def main():
gn = Granatum()
gene_scores = gn.get_import("gene_scores")
species = gn.get_arg("species")
gset_group_id = gn.get_arg("gset_group_id")
n_repeats = gn.get_arg("n_repeats")
alterChoice = gn.get_arg("alterChoice")
if alterChoice=="pos":
gene_scores = dict(filter(lambda elem: elem[1] >= 0.0, gene_scores.items()))
elif alterChoice=="neg":
gene_scores = dict(filter(lambda elem: elem[1] < 0.0, gene_scores.items()))
gene_scores = { k: abs(v) for k, v in gene_scores.items() }
gene_ids = gene_scores.keys()
gene_scores = gene_scores.values()
gene_id_type = guess_gene_id_type(list(gene_ids)[:5])
if gene_id_type != 'symbol':
gene_ids = convert_gene_ids(gene_ids, gene_id_type, 'symbol', species)
if species == "human":
pass
elif species == "mouse":
gene_ids = zgsea.to_human_homolog(gene_ids, "mouse")
else:
raise ValueError()
result_df = zgsea.gsea(gene_ids, gene_scores, gset_group_id, n_repeats=n_repeats)
if result_df is None:
gn.add_markdown('No gene set is enriched with your given genes.')
else:
result_df = result_df[["gset_name", "gset_size", "nes", "p_val", "fdr"]]
gn.add_pandas_df(result_df)
gn.export(result_df.to_csv(index=False), 'gsea_results.csv', kind='raw', meta=None, raw=True)
newdict = dict(zip(result_df.index.tolist(), result_df['nes'].tolist()))
print(newdict, flush=True)
gn.export(newdict, 'nes', 'geneMeta')
gn.commit()
def main():
gn = Granatum()
tb1 = gn.pandas_from_assay(gn.get_import('assay1'))
tb2 = gn.pandas_from_assay(gn.get_import('assay2'))
label1 = gn.get_arg('label1')
label2 = gn.get_arg('label2')
direction = gn.get_arg('direction')
normalization = gn.get_arg('normalization')
if direction == 'samples':
tb1 = tb1.T
tb2 = tb2.T
overlapped_index = set(tb1.index) & set(tb2.index)
tb1.index = [
f"{label1}_{x}" if x in overlapped_index else x for x in tb1.index
]
tb2.index = [
f"{label2}_{x}" if x in overlapped_index else x for x in tb2.index
]
if normalization == 'none':
tb = pd.concat([tb1, tb2], axis=0)
elif normalization == 'frobenius':
ntb1 = np.linalg.norm(tb1)
ntb2 = np.linalg.norm(tb2)
ntb = np.mean([ntb1, ntb2])
fct1 = ntb / ntb1
fct2 = ntb / ntb2
tb = pd.concat([tb1 * fct1, tb2 * fct2], axis=0)
gn.add_markdown(f"""\
Normalization info:
- Assay **{label1}** is multiplied by {fct1}
- Assay **{label2}** is multiplied by {fct2}
""")
elif normalization == 'mean':
ntb1 = np.mean(tb1)
ntb2 = np.mean(tb2)
ntb = np.mean([ntb1, ntb2])
fct1 = ntb / ntb1
fct2 = ntb / ntb2
tb = pd.concat([tb1 * fct1, tb2 * fct2], axis=0)
gn.add_markdown(f"""\
Normalization info:",
- Assay **{label1}** is multiplied by {fct1}
- Assay **{label2}** is multiplied by {fct2}
""")
else:
raise ValueError()
if direction == 'samples':
tb = tb.T
gn.add_markdown(f"""\
You combined the following assays:
- Assay 1 (with {tb1.shape[0]} genes and {tb1.shape[1]} cells)
- Assay 2 (with {tb2.shape[0]} genes and {tb2.shape[1]} cells)
into:
- Combined Assay (with {tb.shape[0]} genes and {tb.shape[1]} cells)
""")
gn.export_statically(gn.assay_from_pandas(tb), 'Combined assay')
if direction == 'samples':
meta_type = 'sampleMeta'
elif direction == 'genes':
meta_type = 'geneMeta'
else:
raise ValueError()
gn.export(
{
**{x: label1
for x in tb1.index},
**{x: label2
for x in tb2.index}
}, 'Assay label', meta_type)
gn.commit()
