Time and memory usage by SCNIC
To evaluate the memory resources needed by SCNIC, we ran the SCNIC
modules step locally on a 2015 MacBook Pro with 16 GB RAM with a 2.5 GHz
Quad-Core Intel Core i7 processor for both the Great Lakes dataset and
an integrated microbiome-metabolome dataset with 1,301 features, which
will be referred to as the GT dataset. The runtime was recorded across 3
runs per method (SMD vs LMM) for each dataset using GNU Time, and memory
was profiled using memory-profiler 0.60.0. The “within” step, which
calculates correlations between features and creates the correlation
network was not tested because it depends greatly on the correlation
metric used, and the runtime and memory usage of FastSpar (likely the
most computationally intensive correlation metric to be used in this
step) have already been profiled[39]. The modules step only utilizes
a correlation matrix and as such does not scale with the number of
samples, only the number of features, except when the values of the
count table are being summed, which is a generally trivial calculation
compared to the module generation step.
RESULTS