def test_agg_cols_group_by(self):
t = hl.utils.range_matrix_table(1, 10)
tests = [
(agg.group_by(
t.col_idx % 2,
hl.array(agg.collect_as_set(t.col_idx + 1)).append(0)), {
0: [1, 3, 5, 7, 9, 0],
1: [2, 4, 6, 8, 10, 0]
}),
(agg.group_by(
t.col_idx % 3,
agg.filter(
t.col_idx > 7,
hl.array(agg.collect_as_set(t.col_idx + 1)).append(0))), {
0: [10, 0],
1: [0],
2: [9, 0]
}),
(agg.group_by(
t.col_idx % 3,
agg.explode(
lambda elt: agg.collect(elt + 1).append(0),
hl.cond(t.col_idx > 7, [t.col_idx, t.col_idx + 1],
hl.empty_array(hl.tint32)))), {
0: [10, 11, 0],
1: [0],
2: [9, 10, 0]
}),
]
for aggregation, expected in tests:
self.assertEqual(
t.select_rows(result=aggregation).result.collect()[0],
expected)
def test_agg_cols_group_by(self):
t = hl.utils.range_matrix_table(1, 10)
tests = [(agg.group_by(t.col_idx % 2,
hl.array(agg.collect_as_set(t.col_idx + 1)).append(0)),
{0: [1, 3, 5, 7, 9, 0], 1: [2, 4, 6, 8, 10, 0]}),
(agg.group_by(t.col_idx % 3,
agg.filter(t.col_idx > 7,
hl.array(agg.collect_as_set(t.col_idx + 1)).append(0))),
{0: [10, 0], 1: [0], 2: [9, 0]}),
(agg.group_by(t.col_idx % 3,
agg.explode(lambda elt: agg.collect(elt + 1).append(0),
hl.cond(t.col_idx > 7,
[t.col_idx, t.col_idx + 1],
hl.empty_array(hl.tint32)))),
{0: [10, 11, 0], 1: [0], 2:[9, 10, 0]}),
]
for aggregation, expected in tests:
self.assertEqual(t.select_rows(result = aggregation).result.collect()[0], expected)
def test_agg_cols_filter(self):
t = hl.utils.range_matrix_table(1, 10)
tests = [(agg.filter(t.col_idx > 7,
agg.collect(t.col_idx + 1).append(0)),
[9, 10, 0]),
(agg.filter(t.col_idx > 7,
agg.explode(lambda elt: agg.collect(elt + 1).append(0),
[t.col_idx, t.col_idx + 1])),
[9, 10, 10, 11, 0]),
(agg.filter(t.col_idx > 7,
agg.group_by(t.col_idx % 3,
hl.array(agg.collect_as_set(t.col_idx + 1)).append(0))),
{0: [10, 0], 2: [9, 0]})
]
for aggregation, expected in tests:
self.assertEqual(t.select_rows(result = aggregation).result.collect()[0], expected)
def test_agg_cols_filter(self):
t = hl.utils.range_matrix_table(1, 10)
tests = [(agg.filter(t.col_idx > 7,
agg.collect(t.col_idx + 1).append(0)),
[9, 10, 0]),
(agg.filter(t.col_idx > 7,
agg.explode(lambda elt: agg.collect(elt + 1).append(0),
[t.col_idx, t.col_idx + 1])),
[9, 10, 10, 11, 0]),
(agg.filter(t.col_idx > 7,
agg.group_by(t.col_idx % 3,
hl.array(agg.collect_as_set(t.col_idx + 1)).append(0))),
{0: [10, 0], 2: [9, 0]})
]
for aggregation, expected in tests:
self.assertEqual(t.select_rows(result = aggregation).result.collect()[0], expected)
def import_gene_list(gene_list_path,
gene_column,
ensg=False,
pLI_threshold=False,
peek=False):
"""
Imports a gene list tsv and returns a set of ENSG or gene symbols
:param str gene_list_path: Path to TSV file with gene list of interest
:param str or None gene_column: Column in TSV file that specifies gene symbol or ENSG id.
This column will be turned into a set.
:param str or bool ENSG: If there are no ENSGs with version numbers in the file, specify False (Default)
If there are ENSGs with version numbers in the file, specify column containing the ENSGs.
:param float or bool pLI_threshold: If the file does not contain pLI scores to filter, specify False (Default)
If the file contains pLI scores, specify threshold to filter files.
e.g. pLI threshold = 0.95
:param bool peek: Default False.
If you want to peek at the gene list to get the parameters
Print out the first few lines of the gene list tsv, returns None
:return: Set of genes of interest
:rtype: set or None
"""
genes = hl.import_table(gene_list_path, impute=True)
if peek:
genes.show(width=200)
return None
if pLI_threshold:
genes = genes.filter(genes.pLI > pLI_threshold)
if ensg:
genes = genes.annotate(ensg=genes[gene_column].split("\\.")[0])
gene_column = "ensg"
genes = genes.aggregate(agg.collect_as_set(genes[gene_column]))
return genes