def cterm_dimotif_enrichment(df, thresh):
aa_vocab = degron_pred.vocab
output_list = []
base_str = 'xxxxxx'
for i in range(1, 6):
for j in range(i + 1, 7):
for aa1 in aa_vocab:
for aa2 in aa_vocab:
tmp = list(base_str)
tmp[-i] = aa1
tmp[-j] = aa2
pattern = ''.join(tmp)
testing = df['Peptide amino acid sequence'].apply(
motif_count, args=([pattern], 'cterm'))
top_ct = testing.iloc[:thresh].sum()
#bottom_ct = testing.iloc[thresh:-thresh].sum() + 1
bottom_ct = testing.sum()
top_n = thresh
#bot_n = len(testing)-thresh*2+1
bot_n = len(testing)
pval = stats.binom_test(top_ct,
n=top_n,
p=bottom_ct / bot_n,
alternative='greater')
#output_list.append([pattern, i, j, aa1, aa2, top_ct, bottom_ct, thresh, len(testing)-2*thresh, pval])
output_list.append([
pattern, i, j, aa1, aa2, top_ct, bottom_ct, thresh,
len(testing), pval
])
# compile results
mycols = [
'motif', 'pos1', 'pos2', 'aa1', 'aa2', 'top ct', 'bot ct', 'top total',
'bot total', 'pvalue'
]
result = pd.DataFrame(output_list, columns=mycols)
result['log(OR)'] = np.log2(
(result['top ct'] / (result['top total'] - result['top ct'])) /
(result['bot ct'] / (result['bot total'] - result['bot ct'])))
result['adjusted pvalue'] = result['pvalue'] * len(result)
result['qvalue'] = pvalue.bh_fdr(result['pvalue'])
result.sort_values('pvalue', inplace=True)
return result
def cterm_quadmotif_enrichment(df, thresh):
aa_vocab = degron_pred.vocab
# get background probs for each aa
aa_bg = {}
#bot_n = len(df)-thresh*2+1
bot_n = len(df)
for aa in aa_vocab:
tmp = df['Peptide amino acid sequence'].apply(motif_count_nopos,
args=([aa], 'cterm'))
#tmp_prob = (tmp.iloc[thresh:-thresh].sum() + 1) / (bot_n*6)
tmp_prob = (tmp.sum()) / (bot_n * 6)
aa_bg[aa] = tmp_prob
output_list = []
for aa1 in aa_vocab:
for aa2 in aa_vocab:
for aa3 in aa_vocab:
for aa4 in aa_vocab:
pattern = ''.join([aa1, aa2, aa3, aa4])
# figure out full count
testing = df['Peptide amino acid sequence'].apply(
motif_count_nopos, args=([pattern], 'cterm'))
top_ct = testing.iloc[:thresh].sum()
top_n = thresh * 3
# get baseline prob
bottom_prob_1 = aa_bg[pattern[0]]
tmp = df['Peptide amino acid sequence'].apply(
motif_count_nopos, args=([pattern[:2]], 'cterm'))
bottom_prob_2_di = (tmp.sum()) / (bot_n * 5)
bottom_prob_2 = bottom_prob_2_di / bottom_prob_1
bottom_prob_2_aa = aa_bg[pattern[1]]
tmp = df['Peptide amino acid sequence'].apply(
motif_count_nopos, args=([pattern[1:3]], 'cterm'))
bottom_prob_3_di = (tmp.sum()) / (bot_n * 5)
bottom_prob_3 = bottom_prob_3_di / bottom_prob_2_aa
bottom_prob_3_aa = aa_bg[pattern[2]]
tmp = df['Peptide amino acid sequence'].apply(
motif_count_nopos, args=([pattern[2:4]], 'cterm'))
bottom_prob_4_di = (tmp.sum()) / (bot_n * 5)
bottom_prob_4 = bottom_prob_4_di / bottom_prob_3_aa
# calc background p
bottom_prob = bottom_prob_1 * bottom_prob_2 * bottom_prob_3 * bottom_prob_4
# measure significance
pval = stats.binom_test(top_ct,
n=top_n,
p=bottom_prob,
alternative='greater')
#output_list.append([pattern, top_ct, bottom_prob, thresh, len(testing)-2*thresh, pval])
output_list.append(
[pattern, top_ct, bottom_prob, thresh, bot_n, pval])
# compile results
mycols = [
'motif', 'top ct', 'background p', 'top total', 'bot total', 'pvalue'
]
result = pd.DataFrame(output_list, columns=mycols)
result['log(OR)'] = np.log2(
(result['top ct'] / (result['top total'] - result['top ct'])) /
(result['background p'] / (1 - result['background p'])))
result['adjusted pvalue'] = result['pvalue'] * len(result)
result.loc[result['adjusted pvalue'] > 1, 'adjusted pvalue'] = 1
result['qvalue'] = pvalue.bh_fdr(result['pvalue'])
result.sort_values('pvalue', inplace=True)
return result