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COULD DROPPING A FEW CELLS CHANGE THE TAKEAWAYS FROM DIFFERENTIAL EXPRESSION?

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arXiv 2023年

作者： Shiffman, Miriam Giordano, Ryan Broderick, Tamara MIT Computational & Systems Biology Program United States MIT Dept. of Electrical Engineering & Computer Science United States UC Berkeley Dept. of Statistics United States

Analysis of differential gene expression plays a fundamental role in biology toward illuminating the molecular mechanisms driving a difference between groups (e.g., due to treatment or disease). While any analysis is run on particular cells or samples, the intent is to generalize to future occurrences of the treatment or disease. Implicitly, generalization is justified under the assumption that present and future samples are independent and identically distributed from the same population. Though this assumption is always false, we might hope that any deviation from the assumption is small enough that A) fundamental conclusions of the analysis still hold, and B) standard tools like standard error, significance, and power still reflect generalizability. Conversely, we might worry about these deviations, and reliance on standard statistical tools, if conclusions could be substantively changed by dropping a very small fraction of observations. While checking every small fraction is computationally intractable, recent work develops an approximation to identify when such an influential subset exists. Building on this work, we develop a metric for dropping-data robustness of differential expression;namely, we cast the analysis in a differentiable form suitable to the approximation, extend the approximation to models whose hyperparameters depend on the full dataset, and extend the notion of a data point from a single cell to a pseudobulk observation. We then overcome the inherent non-differentiability of gene set enrichment analysis to develop an additional approximation for the robustness of top gene sets. We use our tool to assess the robustness of differential expression for published single-cell RNA-seq data, and discover that thousands of genes can have their results flipped by dropping Copyright © 2023, The Authors. All rights reserved.

关键词： Diseases

Personalized Predictions from Population Level Experiments: A Study on Alzheimer’s Disease

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arXiv 2024年

作者： Shen, Dennis Agarwal, Anish Misra, Vishal Schelter, Bjoern Shah, Devavrat Shiells, Helen Wischik, Claude USC Department of Data Sciences & Operations United States Columbia University Department of Industrial Engineering & Operations Research United States Columbia University Department of Computer Science United States TauRx Therapeutics United Kingdom MIT Department of Electrical Engineering & Computer Science United States

The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages information across patients to impute missing data associated with each patient of interest. We focus on two types of missing data: (i) unrecorded outcomes from discontinuing the assigned treatments and (ii) unobserved outcomes associated with unassigned treatments. Data imputation in the former powers and de-biases RCTs, while data imputation in the latter simulates "synthetic RCTs" to predict the outcomes for each patient under every treatment. The SNN estimator is interpretable, transparent, and causally justified under a broad class of missing data scenarios. Relative to several standard methods, we empirically find that SNN performs well for the above two applications using Phase 3 clinical trial data on patients with Alzheimer’s Disease. Our findings directly suggest that SNN can tackle a current pain point within the clinical trial workflow on patient dropouts and serve as a new tool towards the development of precision medicine. Building on our insights, we discuss how SNN can further generalize to real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Patient treatment

Sample-Then-Optimize Batch Neural Thompson Sampling

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arXiv 2022年

作者： Dai, Zhongxiang Shu, Yao Low, Bryan Kian Hsiang Jaillet, Patrick Dept. of Computer Science National University of Singapore Singapore Dept. of Electrical Engineering and Computer Science MIT United States

Bayesian optimization (BO), which uses a Gaussian process (GP) as a surrogate to model its objective function, is popular for black-box optimization. However, due to the limitations of GPs, BO underperforms in some problems such as those with categorical, high-dimensional or image inputs. To this end, recent works have used the highly expressive neural networks (NNs) as the surrogate model and derived theoretical guarantees using the theory of neural tangent kernel (NTK). However, these works suffer from the limitations of the requirement to invert an extremely large parameter matrix and the restriction to the sequential (rather than batch) setting. To overcome these limitations, we introduce two algorithms based on the Thompson sampling (TS) policy named Sample-Then-Optimize Batch Neural TS (STO-BNTS) and STO-BNTS-Linear. To choose an input query, we only need to train an NN (resp. a linear model) and then choose the query by maximizing the trained NN (resp. linear model), which is equivalently sampled from the GP posterior with the NTK as the kernel function. As a result, our algorithms sidestep the need to invert the large parameter matrix yet still preserve the validity of the TS policy. Next, we derive regret upper bounds for our algorithms with batch evaluations, and use insights from batch BO and NTK to show that they are asymptotically no-regret under certain conditions. Finally, we verify their empirical effectiveness using practical AutoML and reinforcement learning experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

Plug-and-Play Drag Sail Module for LEO Satellites: Implementation and Early Testing of AirDragMod (ADM)

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arXiv 2024年

作者： Shukla, Anshuman Sawant, Pranav Department of Electrical & Electronics Engineering MIT Manipal Manipal University Karnataka Udupi576104 India Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Space debris has become an increasingly critical subject, with the amount of debris in orbit now exceeding the number of active operational satellites, posing significant risks to the sustainability of space activities. A major constituent of debris is a payload or rocket body discarded in orbit post-mission with no orbital control capacity (OCC) in LEO [1]. The Inter-Agency Space Debris Coordination Committee (IADC) guidelines recommend limiting the post-mission presence of a rocket body or payload in a protected region to 25 years [2]. Recently, the Federal Communications Commission (FCC) introduced stricter regulations, reducing the allowable post-mission stay to no more than 5 years for satellites in LEO [3]. These changes necessitate the integration of deorbiting systems in satellite designs. Adding extra fuel and engines for active thrust-induced deorbiting poses significant challenges. LEO satellites, especially those in large constellations or small CubeSats, i.e., the major composition of LEOIADC [2], are launched with strict mass and volume limitations. The limited payload capacity of launch vehicles necessitates prioritizing mission-critical components over supplementary systems like deorbiting mechanisms. Therefore, alternative approaches, such as passive deorbiting techniques or international regulatory measures, are often explored to address the issue of space debris in LEO. A well-known cost-effective passive deorbit solution in the field is drag sails owing to their effective method of deorbiting small and medium-sized satellites in LEO. In this paper, we propose a plug-and-play design of a drag sail module, different from the mechanical boom and electrical deployment methods for drag sails currently used, using commercial-off-the-shelf (COTS) components for CubeSats and satellites under 700 kg in mass for LEOIADC © 2024, CC BY-SA.

关键词： Space debris

Prefix-Tree Decoding for Predicting Mass Spectra from Molecules

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arXiv 2023年

作者： Goldman, Samuel Bradshaw, John Xin, Jiayi Coley, Connor W. Computational and Systems Biology MIT CambridgeMA02139 United States Chemical Engineering MIT CambridgeMA02139 United States Statistics and Actuarial Science The University of Hong Kong Pokfulam Hong Kong Chemical Engineering Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

Computational predictions of mass spectra from molecules have enabled the discovery of clinically relevant metabolites. However, such predictive tools are still limited as they occupy one of two extremes, either operating (a) by fragmenting molecules combinatorially with overly rigid constraints on potential rearrangements and poor time complexity or (b) by decoding lossy and nonphysical discretized spectra vectors. In this work, we use a new intermediate strategy for predicting mass spectra from molecules by treating mass spectra as sets of molecular formulae, which are themselves multisets of atoms. After first encoding an input molecular graph, we decode a set of molecular subformulae, each of which specify a predicted peak in the mass spectrum, the intensities of which are predicted by a second model. Our key insight is to overcome the combinatorial possibilities for molecular subformulae by decoding the formula set using a prefix tree structure, atom-type by atom-type, representing a general method for ordered multiset decoding. We show promising empirical results on mass spectra prediction tasks. © 2023, CC BY-SA.

关键词： Atoms

Strong quantum mechanical squeezing based on nonlinear dispersive loss in semiconductor lasers

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Strong quantum mechanical squeezing based on nonlinear dispe...

Conference on Lasers and Electro-Optics (CLEO)

作者： Sahil Pontula Jamison Sloan Nicholas Rivera Marin Soljačić Department of Physics MIT Cambridge MA United States Department of Electrical Engineering and Computer Science MIT Cambridge MA United States Department of Physics Harvard Cambridge MA United States

We predict that a combination of frequency-dependent outcoupling and optical nonlinearity in semiconductor lasers can create sharply intensity-dependent loss that squeezes intensity noise far below the shot noise limit.

ISBN: (纸本)9781957171258

关键词： Diode lasers Fano resonance Photonic crystal cavities Quantum dot lasers Quantum light sources Semiconductor lasers

Shannon Bounds on Lossy Gray-Wyner Networks

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Shannon Bounds on Lossy Gray-Wyner Networks

IEEE International Symposium on Information Theory

作者： Erixhen Sula Michael Gastpar The Department of Electrical Engineering and Computer Science MIT Massachusetts USA School of Computer and Communication Sciences EPFL Lausanne Switzerland

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

The Gray-Wyner network subject to a fidelity criterion is studied. Upper and lower bounds for the trade-offs between the private sum-rate and the common rate are obtained for arbitrary sources subject to mean-squared error distortion. The bounds meet exactly, leading to the computation of the rate region, when the source is jointly Gaussian. They meet partially when the sources are modeled via an additive Gaussian “channel”. The bounds are inspired from the Shannon bounds on the rate-distortion problem.

关键词： Additives Computational modeling Rate-distortion Distortion Gaussian channels

Probing intensity noise in ultrafast pulses using the dispersive Fourier transform augmented by quantum sensitivity analysis

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arXiv 2025年

作者： Uddin, Shiekh Zia Pontula, Sahil Liu, Jiaxin Xu, Shutao Choi, Seou Sander, Michelle Y. Soljačić, Marin Department of Physics MIT CambridgeMA02139 United States Research Laboratory of Electronics MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT 02139 CambridgeMA United States Department of Physics Imperial College London South Kensington LondonSW7 2BW United Kingdom Department of Electrical and Computer Engineering BU Photonics Center Boston University BostonMA02215 United States

To reach the next frontier in multimode nonlinear optics, it is crucial to better understand the classical and quantum phenomena of systems with many interacting degrees of freedom – both how they emerge and how they can be tailored to emerging applications, from multimode quantum light generation to optical computing. Soliton fission and Raman scattering comprise two such phenomena that are ideal testbeds for exploring multimode nonlinear optics, especially power-dependent physics. To fully capture the complexity of such processes, an experimental measurement technique capable of measuring shot-to-shot pulse variations is necessary. The dispersive Fourier transform (DFT) is the ideal technique to achieve this goal, using chromatic dispersion to temporally stretch an ultrafast pulse and map its spectrum onto a measurable temporal waveform. Here, we apply DFT to explore the power-dependent mean field and noise properties of soliton fission and Raman scattering. To explain quantum noise properties, the traditional approach is to perform several hundred stochastic simulations for computing statistics. In our work, we apply quantum sensitivity analysis (QSA) to compute the noise in any output observable based on fluctuations in the input pulse, all using a single backwards differentiation step. We find that the combination of DFT and QSA provides a powerful framework for understanding the quantum and classical properties of soliton fission and Raman scattering, and can be generalized to other multimode nonlinear phenomena. © 2025, CC BY.

关键词： Solitons

Quantum Bayesian Optimization

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arXiv 2023年

作者： Dai, Zhongxiang Lau, Gregory Kang Ruey Verma, Arun Shu, Yao Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore CNRS@CREATE 1 Create Way #08-01 Create Tower 138602 Singapore Department of Electrical Engineering and Computer Science MIT United States

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent method for optimizing complicated black-box reward functions. Various BO algorithms have been theoretically shown to enjoy upper bounds on their cumulative regret which are sub-linear in the number T of iterations, and a regret lower bound of Ω(√T ) has been derived which represents the unavoidable regrets for any classical BO algorithm. Recent works on quantum bandits have shown that with the aid of quantum computing, it is possible to achieve tighter regret upper bounds better than their corresponding classical lower bounds. However, these works are restricted to either multi-armed or linear bandits, and are hence not able to solve sophisticated real-world problems with non-linear reward functions. To this end, we introduce the quantum-Gaussian process-upper confidence bound (Q-GP-UCB) algorithm. To the best of our knowledge, our Q-GP-UCB is the first BO algorithm able to achieve a regret upper bound of O(poly log T ), which is significantly smaller than its regret lower bound of Ω(√T ) in the classical setting. Moreover, thanks to our novel analysis of the confidence ellipsoid, our Q-GP-UCB with the linear kernel achieves a smaller regret than the quantum linear UCB algorithm from the previous work. We use simulations, as well as an experiment using a real quantum computer, to verify that the theoretical quantum speedup achieved by our Q-GP-UCB is also potentially relevant in practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits