Stop losing downstream scores to NA

[rcannood]

Two ways the downstream metrics were reporting NA where they should have reported a number.

generate_cosine() filtered the wrong thing. real[!is.na(real) & !is.na(sim)] operates on the list returned by BivariateMoransI(), not on the Moran's I values inside it -- is.na() on a list of matrices is FALSE for every element, so it never dropped anything. One NaN anywhere then took the whole metric with it: crosscor_cosine was NA for 32 of 99 runs, while generate_mantel() passes na.rm = TRUE on the same inputs and lost none.

identical inputs  -> 1        (want 1)
one NaN in sim    -> 1        (was NaN before)
unrelated inputs  -> 0.8618
all NaN           -> NA       (want NA)

A simulation with no spatially variable genes now scores 0, not NA. calculate_precision() returned NA when tp + fp == 0, which is exactly what a simulator producing nothing spatially variable does -- the worst possible outcome, recorded as a missing result. Both negative controls were NA on all 10 datasets, so QC reported 8 of 30 expected control scores and nothing anchored the bottom of the scale.

calculate_recall() deliberately keeps NA in the same situation, and I added a comment saying why: there the empty denominator is a property of the real dataset (brain and hindlimbmuscle have no significant SVGs at all), so there is nothing for any simulator to recover, and scoring 0 would penalise every method for a property of the data.

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