Daniela Witten
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View article: Long-read transcriptome analysis using IsoRanker for identifying pathogenic variants in Mendelian conditions
Long-read transcriptome analysis using IsoRanker for identifying pathogenic variants in Mendelian conditions Open
Identifying pathogenic non-coding variants that contribute to Mendelian conditions remains challenging as the functional impact of these variants on gene function is often unknown. We present IsoRanker, a long-read transcriptome sequencing…
View article: Post-selection inference with a single realization of a network
Post-selection inference with a single realization of a network Open
Given a dataset consisting of a single realization of a network, we consider conducting inference on a parameter selected from the data. In particular, we focus on the setting where the parameter of interest is a linear combination of the …
View article: Inference on the Proportion of Variance Explained in Principal Component Analysis
Inference on the Proportion of Variance Explained in Principal Component Analysis Open
Principal component analysis (PCA) is a longstanding and well-studied approach for dimension reduction. It rests upon the assumption that the underlying signal in the data has low rank, and thus can be well-summarized using a small number …
View article: Valid F-screening in linear regression
Valid F-screening in linear regression Open
Suppose that a data analyst wishes to report the results of a least squares linear regression only if the overall null hypothesis, $H_0^{1:p}: β_1= β_2 = \ldots = β_p=0$, is rejected. This practice, which we refer to as F-screening (since …
View article: Lateral Hypothalamic Glutamate and GABA Neurons Cooperatively Shape Striatum-Wide Dopamine Dynamics During Consumption
Lateral Hypothalamic Glutamate and GABA Neurons Cooperatively Shape Striatum-Wide Dopamine Dynamics During Consumption Open
SUMMARY The lateral hypothalamic area (LHA) contains GABAergic and glutamatergic neurons that converge on the midbrain dopamine system and exert opposing influences on consummatory feeding behavior. However, the activity dynamics of these …
View article: A Unified Framework for Semiparametrically Efficient Semi-Supervised Learning
A Unified Framework for Semiparametrically Efficient Semi-Supervised Learning Open
We consider statistical inference under a semi-supervised setting where we have access to both a labeled dataset consisting of pairs $\{X_i, Y_i \}_{i=1}^n$ and an unlabeled dataset $\{ X_i \}_{i=n+1}^{n+N}$. We ask the question: under wha…
View article: Thinning a Wishart Random Matrix
Thinning a Wishart Random Matrix Open
Recent work has explored data thinning, a generalization of sample splitting that involves decomposing a (possibly matrix-valued) random variable into independent components. In the special case of a $n \times p$ random matrix with indepen…
View article: Author response: Valence and salience encoding in the central amygdala
Author response: Valence and salience encoding in the central amygdala Open
View article: Valence and salience encoding in the central amygdala
Valence and salience encoding in the central amygdala Open
The central amygdala (CeA) has emerged as an important brain region for regulating both negative (fear and anxiety) and positive (reward) affective behaviors. The CeA has been proposed to encode affective information in the form of valence…
View article: Author response: Valence and Salience Encoding in the Central Amygdala
Author response: Valence and Salience Encoding in the Central Amygdala Open
View article: Valence and Salience Encoding in the Central Amygdala
Valence and Salience Encoding in the Central Amygdala Open
The central amygdala (CeA) has emerged as an important brain region for regulating both negative (fear and anxiety) and positive (reward) affective behaviors. The CeA has been proposed to encode affective information in the form of valence…
View article: Author response: Valence and Salience Encoding in the Central Amygdala
Author response: Valence and Salience Encoding in the Central Amygdala Open
View article: Valence and salience encoding in the central amygdala
Valence and salience encoding in the central amygdala Open
The central amygdala (CeA) has emerged as an important brain region for regulating both negative (fear and anxiety) and positive (reward) affective behaviors. The CeA has been proposed to encode affective information in the form of valence…
View article: Valence and Salience Encoding in the Central Amygdala
Valence and Salience Encoding in the Central Amygdala Open
The central amygdala (CeA) has emerged as an important brain region for regulating both negative (fear and anxiety) and positive (reward) affective behaviors. The CeA has been proposed to encode affective information in the form of valence…
View article: Decomposing Gaussians with Unknown Covariance
Decomposing Gaussians with Unknown Covariance Open
Common workflows in machine learning and statistics rely on the ability to partition the information in a data set into independent portions. Recent work has shown that this may be possible even when conventional sample splitting is not (e…
View article: Discussion of "Data fission: splitting a single data point"
Discussion of "Data fission: splitting a single data point" Open
Leiner et al. [2023] introduce an important generalization of sample splitting, which they call data fission. They consider two cases of data fission: P1 fission and P2 fission. While P1 fission is extremely useful and easy to use, Leiner …
View article: Infer-and-widen, or not?
Infer-and-widen, or not? Open
In recent years, there has been substantial interest in the task of selective inference: inference on a parameter that is selected from the data. Many of the existing proposals fall into what we refer to as the \emph{infer-and-widen} frame…
View article: Valence and Salience Encoding in the Central Amygdala
Valence and Salience Encoding in the Central Amygdala Open
The central amygdala (CeA) has emerged as an important brain region for regulating both negative (fear and anxiety) and positive (reward) affective behaviors. The CeA has been proposed to encode affective information in the form of valence…
View article: A haplotype-resolved view of human gene regulation
A haplotype-resolved view of human gene regulation Open
Diploid human cells contain two non-identical genomes, and differences in their regulation underlie human development and disease. We present Fiber-seq Inferred Regulatory Elements (FIRE) and show that FIRE provides a more comprehensive an…
View article: Modeling functional cell types in spike train data
Modeling functional cell types in spike train data Open
A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by groupi…
View article: Negative binomial count splitting for single-cell RNA sequencing data
Negative binomial count splitting for single-cell RNA sequencing data Open
The analysis of single-cell RNA sequencing (scRNA-seq) data often involves fitting a latent variable model to learn a low-dimensional representation for the cells. Validating such a model poses a major challenge. If we could sequence the s…
View article: Revisiting inference after prediction
Revisiting inference after prediction Open
Recent work has focused on the very common practice of prediction-based inference: that is, (i) using a pre-trained machine learning model to predict an unobserved response variable, and then (ii) conducting inference on the association be…
View article: Condition-dependent fitness effects of large synthetic chromosome amplifications
Condition-dependent fitness effects of large synthetic chromosome amplifications Open
Whole-chromosome aneuploidy and large segmental amplifications can have devastating effects in multicellular organisms, from developmental disorders and miscarriage to cancer. Aneuploidy in single-celled organisms such as yeast also result…
View article: Generalized Data Thinning Using Sufficient Statistics
Generalized Data Thinning Using Sufficient Statistics Open
Our goal is to develop a general strategy to decompose a random variable $X$ into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper showed that for some well-known natural e…
View article: Highly Parallel Tissue Grafting for Combinatorial In Vivo Screening
Highly Parallel Tissue Grafting for Combinatorial In Vivo Screening Open
Material- and cell-based technologies such as engineered tissues hold great promise as human therapies. Yet, the development of many of these technologies becomes stalled at the stage of pre-clinical animal studies due to the tedious and l…
View article: Modeling functional cell types in spike train data
Modeling functional cell types in spike train data Open
A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using functional cell types to refine single-cell models by groupi…
View article: Viral inhibitory nucleotide sequences and vaccines
Viral inhibitory nucleotide sequences and vaccines Open
The invention relates to inhibitory nucleotide signal sequences or “INS” sequences in the genomes of lentiviruses. In particular the invention relates to the AGG motif present in all viral genomes. The AGG motif may have an inhibitory effe…
View article: Recoding method that removes inhibitory sequences and improves HIV gene expression
Recoding method that removes inhibitory sequences and improves HIV gene expression Open
The invention relates to inhibitory nucleotide signal sequences or "INS" sequences in the genomes of lentiviruses. In particular the invention relates to the AGG motif present in all viral genomes. The AGG motif may have an inhibitory effe…
View article: Data thinning for convolution-closed distributions
Data thinning for convolution-closed distributions Open
We propose data thinning, an approach for splitting an observation into two or more independent parts that sum to the original observation, and that follow the same distribution as the original observation, up to a (known) scaling of a par…
View article: Inference after latent variable estimation for single-cell RNA sequencing data
Inference after latent variable estimation for single-cell RNA sequencing data Open
Summary In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, representing some aspect of the cell’s state. They the…