.. _scil_connectivity_compare_populations:

scil_connectivity_compare_populations
=====================================

::

	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,ERROR}]] [-f]
	                   out_pval_matrix
	
	Performs a statistical comparison between connectivity matrices for populations
	g1 and g2, using a t-test.
	
	The inputs are any connectivity matrix, that can be obtained with
	scil_connectivity_compute_matrices, used separately on the two populations.
	All input matrices must have the same shape (NxN).
	
	The output is a matrix of the same size as the input connectivity matrices,
	with p-values at each connection (edge).
	
	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 pval.npy
	           --g1 MCI/*_sc.npy --g2 CTL/*_sc.npy
	
	Options:
	
	--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.
	
	--paired will use a paired t-test. Then both groups must have the same number
	of observations (subjects). They must be listed in the right order using --g1
	and --g2.
	
	----------------------------------------------------------------------------
	References:
	[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.
	----------------------------------------------------------------------------
	
	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 each subject in the first population (.npy).
	  --in_g2 IN_G2 [IN_G2 ...]
	                        List of matrices for each subject in 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,ERROR}]
	                        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.
	
	2.2.2