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update to prev #24

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update to prev #24

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79a716a
fixed possible bug on calc_splizvd.py line 257
afuentri Aug 18, 2023
2925e98
made changes more general
Aug 21, 2023
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16 changes: 8 additions & 8 deletions bin/calc_splizvd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -231,17 +231,17 @@ def main():
split_dict = {True : ["ann"], False : ["unann"]}

# remove constitutive splicing
df["posA_group"] = df["juncStart"].astype(str) + df["gene"]
df["posB_group"] = df["juncEnd"].astype(str) + df["gene"]
df["posStart_group"] = df["juncStart"].astype(str) + df["gene"]
df["posEnd_group"] = df["juncEnd"].astype(str) + df["gene"]

df["rank_acc"] = df.groupby("posA_group")["juncEnd"].rank(method="dense")
df["rank_don"] = df.groupby("posB_group")["juncStart"].rank(method="dense")
df["rank_acc"] = df.groupby("posStart_group")["juncEnd"].rank(method="dense")
df["rank_don"] = df.groupby("posEnd_group")["juncStart"].rank(method="dense")
# remove "almost consistutive splicing"
if args.rank_quant > 0:
let_dict2 = {"A" : "acc", "B" : "don"}
let_dict2 = {"Start" : "acc", "End" : "don"}

# threshold ranks for each donor and acceptor
for let in ["A","B"]:
for let in ["Start","End"]:
df["bottom_{}_quant".format(let_dict2[let])] = df["pos{}_group".format(let)].map(df.groupby("pos{}_group".format(let))["rank_{}".format(let_dict2[let])].quantile(q=args.rank_quant))
df["top_{}_quant".format(let_dict2[let])] = df["pos{}_group".format(let)].map(df.groupby("pos{}_group".format(let))["rank_{}".format(let_dict2[let])].quantile(q=1 - args.rank_quant))
df["rank_{}".format(let_dict2[let])] = df[["bottom_{}_quant".format(let_dict2[let]),"rank_{}".format(let_dict2[let])]].max(axis=1)
Expand All @@ -250,8 +250,8 @@ def main():
# start ranks at 1 (in case 1 is removed by quantiling)
df["rank_{}".format(let_dict2[let])] = df["rank_{}".format(let_dict2[let])] - df["bottom_{}_quant".format(let_dict2[let])] + 1

df["max_rank_acc"] = df["posA_group"].map(df.groupby("posA_group")["rank_acc"].max())
df["max_rank_don"] = df["posB_group"].map(df.groupby("posB_group")["rank_don"].max())
df["max_rank_acc"] = df["posStart_group"].map(df.groupby("posStart_group")["rank_acc"].max())
df["max_rank_don"] = df["posEnd_group"].map(df.groupby("posEnd_group")["rank_don"].max())

# add domain columns
letters = ["Start", "End"]
Expand Down