What is the meaning of normxpehh (not the results of windows)

[kuangzhuoran]

I didn't use the results from the sliding window, after running norm I used the normxpehh value for each SNP。

Does the value of normxpehh mean the same thing as Z-score?
When normxpehh>1 means at 68.3%percentage, when normxpehh>2 means at 95.5%percentage?

here are my log:
Total loci: 16276736
num mean variance
16184832 0.0406915 0.075877
60802 windows with nSNPs >= 10.

High Scores
nSNPs 1.0 5.0
97 1 0.275647
131 1 0.134103
161 0.888617 0.0666757
194 0.921695 0.0779179
230 0.805676 0.0719993
269 0.787447 0.071026
314 0.861717 0.0987948
374 0.804403 0.129891
474 0.77446 0.190849
3102 0.789735 0.182703

Low Scores
nSNPs 1.0 5.0
97 0.832828 0.0593487
131 0.625114 0.0288462
161 0.381837 0.0285714
194 0.452405 0.0410326
230 0.52416 0.0468621
269 0.445802 0.0571757
314 0.399203 0.0661923
374 0.396143 0.0908381
474 0.545329 0.114022
3102 0.69193 0.16419

[szpiech]

Hello, Yes essentially you can treat them as z-scores. Under neutrality the normalized scores are approximately normally distributed. Zachary Le lun. 13 mai 2024 à 08:42, Zhuoran Kuang ***@***.***> a écrit :

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I didn't use the results from the sliding window, after running norm I used the normxpehh value for each SNP。 Does the value of normxpehh mean the same thing as Z-score? When normxpehh>1 means at 68.3%percentage, when normxpehh>2 means at 95.5%percentage? here are my log: Total loci: 16276736 num mean variance 16184832 0.0406915 0.075877 60802 windows with nSNPs >= 10. High Scores nSNPs 1.0 5.0 97 1 0.275647 131 1 0.134103 161 0.888617 0.0666757 194 0.921695 0.0779179 230 0.805676 0.0719993 269 0.787447 0.071026 314 0.861717 0.0987948 374 0.804403 0.129891 474 0.77446 0.190849 3102 0.789735 0.182703 Low Scores nSNPs 1.0 5.0 97 0.832828 0.0593487 131 0.625114 0.0288462 161 0.381837 0.0285714 194 0.452405 0.0410326 230 0.52416 0.0468621 269 0.445802 0.0571757 314 0.399203 0.0661923 374 0.396143 0.0908381 474 0.545329 0.114022 3102 0.69193 0.16419 — Reply to this email directly, view it on GitHub <#112>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABAKRQTOGOLGFZ46XYHH6YDZCCYNBAVCNFSM6AAAAABHUELLSKVHI2DSMVQWIX3LMV43ASLTON2WKOZSGI4TENZRGUZTIMI> . You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>