LZeroSpikeInference {LZeroSpikeInference}

LZeroSpikeInference: LZeroSpikeInference: A package for estimating spike times from calcium imaging data using an L0 penalty

Description

This package implements an algorithm for deconvolving calcium imaging data for a single neuron in order to estimate the times at which the neuron spikes.

Details

This package implements an algorithm for deconvolving calcium imaging data for a single neuron in order to estimate the times at which the neuron spikes. This algorithm solves the optimization problems

AR(1)-model: minimize_c1,...,cT 0.5 sum_t=1^T ( y_t - c_t )^2 + lambda sum_t=2^T 1_c_t neq gamma c_t-1 for the global optimum, where $y_t$ is the observed fluorescence at the tth timepoint.

If hardThreshold = T then the additional constraint c_t >= 0 is added to the optimzation problem above.

AR(1) with intercept: minimize_c1,...,cT,b1,...,bT 0.5 sum_t=1^T (y_t - c_t - b_t)^2 + lambda sum_t=2^T 1_c_t neq gamma c_t-1, b_t neq b_t-1 where the indicator variable 1_(A,B) equals 1 if the event A cup B holds, and equals zero otherwise.

See Jewell and Witten (2017) <arXiv:1703.08644>

See Also

Estimate spikes: estimateSpikes, print.estimatedSpikes, plot.estimatedSpikes.

Cross validation: cv.estimateSpikes, print.cvSpike, plot.cvSpike.

Simulation: simulateAR1, plot.simdata.

Examples

# To reproduce Figure 1 of Jewell and Witten (2017) <arXiv:1703.08644>

sampleData <- simulateAR1(n = 500, gam = 0.998, poisMean = 0.009, sd = 0.15, seed = 8)
fit <- estimateSpikes(sampleData$fl, gam = 0.998, lambda = 8, type = "ar1")
plot(fit)