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()
assay_df = gn.pandas_from_assay(gn.get_import('assay'))
grdict = gn.get_import('groupVec')
phe_dict = pd.Series(gn.get_import('groupVec'))
groups = set(parse(gn.get_arg('groups')))
inv_map = {}
for k, v in grdict.items():
if v in groups:
inv_map[v] = inv_map.get(v, []) + [k]
cells = []
for k, v in inv_map.items():
cells.extend(v)
assay_df = assay_df.loc[:, cells]
assay_df = assay_df.sparse.to_dense().fillna(0)
#assay_mat = r['as.matrix'](pandas2ri.py2ri(assay_df))
# assay_mat = r['as.matrix'](conversion.py2rpy(assay_df))
phe_vec = phe_dict[assay_df.columns]
r.source('./drive_DESeq2.R')
ret_r = r['run_DESeq'](assay_df, phe_vec)
ret_r_as_df = r['as.data.frame'](ret_r)
# ret_py_df = pandas2ri.ri2py(ret_r_as_df)
# TODO: maybe rename the columns to be more self-explanatory?
result_df = ret_r_as_df
result_df = result_df.sort_values('padj')
result_df.index.name = 'gene'
gn.add_pandas_df(result_df.reset_index(),
description='The result table as returned by DESeq2.')
gn.export(result_df.to_csv(), 'DESeq2_results.csv', raw=True)
significant_genes = result_df.loc[
result_df['padj'] < 0.05]['log2FoldChange'].to_dict()
gn.export(significant_genes, 'Significant genes', kind='geneMeta')
gn.commit()
def main():
gn = Granatum()
sample_coords = gn.get_import("viz_data")
df = gn.pandas_from_assay(gn.get_import("assay"))
gene_ids = gn.get_arg("gene_ids")
overlay_genes = gn.get_arg("overlay_genes")
max_colors = gn.get_arg("max_colors")
min_level = gn.get_arg("min_level")
max_level = gn.get_arg("max_level")
convert_to_zscore = gn.get_arg("convert_to_zscore")
min_marker_area = gn.get_arg("min_marker_area")
max_marker_area = gn.get_arg("max_marker_area")
min_alpha = gn.get_arg("min_alpha")
max_alpha = gn.get_arg("max_alpha")
grey_level = gn.get_arg("grey_level")
coords = sample_coords.get("coords")
dim_names = sample_coords.get("dimNames")
cmaps = []
if overlay_genes:
if max_colors == "":
numcolors = len(gene_ids.split(','))
cycol = cycle('bgrcmk')
for i in range(numcolors):
cmaps = cmaps + [
LinearSegmentedColormap("fire",
produce_cdict(next(cycol),
grey=grey_level,
min_alpha=min_alpha,
max_alpha=max_alpha),
N=256)
]
else:
for col in max_colors.split(','):
col = col.strip()
cmaps = cmaps + [
LinearSegmentedColormap("fire",
produce_cdict(col,
grey=grey_level,
min_alpha=min_alpha,
max_alpha=max_alpha),
N=256)
]
else:
if max_colors == "":
cmaps = cmaps + [LinearSegmentedColormap("fire", cdict, N=256)]
else:
for col in max_colors.split(','):
col = col.strip()
cmaps = cmaps + [
LinearSegmentedColormap("fire",
produce_cdict(col,
grey=grey_level,
min_alpha=min_alpha,
max_alpha=max_alpha),
N=256)
]
colorbar_height = 10
plot_height = 650
num_cbars = 1
if overlay_genes:
num_cbars = len(gene_ids.split(','))
cbar_height_ratio = plot_height / (num_cbars * colorbar_height)
fig, ax = plt.subplots(
1 + num_cbars,
1,
gridspec_kw={'height_ratios': [cbar_height_ratio] + [1] * num_cbars})
gene_index = -1
for gene_id in gene_ids.split(','):
gene_id = gene_id.strip()
gene_index = gene_index + 1
if gene_id in df.index:
if not overlay_genes:
plt.clf()
fig, ax = plt.subplots(
1 + num_cbars,
1,
gridspec_kw={
'height_ratios': [cbar_height_ratio] + [1] * num_cbars
})
transposed_df = df.T
mean = transposed_df[gene_id].mean()
stdev = transposed_df[gene_id].std(ddof=0)
if convert_to_zscore:
scatter_df = pd.DataFrame(
{
"x": [a[0] for a in coords.values()],
"y": [a[1] for a in coords.values()],
"value": (df.loc[gene_id, :] - mean) / stdev
},
index=coords.keys())
else:
scatter_df = pd.DataFrame(
{
"x": [a[0] for a in coords.values()],
"y": [a[1] for a in coords.values()],
"value": df.loc[gene_id, :]
},
index=coords.keys())
values_df = np.clip(scatter_df["value"],
min_level,
max_level,
out=None)
min_value = np.nanmin(values_df)
max_value = np.nanmax(values_df)
scaled_marker_size = (max_marker_area - min_marker_area) * (
values_df - min_value) / (max_value -
min_value) + min_marker_area
scaled_marker_size = scaled_marker_size * scaled_marker_size
# s = 5000 / scatter_df.shape[0]
scatter = ax[0].scatter(
x=scatter_df["x"],
y=scatter_df["y"],
s=scaled_marker_size,
c=values_df,
cmap=cmaps[gene_index % len(cmaps)]) #Amp_3.mpl_colormap)
cbar = fig.colorbar(scatter,
cax=ax[1 + (gene_index % num_cbars)],
orientation='horizontal',
aspect=40)
cbar.set_label(gene_id, rotation=0)
ax[0].set_xlabel(dim_names[0])
ax[0].set_ylabel(dim_names[1])
if not overlay_genes:
gn.add_current_figure_to_results(
"Scatter-plot of {} expression".format(gene_id), dpi=75)
else:
# if the gene ID entered is not present in the assay
# Communicate it to the user and output a table of available gene ID's
description = 'The selected gene is not present in the assay. See the step that generated the assay'
genes_in_assay = pd.DataFrame(
df.index.tolist(),
columns=['Gene unavailable in assay: choose from below'])
gn.add_pandas_df(genes_in_assay, description)
if overlay_genes:
gn.add_current_figure_to_results(
"Scatter-plot of {} expression".format(gene_ids),
height=650 + 100 * len(gene_ids.split(',')),
dpi=75)
gn.commit()
def main():
gn = Granatum()
gene_scores_dict = gn.get_import("gene_scores")
species = gn.get_arg("species")
gset_group_id = gn.get_arg("gset_group_id")
threshold = gn.get_arg("threshold")
use_abs = gn.get_arg("use_abs")
background = gn.get_arg("background")
gene_ids = list(gene_scores_dict.keys())
gene_scores = list(gene_scores_dict.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")
# problem is that gene_ids is NAN after this
else:
raise ValueError()
if use_abs:
input_list = np.array(gene_ids)[
np.abs(np.array(gene_scores)) >= threshold]
else:
input_list = np.array(gene_ids)[np.array(gene_scores) >= threshold]
print(input_list)
gn.add_result(
f"""\
Number of genes after thresholding: {len(input_list)} (out of original {len(gene_ids)}).
Please see the attachment `list_of_genes.csv` for the list of genes considered in this enrichment analysis.""",
'markdown',
)
gn.export(pd.Series(input_list).to_csv(index=False),
'list_of_genes.csv',
kind='raw',
meta=None,
raw=True)
if background == 'all':
background_list = get_all_genes('human')
elif background == 'from_gene_sets':
background_list = None
elif background == 'from_input':
background_list = gene_ids
else:
raise ValueError()
result_df = zgsea.simple_fisher(input_list,
gset_group_id,
background_list=background_list)
result_df = result_df.sort_values('fdr')
result_df = result_df[[
'gene_set_name',
'size',
'p_val',
'fdr',
'odds_ratio',
'n_overlaps',
'overlapping_genes',
]]
result_df.columns = [
'Gene set',
'Gene set size',
'p-value',
'FDR',
'Odds ratio',
'Number of overlapping genes',
'Overlapping genes',
]
gn.add_pandas_df(result_df)
gn.export(result_df.to_csv(index=False),
'enrichment_results.csv',
kind='raw',
meta=None,
raw=True)
gn.commit()