Mercurial > repos > bimib > cobraxy
comparison COBRAxy/marea.py @ 276:2748cef7fa12 draft
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author | francesco_lapi |
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date | Mon, 05 May 2025 10:33:30 +0000 |
parents | 99703bcaa290 |
children | 1bf66a30ab23 |
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275:e87aeb3a33cd | 276:2748cef7fa12 |
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291 tmp = d.get(el_id[2:]) | 291 tmp = d.get(el_id[2:]) |
292 if tmp != None: | 292 if tmp != None: |
293 p_val :float = tmp[0] | 293 p_val :float = tmp[0] |
294 f_c = tmp[1] | 294 f_c = tmp[1] |
295 z_score = tmp[2] | 295 z_score = tmp[2] |
296 | |
297 if math.isnan(p_val) or (isinstance(f_c, float) and math.isnan(f_c)): continue | |
298 | |
296 if p_val <= threshold_P_V: | 299 if p_val <= threshold_P_V: |
297 if not isinstance(f_c, str): | 300 if not isinstance(f_c, str): |
298 if abs(f_c) < ((threshold_F_C - 1) / (abs(threshold_F_C) + 1)): # | 301 if abs(f_c) < ((threshold_F_C - 1) / (abs(threshold_F_C) + 1)): # |
299 col = grey | 302 col = grey |
300 width = str(minT) | 303 width = str(minT) |
489 for reactionId, values in rpsEnrichmentRes.items(): | 492 for reactionId, values in rpsEnrichmentRes.items(): |
490 pValue = values[0] | 493 pValue = values[0] |
491 foldChange = values[1] | 494 foldChange = values[1] |
492 z_score = values[2] | 495 z_score = values[2] |
493 | 496 |
497 if math.isnan(pValue) or (isinstance(foldChange, float) and math.isnan(foldChange)): continue | |
498 | |
494 if isinstance(foldChange, str): foldChange = float(foldChange) | 499 if isinstance(foldChange, str): foldChange = float(foldChange) |
495 if pValue >= ARGS.pValue: # pValue above tresh: dashed arrow | 500 if pValue >= ARGS.pValue: # pValue above tresh: dashed arrow |
496 INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId) | 501 INSIGNIFICANT_ARROW.styleReactionElements(metabMap, reactionId) |
497 continue | 502 continue |
498 | 503 |
759 continue | 764 continue |
760 | 765 |
761 # fallthrough is intended, regular scores need to be computed when tips aren't net but RAS datasets aren't used | 766 # fallthrough is intended, regular scores need to be computed when tips aren't net but RAS datasets aren't used |
762 p_value, z_score = computePValue(l1, l2) | 767 p_value, z_score = computePValue(l1, l2) |
763 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2)) | 768 avg = fold_change(sum(l1) / len(l1), sum(l2) / len(l2)) |
764 if math.isnan(p_value) or (isinstance(avg, float) and math.isnan(avg)): continue | |
765 if not isinstance(z_score, str) and max_z_score < abs(z_score): max_z_score = abs(z_score) | 769 if not isinstance(z_score, str) and max_z_score < abs(z_score): max_z_score = abs(z_score) |
766 tmp[reactId] = [float(p_value), avg, z_score] | 770 tmp[reactId] = [float(p_value), avg, z_score] |
767 | 771 |
768 except (TypeError, ZeroDivisionError): continue | 772 except (TypeError, ZeroDivisionError): continue |
769 | 773 |