def test_htcount_split_by_rpm():
h = HTCountFilter(r"all_expr.csv")
high_truth = general.load_csv(r"all_expr_above60_rpm.csv", 0)
low_truth = general.load_csv(r"all_expr_below60_rpm.csv", 0)
high, low = h.split_by_rpm(threshold=60)
assert np.all(high.df == high_truth)
assert np.all(low.df == low_truth)
def test_htcount_filter_biotype():
truth_protein_coding = general.load_csv('all_expr_biotype_protein_coding.csv', 0)
truth_pirna = general.load_csv('all_expr_biotype_piRNA.csv', 0)
h = HTCountFilter("all_expr_biotype.csv")
protein_coding = h.filter_biotype(ref='extendedCelegansIDs_bigTable_39col_2019format_edit.csv', inplace=False)
pirna = h.filter_biotype('piRNA', ref='extendedCelegansIDs_bigTable_39col_2019format_edit.csv', inplace=False)
assert np.all(truth_protein_coding == protein_coding.df)
assert np.all(truth_pirna == pirna.df)
def test_deseq_filter_fold_change_direction():
pos_truth = general.load_csv("test_deseq_fc_pos_truth.csv", 0)
neg_truth = general.load_csv("test_deseq_fc_neg_truth.csv", 0)
d = DESeqFilter("test_deseq_fc.csv")
pos = d.filter_fold_change_direction('pos', inplace=False)
neg = d.filter_fold_change_direction('neg', inplace=False)
assert np.all(pos.df == pos_truth)
assert np.all(neg.df == neg_truth)
def test_deseq_split_fold_change():
d = DESeqFilter("test_deseq_fc.csv")
pos_truth = general.load_csv("test_deseq_fc_pos_truth.csv", 0)
neg_truth = general.load_csv("test_deseq_fc_neg_truth.csv", 0)
d = DESeqFilter("test_deseq_fc.csv")
pos, neg = d.split_fold_change_direction()
assert np.all(pos.df == pos_truth)
assert np.all(neg.df == neg_truth)
def test_htcount_filter_biotype_opposite():
truth_no_pirna = general.load_csv(r'all_expr_biotype_no_piRNA.csv', 0)
h = HTCountFilter("all_expr_biotype.csv")
h.filter_biotype('piRNA', opposite=True, inplace=True)
h.df.sort_index(inplace=True)
truth_no_pirna.sort_index(inplace=True)
assert np.all(h.df == truth_no_pirna)
def test_deseq_filter_abs_fold_change():
truth = general.load_csv("test_deseq_fc_4_truth.csv", 0)
d = DESeqFilter("test_deseq_fc.csv")
fc4 = d.filter_abs_fold_change(4, inplace=False)
fc4.df.sort_index(inplace=True)
truth.sort_index(inplace=True)
assert np.all(fc4.df == truth)
def import_target(self):
"""
import the target filename into a pandas DataFrame
"""
self.target_df = general.load_csv(self.target_filename,
drop_gene_names=False)
self.target_wbgene = [''] * self.target_df.shape[0]
def test_filter_by_bigtable_group_union():
union_truth = general.load_csv(r'all_expr_filter_by_bigtable_union_truth.csv', 0)
h = HTCountFilter('all_expr_filter_by_bigtable.csv')
union = h.filter_by_bigtable_group(['epigenetic_related_genes', 'P_granule_proteins'], mode='union',
ref='extendedCelegansIDs_bigTable_39col_2019format_edit.csv', inplace=False)
union.df.sort_index(inplace=True)
union_truth.sort_index(inplace=True)
assert np.all(union.df == union_truth)
def test_deseq_filter_significant_opposite():
truth = general.load_csv(r'test_deseq_not_sig_truth.csv', 0)
d = DESeqFilter("test_deseq_sig.csv")
d.filter_significant(alpha=0.05, opposite=True)
d.df.sort_index(inplace=True)
truth.sort_index(inplace=True)
truth.fillna(1234567890,inplace=True)
d.df.fillna(1234567890,inplace=True)
assert np.all(d.df == truth)
def test_filter_inplace():
d = DESeqFilter('test_deseq_no_nans.csv')
d_copy = DESeqFilter('test_deseq_no_nans.csv')
truth = general.load_csv('all_expr.csv')
d_inplace_false = d._inplace(truth, opposite=False, inplace=False, suffix='suffix')
assert np.all(d_inplace_false.df == truth)
assert np.all(d.df == d_copy.df)
d._inplace(truth, opposite=False, inplace=True, suffix='other_suffix')
assert np.all(d.df == truth)
def test_filter_low_rpm_reverse():
h = HTCountFilter(r"all_expr.csv")
low_truth = general.load_csv(r"all_expr_below60_rpm.csv", 0)
h.filter_low_rpm(threshold=60, opposite=True)
h.df.sort_index(inplace=True)
low_truth.sort_index(inplace=True)
print(h.shape)
print(low_truth.shape)
print(h.df)
print(low_truth)
assert np.all(h.df == low_truth)
def read_reference(self):
"""
load the reference file into a pandas DataFrame
"""
ref = general.load_csv(self.reference_filename, drop_gene_names=False)
self.gene_symbol_dict = {
name: wbgene
for name, wbgene in zip(ref[self.ref_gene_col], ref[
self.ref_wbgene_col]) if not pd.isna(name)
}
self.sequence_dict = {
name: wbgene
for name, wbgene in zip(ref[self.ref_seq_col], ref[
self.ref_wbgene_col]) if not pd.isna(name)
}
split_other_id = ref[self.ref_other_col].str.split(pat=";")
for namelst, wbgene in zip(split_other_id, ref[self.ref_wbgene_col]):
if isinstance(namelst, list):
for name in namelst:
self.other_id_dict[name] = wbgene
def test_deseq_filter_significant():
truth = general.load_csv("test_deseq_sig_truth.csv", 0)
d = DESeqFilter("test_deseq_sig.csv")
d.filter_significant(alpha=0.05)
assert np.all(d.df == truth)
def test_filter_low_rpm():
truth = general.load_csv("all_expr_low_rpm_truth.csv", 0)
h = HTCountFilter("all_expr_low_rpm.csv")
h.filter_low_rpm(threshold=5)
assert np.isclose(truth, h.df).all()
def test_deseq_filter_top_n():
truth = general.load_csv("test_deseq_top10.csv", 0)
d = DESeqFilter("test_deseq.csv")
d.filter_top_n(10)
assert np.isclose(truth, d.df).all()
def test_htcountfilter_norm_reads_to_rpm():
truth = general.load_csv(r"test_norm_reads_rpm.csv", 0)
h = HTCountFilter(r"all_expr.csv")
h.norm_reads_to_rpm(r"all_feature.csv")
assert np.isclose(truth, h.df).all()
def enrich_big_table(self,
attributes: list = None,
fdr: float = 0.05,
reps=10000,
biotype: str = 'protein_coding',
big_table_pth: str = __bigtable_path__,
save_csv: bool = True,
fname=None):
"""
Calculates enrichment scores, p-values and q-values \
for enrichment and depletion of selected attributes from the Big Table. \
P-values are calculated using a randomization test, and corrected for multiple comparisons using \
the Benjamini–Hochberg step-up procedure (original FDR method). \
Enrichment/depletion is determined automatically by the calculated enrichment score: \
if log2(enrichment score) is positive then enrichment is assumed, \
and if log2(enrichment score) is negative then depletion is assumed.
:param attributes: An iterable of attribute names (strings). If None, a manual input prompt will be raised.
:param fdr: float. Indicates the FDR threshold for significance.
:param reps: How many repetitions to run the randomization for. \
10,000 is the default. Recommended 10,000 or higher.
:param big_table_pth: the path of the Big Table file to be used as reference.
:param biotype: the biotype you want your reference to have. 'all' will include all biotypes, \
'protein_coding' will include only protein-coding genes in the reference, etc.
:param save_csv: bool. If True, will save the results to a .csv file, under the name specified in 'fname'.
:param fname: str/Path. The full path and name of the file to which to save the results. For example: \
r'C:\dir\file'. No '.csv' suffix is required. If None (default), fname will be requested in a manual prompt.
:return:
a pandas DataFrame with the indicated attribute names as rows/index, and the columns 'log2_enrichment_score'
and 'pvalue'.
.. figure:: bigtable_en.png
:align: center
:scale: 40 %
Example plot of big table enrichment
"""
if attributes is None:
attributes = self._from_string(
"Please insert attributes separated by newline "
"(for example: \n'epigenetic_related_genes\nnrde-3 targets\nALG-3/4 class small RNAs')"
)
elif isinstance(attributes, str):
attributes = [attributes]
else:
assert isinstance(
attributes,
(list, tuple,
set)), "'attributes' must be a list, tuple or set!"
try:
big_table = general.load_csv(big_table_pth, 0)
except:
raise ValueError("Invalid or nonexistent big table path!")
assert (isinstance(biotype, str))
if biotype == 'all':
pass
else:
big_table = big_table[big_table['bioType'] == biotype]
fraction = lambda mysrs: (mysrs.shape[0] - mysrs.isna().sum()
) / mysrs.shape[0]
enriched_list = []
for k, attribute in enumerate(attributes):
assert isinstance(
attribute, str
), f"Error in attribute {attribute}: attributes must be strings!"
print(f"Finished {k} attributes out of {len(attributes)}")
srs = big_table[attribute]
obs_srs = srs.loc[self.gene_set]
expected_fraction = fraction(srs)
observed_fraction = fraction(obs_srs)
log2_enrichment_score = np.log2(
(observed_fraction + 0.0001) / (expected_fraction + 0.0001))
success = sum((fraction(srs.loc[np.random.choice(
srs.index, obs_srs.shape[0], replace=False)]) >=
observed_fraction if log2_enrichment_score >= 0 else
fraction(srs.loc[np.random.choice(
srs.index, obs_srs.shape[0],
replace=False)]) <= observed_fraction
for rep in range(reps)))
pval = (success + 1) / (reps + 1)
n = obs_srs.shape[0]
enriched_list.append(
(attribute, n, int(n * observed_fraction),
n * expected_fraction, log2_enrichment_score, pval))
enriched_df = pd.DataFrame(enriched_list,
columns=[
'name', 'samples', 'n obs', 'n exp',
'log2_enrichment_score', 'pvals'
])
significant, padj = multitest.fdrcorrection(
enriched_df['pvals'].values, alpha=fdr)
enriched_df['padj'] = padj
enriched_df['significant'] = significant
enriched_df.set_index('name', inplace=True)
self._plot_enrich_big_table(enriched_df)
if save_csv:
self._enrichment_save_csv(enriched_df, fname)
print(enriched_df)
return enriched_df
