Pipeline Comparison

Pipeline Comparison#

This page compares how different calcium imaging analysis pipelines handle ΔF/F calculation, neuropil correction, and spike deconvolution.

ΔF/F Calculation Methods#

Different pipelines handle ΔF/F₀ differently:

Pipeline	F₀ Method	ΔF/F₀	Neuropil
CaImAn	8th percentile, 500-frame window	Yes, in pipeline	Modeled via CNMF, no manual subtraction
Suite2p	Maximin (default) or 8th percentile	No, user divides post hoc	0.7 × Fneu subtracted before baseline
EXTRACT	User-defined (e.g. 10th percentile)	No, user computes	Implicitly handled via robust model

CaImAn#

CaImAn computes ΔF/F₀ using a running low-percentile baseline. By default, it uses the 8th percentile over a 500 frame window. The idea is to track the lower envelope of the signal to get F₀ without being biased by transients.

Neuropil/background: CaImAn handles this as part of its CNMF model. Background and neuropil are explicitly separated into distinct spatial/temporal components, so the output traces are background subtracted during this factorization.

CaImAn default DF/F strategy — CaImAn uses a default baseline of the `lower 8th percentile` and a moving window of `200 frames`.#

Suite2p#

Suite2p does not output traces in ΔF/F₀ format directly. Instead, it gives you the raw trace and the neuropil, along with spike estimates if you ran deconvolution. The neuropil represents fluorescence from the surrounding non-somatic tissue. As an optional step, many experimenters apply a fixed subtraction:

# F is an [n_neurons x time] array of raw signal
# Fneu is an [n_neurons x time] array of neuropil
F_corrected = F - 0.7 * Fneu

The 0.7 is an empirically chosen scalar to account for the partial contamination. Essentially you subtract 70% of the signal contained in the surrounding neuropil.

_images/dff_oasis.png — Raw, neuropil, ΔF/F₀ and resulting deconvolved spikes as output by Suite2p.#

EXTRACT#

EXTRACT outputs raw fluorescence signals without built-in ΔF/F₀ calculation. You compute it yourself using something like a low-percentile (e.g. 10%) as F₀. Most users apply a global or sliding percentile window.

Neuropil: Handled implicitly. The algorithm uses robust factorization to ignore background and neuropil. There’s no explicit subtraction or coefficient to tune—it isolates only what fits a consistent spatial footprint and suppresses outliers by design.

Deconvolution Algorithms#

Different pipelines use different approaches to spike deconvolution, each with their own τ (tau) handling:

Pipeline	Uses τ?	Range (s)	Method
FOOPSI	Yes	~1.0	Fixed exponential
OASIS / CNMF	Yes	0.3 – 2.0	AR(1/2) model
Suite2p	Yes	0.7 – 1.5	OASIS internal
CaImAn	Yes	0.4 – 2.0	CNMF-E fit
CASCADE	Implicit	0.3 – 2.0	Learned dynamics

Key Takeaways

τ defines calcium transient decay and sets temporal resolution of spike inference
Optimal τ depends on both indicator kinetics and frame rate
Pipelines like Suite2p and CaImAn require τ tuning per GECI
CASCADE bypasses explicit τ by learning it implicitly
GCaMP8 series are ~3× faster than GCaMP6

Note

All τ values summarized here reflect in vivo mammalian calcium imaging (typically ~30 Hz frame rate). In vitro or temperature-controlled decay times (e.g., 37 °C) can be >10× shorter. Choosing an incorrect τ biases both spike amplitude and inferred firing rate.