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| author | Arun Isaac | 2021-11-11 16:10:35 +0530 |
|---|---|---|
| committer | BonfaceKilz | 2021-11-11 20:49:30 +0300 |
| commit | ec1d2180d99e0cde1dc181ee9ed79e86cf1a5675 (patch) | |
| tree | 61162273cc9b0b75577573b8b2082a1660cb39c1 /gn3 | |
| parent | 4e790f08000825931cb5edec1738d2b7d073f73e (diff) | |
| download | genenetwork3-ec1d2180d99e0cde1dc181ee9ed79e86cf1a5675.tar.gz | |
Reimplement correlations2.compute_correlation using pearsonr.
correlations2.compute_correlation computes the Pearson correlation coefficient. Outsource this computation to scipy.stats.pearsonr. When the inputs are constant, the Pearson correlation coefficient does not exist and is represented by NaN. Update the tests to reflect this. * gn3/computations/correlations2.py: Remove import of sqrt from math. (compute_correlation): Reimplement using scipy.stats.pearsonr. * tests/unit/computations/test_correlation.py: Import math. (TestCorrelation.test_compute_correlation): When inputs are constant, set expected correlation coefficient to NaN.
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/computations/correlations2.py | 21 |
1 files changed, 4 insertions, 17 deletions
diff --git a/gn3/computations/correlations2.py b/gn3/computations/correlations2.py index 69921b1..d0222ae 100644 --- a/gn3/computations/correlations2.py +++ b/gn3/computations/correlations2.py @@ -6,7 +6,7 @@ FUNCTIONS: compute_correlation: TODO: Describe what the function does...""" -from math import sqrt +from scipy import stats ## From GN1: mostly for clustering and heatmap generation def __items_with_values(dbdata, userdata): @@ -16,24 +16,11 @@ def __items_with_values(dbdata, userdata): return tuple(zip(*filtered)) if filtered else ([], []) def compute_correlation(dbdata, userdata): - """Compute some form of correlation. + """Compute the Pearson correlation coefficient. This is extracted from https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/utility/webqtlUtil.py#L622-L647 """ x_items, y_items = __items_with_values(dbdata, userdata) - if len(x_items) < 6: - return (0.0, len(x_items)) - meanx = sum(x_items)/len(x_items) - meany = sum(y_items)/len(y_items) - def cal_corr_vals(acc, item): - xitem, yitem = item - return [ - acc[0] + ((xitem - meanx) * (yitem - meany)), - acc[1] + ((xitem - meanx) * (xitem - meanx)), - acc[2] + ((yitem - meany) * (yitem - meany))] - xyd, sxd, syd = reduce(cal_corr_vals, zip(x_items, y_items), [0.0, 0.0, 0.0]) - try: - return ((xyd/(sqrt(sxd)*sqrt(syd))), len(x_items)) - except ZeroDivisionError: - return(0, len(x_items)) + correlation = stats.pearsonr(x_items, y_items)[0] if len(x_items) >= 6 else 0 + return (correlation, len(x_items)) |