasting method for high-dimensional chaotic
systems using long short-term memory (LSTM) recurrent neural networks. The
proposed LSTM neural networks perform inference of high-dimensional dynamical
systems in their reduced order space and are shown to be an effective set of
nonlinear approximators of their attractor. We demonstrate the forecasting
performance of the LSTM and compare it with Gaussian processes (GPs) in time
series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation
and a prototype climate model. The LSTM networks outperform the GPs in
short-term forecasting accuracy in all applications considered. A hybrid
architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is
proposed to ensure convergence to the invariant measure. This novel hybrid
method is fully data-driven and extends the forecasting capabilities of LSTM
networks.Comment: 31 page
Accurate and robust image superresolution by neural processing of local image representations
Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method
Subspace clustering of dimensionality-reduced data
Subspace clustering refers to the problem of clustering unlabeled
high-dimensional data points into a union of low-dimensional linear subspaces,
assumed unknown. In practice one may have access to dimensionality-reduced
observations of the data only, resulting, e.g., from "undersampling" due to
complexity and speed constraints on the acquisition device. More pertinently,
even if one has access to the high-dimensional data set it is often desirable
to first project the data points into a lower-dimensional space and to perform
the clustering task there; this reduces storage requirements and computational
cost. The purpose of this paper is to quantify the impact of
dimensionality-reduction through random projection on the performance of the
sparse subspace clustering (SSC) and the thresholding based subspace clustering
(TSC) algorithms. We find that for both algorithms dimensionality reduction
down to the order of the subspace dimensions is possible without incurring
significant performance degradation. The mathematical engine behind our
theorems is a result quantifying how the affinities between subspaces change
under random dimensionality reducing projections.Comment: ISIT 201
Mapping hybrid functional-structural connectivity traits in the human connectome
One of the crucial questions in neuroscience is how a rich functional
repertoire of brain states relates to its underlying structural organization.
How to study the associations between these structural and functional layers is
an open problem that involves novel conceptual ways of tackling this question.
We here propose an extension of the Connectivity Independent Component Analysis
(connICA) framework, to identify joint structural-functional connectivity
traits. Here, we extend connICA to integrate structural and functional
connectomes by merging them into common hybrid connectivity patterns that
represent the connectivity fingerprint of a subject. We test this extended
approach on the 100 unrelated subjects from the Human Connectome Project. The
method is able to extract main independent structural-functional connectivity
patterns from the entire cohort that are sensitive to the realization of
different tasks. The hybrid connICA extracted two main task-sensitive hybrid
traits. The first, encompassing the within and between connections of dorsal
attentional and visual areas, as well as fronto-parietal circuits. The second,
mainly encompassing the connectivity between visual, attentional, DMN and
subcortical networks. Overall, these findings confirms the potential ofthe
hybrid connICA for the compression of structural/functional connectomes into
integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5
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