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fix bugs.
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@tanliwei-genomics-cn
tanliwei-genomics-cn committed Jan 17, 2024
1 parent 36658d0 commit f0d45c6
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Showing 3 changed files with 29 additions and 122 deletions.
2 changes: 1 addition & 1 deletion requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ numpy==1.23.5
matplotlib==3.7.4
pandas>=1.5.3
scipy==1.10.1
seaborn>=0.12.2
seaborn==0.12.2
h5py>=3.7.0
gefpy>=1.1.0
# setuptools>=41.0.0,<60.0.0
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145 changes: 26 additions & 119 deletions stereo/io/reader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,8 +42,7 @@ def read_gem(
bin_type: str = "bins",
bin_size: int = 100,
is_sparse: bool = True,
bin_coor_offset: bool = False,
gene_name_index: bool = False
bin_coor_offset: bool = False
):
"""
Read the Stereo-seq GEM file, and generate the StereoExpData object.
Expand All @@ -61,13 +60,8 @@ def read_gem(
is_sparse
the expression matrix is sparse matrix, if `True`, otherwise `np.ndarray`.
bin_coor_offset
if set it to True, the coordinates of bins are calculated as
If set it to True, the coordinates of bins are calculated as
((gene_coordinates - min_coordinates) // bin_size) * bin_size + min_coordinates + bin_size/2
gene_name_index
In a v0.1 gem file, the column geneID is the gene name actually, but in a v0.2,
geneID just a ID for genes and there is an additional column called geneName where is the gene name,
When the version of gem file is v0.2, set `gene_name_index` to True to set column geneName as index, otherwise,
set column geneID, if a v0.1 gem file, `gene_name_index` will be ignored and column geneID is set as index.
Returns
-------------
Expand All @@ -86,9 +80,6 @@ def read_gem(
dropna_subset = ['geneID', 'x', 'y', 'UMICount']
if 'cell_id' in df.columns:
dropna_subset.append('cell_id')
if 'geneName' in df.columns:
dropna_subset.append('geneName')

df.dropna(
subset=dropna_subset,
axis=0,
Expand All @@ -108,21 +99,10 @@ def read_gem(
genes_dict = dict(zip(genes, range(0, len(genes))))
rows = df['cell_id'].map(cells_dict)
cols = df['geneID'].map(genes_dict)
# logger.info(f'the martrix has {len(cells)} cells, and {len(genes)} genes.')
logger.info(f'the martrix has {len(cells)} cells, and {len(genes)} genes.')
exp_matrix = csr_matrix((df['UMICount'], (rows, cols)), shape=(cells.shape[0], genes.shape[0]), dtype=np.int32)
data.cells = Cell(cell_name=cells)

if 'geneName' in df.columns:
gene_names = df['geneName'].unique().astype('U')
if gene_name_index:
exp_matrix, gene_names = __integrate_genes(gene_names, exp_matrix)
data.genes = Gene(gene_name=gene_names)
else:
data.genes = Gene(gene_name=genes)
data.genes['gene_name_underline'] = gene_names
data.genes['real_gene_name'] = __remove_gene_number(gene_names)
else:
data.genes = Gene(gene_name=genes)
data.genes = Gene(gene_name=genes)
data.exp_matrix = exp_matrix if is_sparse else exp_matrix.toarray()
if data.bin_type == 'bins':
# data.position = df.loc[:, ['x_center', 'y_center']].drop_duplicates().values
Expand All @@ -146,7 +126,6 @@ def read_gem(
'maxExp': data.exp_matrix.max(), # noqa
'resolution': resolution,
}
logger.info(f'the martrix has {data.cell_names.size} cells, and {data.gene_names.size} genes.')
return data


Expand Down Expand Up @@ -1038,8 +1017,7 @@ def read_gef(
bin_size: int = 100,
is_sparse: bool = True,
gene_list: Optional[list] = None,
region: Optional[list] = None,
gene_name_index: Optional[bool] = False
region: Optional[list] = None
):
"""
Read the GEF (.h5) file, and generate the StereoExpData object.
Expand All @@ -1058,10 +1036,6 @@ def read_gef(
select targeted data based on the gene list.
region
restrict data to the region condition, like [minX, maxX, minY, maxY].
gene_name_index
`True` to set gene name as index if the version of gef file is 4 or greater,
otherwise to set gene id, if the version is 3 or less, `gene_name_index` would
be forced to `True` because there is no gene id in this case.
Returns
------------------------
Expand Down Expand Up @@ -1099,59 +1073,22 @@ def read_gef(
data.cells = Cell(cell_name=uniq_cell, cell_border=uniq_cell_borders)
data.cells['dnbCount'] = dnb_cnt
data.cells['area'] = cell_area

if len(gene_id[0]) == 0:
gene_name_index = True
if gene_name_index:
if len(gene_id[0]) > 0:
exp_matrix, gene_names = __integrate_genes(gene_names, exp_matrix)
data.genes = Gene(gene_name=gene_names)
else:
data.genes = Gene(gene_name=gene_id)
# data.genes['gene_name_underline'] = gene_names
data.genes['real_gene_name'] = __remove_gene_number(gene_names)
data.genes = Gene(gene_name=gene_names)

data.exp_matrix = exp_matrix if is_sparse else exp_matrix.toarray()
else:
# from gefpy.cell_exp_reader import CellExpReader
# cell_bin_gef = CellExpReader(file_path)
# data.position = cell_bin_gef.positions
# logger.info(f'the matrix has {cell_bin_gef.cell_num} cells, and {cell_bin_gef.gene_num} genes.')
# exp_matrix = csr_matrix((cell_bin_gef.count, (cell_bin_gef.rows, cell_bin_gef.cols)),
# shape=(cell_bin_gef.cell_num, cell_bin_gef.gene_num), dtype=np.uint32)
# data.cells = Cell(cell_name=cell_bin_gef.cells, cell_border=cell_borders)
# data.cells['dnbCount'] = cell_bin_gef.dnbCount
# data.cells['area'] = cell_bin_gef.area
# data.genes = Gene(gene_name=cell_bin_gef.genes)
# data.exp_matrix = exp_matrix if is_sparse else exp_matrix.toarray()
cell_names = gef.get_cell_names()
cell_num = gef.get_cell_num()
gene_names, gene_id = gef.get_gene_names()
gene_names = gene_names.astype('U')
gene_id = gene_id.astype('U')
gene_num = gef.get_gene_num()
# logger.info(f'the matrix has {cell_num} cells, and {gene_num} genes.')
indices, indptr, count = gef.get_sparse_matrix_indices(order='cell')
exp_matrix = csr_matrix((count, indices, indptr), shape=(cell_num, gene_num), dtype=np.uint32)
data.cells = Cell(cell_name=cell_names, cell_border=cell_borders)
cells = gef.get_cells()
data.cells['dnbCount'] = cells['dnbCount']
data.cells['area'] = cells['area']
data.position = np.zeros(shape=(cell_num, 2), dtype=np.uint32)
data.position[:, 0] = cells['x']
data.position[:, 1] = cells['y']
if len(gene_id[0]) == 0:
gene_name_index = True
if gene_name_index:
if len(gene_id[0]) > 0:
exp_matrix, gene_names = __integrate_genes(gene_names, exp_matrix)
data.genes = Gene(gene_name=gene_names)
else:
data.genes = Gene(gene_name=gene_id)
# data.genes['gene_name_underline'] = gene_names
data.genes['real_gene_name'] = __remove_gene_number(gene_names)

from gefpy.cell_exp_reader import CellExpReader
cell_bin_gef = CellExpReader(file_path)
data.position = cell_bin_gef.positions
logger.info(f'the matrix has {cell_bin_gef.cell_num} cells, and {cell_bin_gef.gene_num} genes.')
exp_matrix = csr_matrix((cell_bin_gef.count, (cell_bin_gef.rows, cell_bin_gef.cols)),
shape=(cell_bin_gef.cell_num, cell_bin_gef.gene_num), dtype=np.uint32)
data.cells = Cell(cell_name=cell_bin_gef.cells, cell_border=cell_borders)
data.cells['dnbCount'] = cell_bin_gef.dnbCount
data.cells['area'] = cell_bin_gef.area
data.genes = Gene(gene_name=cell_bin_gef.genes)
data.exp_matrix = exp_matrix if is_sparse else exp_matrix.toarray()

data.attr = {
'resolution': read_gef_info(file_path)['resolution']
}
Expand Down Expand Up @@ -1190,52 +1127,22 @@ def read_gef(
data.position = np.array(
list((zip(np.right_shift(uniq_cell, 32), np.bitwise_and(uniq_cell, 0xffffffff))))).astype('uint32')
data.cells = Cell(cell_name=uniq_cell)
data.genes = Gene(gene_name=gene_names)

exp_matrix = csr_matrix((count, (cell_ind, gene_ind)), shape=(cell_num, gene_num), dtype=np.uint32)
if len(gene_id[0]) == 0:
gene_name_index = True
if gene_name_index:
if len(gene_id[0]) > 0:
exp_matrix, gene_names = __integrate_genes(gene_names, exp_matrix)
data.genes = Gene(gene_name=gene_names)
else:
data.genes = Gene(gene_name=gene_id)
# data.genes['gene_name_underline'] = gene_names
data.genes['real_gene_name'] = __remove_gene_number(gene_names)

data.exp_matrix = exp_matrix if is_sparse else exp_matrix.toarray()
else:
# gene_num = gef.get_gene_num()
# uniq_cells, rows, count = gef.get_exp_data()
# cell_num = len(uniq_cells)
# logger.info(f'the matrix has {cell_num} cells, and {gene_num} genes.')
# cols, uniq_genes, _ = gef.get_gene_data()
# data.position = np.array(list(
# (zip(np.right_shift(uniq_cells, 32), np.bitwise_and(uniq_cells, 0xffffffff))))).astype('uint32')
# exp_matrix = csr_matrix((count, (rows, cols)), shape=(cell_num, gene_num), dtype=np.uint32)
# data.cells = Cell(cell_name=uniq_cells)
# data.genes = Gene(gene_name=uniq_genes)
# data.exp_matrix = exp_matrix if is_sparse else exp_matrix.toarray()
cell_names = gef.get_cell_names()
cell_num = gef.get_cell_num()
gene_names, gene_id = gef.get_gene_names()
gene_num = gef.get_gene_num()
uniq_cells, rows, count = gef.get_exp_data()
cell_num = len(uniq_cells)
logger.info(f'the matrix has {cell_num} cells, and {gene_num} genes.')
cols, uniq_genes, _ = gef.get_gene_data()
data.position = np.array(list(
(zip(np.right_shift(cell_names, 32), np.bitwise_and(cell_names, 0xffffffff))))).astype('uint32')
data.cells = Cell(cell_name=cell_names)
if len(gene_id[0]) == 0: # an old version gef file, no gene id
gene_name_index = True

cell_ind, gene_ind, count = gef.get_sparse_matrix_indices2()
exp_matrix = csr_matrix((count, (cell_ind, gene_ind)), shape=(cell_num, gene_num), dtype=np.uint32)
if gene_name_index:
if len(gene_id[0]) > 0:
exp_matrix, gene_names = __integrate_genes(gene_names, exp_matrix)
data.genes = Gene(gene_name=gene_names)
else:
data.genes = Gene(gene_name=gene_id)
# data.genes['gene_name_underline'] = gene_names
data.genes['real_gene_name'] = __remove_gene_number(gene_names)
(zip(np.right_shift(uniq_cells, 32), np.bitwise_and(uniq_cells, 0xffffffff))))).astype('uint32')
exp_matrix = csr_matrix((count, (rows, cols)), shape=(cell_num, gene_num), dtype=np.uint32)
data.cells = Cell(cell_name=uniq_cells)
data.genes = Gene(gene_name=uniq_genes)
data.exp_matrix = exp_matrix if is_sparse else exp_matrix.toarray()

logger.info(f'the matrix has {data.cell_names.size} cells, and {data.gene_names.size} genes.')
Expand Down
4 changes: 2 additions & 2 deletions stereo/plots/plot_cells.py
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Original file line number Diff line number Diff line change
Expand Up @@ -269,10 +269,10 @@ def _create_figure(cm_key_continuous_value, cm_key_discrete_value, color_by_valu
if color_by_value == 'gene':
self.color_key = gene_names_value
self.gene_names.visible = True
self.gene_names.disabled = False
# self.gene_names.disabled = False
else:
self.gene_names.visible = False
self.gene_names.disabled = True
# self.gene_names.disabled = True

polygons_detail, hover_tool, vdims = self._create_polygons(color_by_value)

Expand Down

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