diff options
Diffstat (limited to 'gn3/computations/correlations2.py')
| -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)) |