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Quantifying uncertainty in spikes estimated from calcium imaging data.

ABSTRACT: In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike-i.e., that there was no increase in calcium-at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample $p$-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the $\texttt{spikefinder}$ challenge.

SUBMITTER: Chen YT

PROVIDER: S-EPMC10449000 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Quantifying uncertainty in spikes estimated from calcium imaging data.

Chen Yiqun T YT Jewell Sean W SW Witten Daniela M DM

Biostatistics (Oxford, England) 20230401 2

In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spi ...[more]

PMID: 34654923

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Project description:MotivationGenome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Microarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs) simultaneously. The starting point for the statistical analyses used by GWAS to determine association between loci and disease is making genotype calls (AA, AB or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays and different sample batches have substantial influence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability can adversely affect the quality of findings reported by the GWAS.ResultsWe developed a method based on an enhanced version of the multi-level model used by CRLMM version 1. Two key differences are that we now account for variability across batches and improve the call-specific assessment of each call. The new model permits the development of quality metrics for SNPs, samples and batches of samples. Using three independent datasets, we demonstrate that the CRLMM version 2 outperforms CRLMM version 1 and the algorithm provided by Affymetrix, Birdseed. The main advantage of the new approach is that it enables the identification of low-quality SNPs, samples and batches.AvailabilitySoftware implementing of the method described in this article is available as free and open source code in the crlmm R/BioConductor package.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Quantifying uncertainty in spikes estimated from calcium imaging data.

Publications

Quantifying uncertainty in spikes estimated from calcium imaging data.

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