usage: __main__.py [-h] --in_g1 IN_G1 [IN_G1 ...] --in_g2 IN_G2 [IN_G2 ...]
[--tail {left,right,both}] [--paired]
[--fdr | --bonferroni] [--p_threshold THRESH OUT_FILE]
[--filtering_mask FILTERING_MASK]
[-v [{DEBUG,INFO,WARNING}]] [-f]
out_pval_matrix
Performs a network-based statistical comparison for populations g1 and g2. The
output is a matrix of the same size as the input connectivity matrices, with
p-values at each edge.
All input matrices must have the same shape (NxN). For paired t-test, both
groups must have the same number of observations.
For example, if you have streamline count weighted matrices for a MCI and a
control group and you want to investiguate differences in their connectomes:
>>> scil_connectivity_compare_populations.py pval.npy
--g1 MCI/*_sc.npy --g2 CTL/*_sc.npy
--filtering_mask will simply multiply the binary mask to all input
matrices before performing the statistical comparison. Reduces the number of
statistical tests, useful when using --fdr or --bonferroni.
Formerly: scil_compare_connectivity.py
positional arguments:
out_pval_matrix Output matrix (.npy) containing the edges p-value.
options:
-h, --help show this help message and exit
--in_g1 IN_G1 [IN_G1 ...]
List of matrices for the first population (.npy).
--in_g2 IN_G2 [IN_G2 ...]
List of matrices for the second population (.npy).
--tail {left,right,both}
Enables specification of an alternative hypothesis:
left: mean of g1 < mean of g2,
right: mean of g2 < mean of g1,
both: both means are not equal (default).
--paired Use paired sample t-test instead of population t-test.
--in_g1 and --in_g2 must be ordered the same way.
--fdr Perform a false discovery rate (FDR) correction for the p-values.
Uses the number of non-zero edges as number of tests (value between 0.01 and 0.1).
--bonferroni Perform a Bonferroni correction for the p-values.
Uses the number of non-zero edges as number of tests.
--p_threshold THRESH OUT_FILE
Threshold the final p-value matrix and save the binary matrix (.npy).
--filtering_mask FILTERING_MASK
Binary filtering mask (.npy) to apply before computing the measures.
-v [{DEBUG,INFO,WARNING}]
Produces verbose output depending on the provided level.
Default level is warning, default when using -v is info.
-f Force overwriting of the output files.
[1] Rubinov, Mikail, and Olaf Sporns. "Complex network measures of brain
connectivity: uses and interpretations." Neuroimage 52.3 (2010):
1059-1069.
[2] Zalesky, Andrew, Alex Fornito, and Edward T. Bullmore. "Network-based
statistic: identifying differences in brain networks." Neuroimage 53.4
(2010): 1197-1207.