  With the increasing popularity of calcium imaging data in neuroscience
research, methods for analyzing calcium trace data are critical to address
various questions. The observed calcium traces are either analyzed directly or
deconvolved to spike trains to infer neuronal activities. When both approaches
are applicable, it is unclear whether deconvolving calcium traces is a
necessary step. In this article, we compare the performance of using calcium
traces or their deconvolved spike trains for three common analyses: clustering,
principal component analysis (PCA), and population decoding. Our simulations
and applications to real data suggest that the estimated spike data outperform
calcium trace data for both clustering and PCA. Although calcium trace data
show higher predictability than spike data at each time point, spike history or
cumulative spike counts is comparable to or better than calcium traces in
population decoding.
