def preprocess(self, min_num_genes = None, min_num_cells = None,
expr_cutoff = 1, loess_frac = 0.01, n_genes = None,
nb_pct = 0.1, n_cluster = 15):
'''
Do the filtration, variable gene selection and dimension reduction to
the dataset. By default it also performs a KMeans clustering for
labeling small outlier groups.
Arguments:
min_num_genes - int, optional (default: None).
Minimum number of genes expressed. For filtering
cells.
min_num_cells - int, optional (default: max(5, (nCells * 0.001)) ).
Minimum number of cells expressing one gene. For
filtering genes.
expr_cutoff - float, optional (default: 1).
Expression cutoff. If greater than expr_cutoff, the
gene is considered 'expressed'.
loess_frac - float, optional (default: 0.01). Between 0 and 1.
The fraction of the data used when estimating each
y-value in LOWESS function. For selecting variable
genes.
n_genes - int, optional (default: None).
Number of variable genes to use. Will be automatically
detected by stream if not specified.
nb_pct - float, optional (default: 0.1).
The percentage neighbor cells. For doing MLLE
dimension reduction. Smaller number such as 0.01 is
recommended when dealing with large dataset with more
than 10,000 cells.
n_cluster - int, optional (default: 15).
The number of cluster when it by default run a KMeans
clustering to help remove the small outlier groups.
'''
st.filter_cells(self.adata, min_num_genes = min_num_genes)
if min_num_cells == None:
min_num_cells = max(5, int(round(self.adata.shape[0] * 0.001)))
st.filter_genes(self.adata, min_num_cells = min_num_cells, expr_cutoff = expr_cutoff)
st.select_variable_genes(self.adata, loess_frac = loess_frac, n_genes = n_genes)
st.dimension_reduction(self.adata, nb_pct = nb_pct)
print('Initial UMAP visualization')
st.plot_visualization_2D(self.adata, fig_legend_ncol = 4)
self.kmeans = KMeans(n_cluster = n_cluster, init = 'k-means++')
self.kmeans.fit(self.adata.obsm['X_vis_umap'])
self._labelByCluster()
print('Clustered UMAP visualization')
st.plot_visualization_2D(self.adata, fig_legend_ncol = 4)
self.backup(key = 'kmeans')
print('If you want to remove some small groups of cells indicated by ')
print('the clustering, Run streamSingleCellSamples.removeSmallCluster([\'cN\', ...])')
print('And then run streamSingleCellSamples.confirmRemoval()')
def main():
sns.set_style('white')
sns.set_context('poster')
parser = argparse.ArgumentParser(
description='%s Parameters' % __tool_name__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-m",
"--matrix",
dest="input_filename",
default=None,
help="input file name",
metavar="FILE")
parser.add_argument("-l",
"--cell_labels",
dest="cell_label_filename",
default=None,
help="filename of cell labels")
parser.add_argument("-c",
"--cell_labels_colors",
dest="cell_label_color_filename",
default=None,
help="filename of cell label colors")
parser.add_argument(
"-s",
"--select_features",
dest="s_method",
default='LOESS',
help=
"LOESS,PCA or all: Select variable genes using LOESS or principal components using PCA or all the genes are kept"
)
parser.add_argument("--TG",
"--detect_TG_genes",
dest="flag_gene_TG_detection",
action="store_true",
help="detect transition genes automatically")
parser.add_argument("--DE",
"--detect_DE_genes",
dest="flag_gene_DE_detection",
action="store_true",
help="detect DE genes automatically")
parser.add_argument("--LG",
"--detect_LG_genes",
dest="flag_gene_LG_detection",
action="store_true",
help="detect leaf genes automatically")
parser.add_argument(
"-g",
"--genes",
dest="genes",
default=None,
help=
"genes to visualize, it can either be filename which contains all the genes in one column or a set of gene names separated by comma"
)
parser.add_argument(
"-p",
"--use_precomputed",
dest="use_precomputed",
action="store_true",
help=
"use precomputed data files without re-computing structure learning part"
)
parser.add_argument("--new",
dest="new_filename",
default=None,
help="file name of data to be mapped")
parser.add_argument("--new_l",
dest="new_label_filename",
default=None,
help="filename of new cell labels")
parser.add_argument("--new_c",
dest="new_label_color_filename",
default=None,
help="filename of new cell label colors")
parser.add_argument("--log2",
dest="flag_log2",
action="store_true",
help="perform log2 transformation")
parser.add_argument("--norm",
dest="flag_norm",
action="store_true",
help="normalize data based on library size")
parser.add_argument("--atac",
dest="flag_atac",
action="store_true",
help="indicate scATAC-seq data")
parser.add_argument(
"--n_jobs",
dest="n_jobs",
type=int,
default=1,
help="Specify the number of processes to use. (default, 1")
parser.add_argument(
"--loess_frac",
dest="loess_frac",
type=float,
default=0.1,
help="The fraction of the data used in LOESS regression")
parser.add_argument(
"--loess_cutoff",
dest="loess_cutoff",
type=int,
default=95,
help=
"the percentile used in variable gene selection based on LOESS regression"
)
parser.add_argument("--pca_first_PC",
dest="flag_first_PC",
action="store_true",
help="keep first PC")
parser.add_argument("--pca_n_PC",
dest="pca_n_PC",
type=int,
default=15,
help="The number of selected PCs,it's 15 by default")
parser.add_argument(
"--dr_method",
dest="dr_method",
default='se',
help=
"Method used for dimension reduction. Choose from {{'se','mlle','umap','pca'}}"
)
parser.add_argument("--n_neighbors",
dest="n_neighbors",
type=float,
default=50,
help="The number of neighbor cells")
parser.add_argument(
"--nb_pct",
dest="nb_pct",
type=float,
default=None,
help=
"The percentage of neighbor cells (when sepcified, it will overwrite n_neighbors)."
)
parser.add_argument("--n_components",
dest="n_components",
type=int,
default=3,
help="Number of components to keep.")
parser.add_argument(
"--clustering",
dest="clustering",
default='kmeans',
help=
"Clustering method used for seeding the intial structure, choose from 'ap','kmeans','sc'"
)
parser.add_argument("--damping",
dest="damping",
type=float,
default=0.75,
help="Affinity Propagation: damping factor")
parser.add_argument(
"--n_clusters",
dest="n_clusters",
type=int,
default=10,
help="Number of clusters for spectral clustering or kmeans")
parser.add_argument("--EPG_n_nodes",
dest="EPG_n_nodes",
type=int,
default=50,
help=" Number of nodes for elastic principal graph")
parser.add_argument(
"--EPG_lambda",
dest="EPG_lambda",
type=float,
default=0.02,
help="lambda parameter used to compute the elastic energy")
parser.add_argument("--EPG_mu",
dest="EPG_mu",
type=float,
default=0.1,
help="mu parameter used to compute the elastic energy")
parser.add_argument(
"--EPG_trimmingradius",
dest="EPG_trimmingradius",
type=float,
default=np.inf,
help="maximal distance of point from a node to affect its embedment")
parser.add_argument(
"--EPG_alpha",
dest="EPG_alpha",
type=float,
default=0.02,
help=
"positive numeric, the value of the alpha parameter of the penalized elastic energy"
)
parser.add_argument("--EPG_collapse",
dest="flag_EPG_collapse",
action="store_true",
help="collapsing small branches")
parser.add_argument(
"--EPG_collapse_mode",
dest="EPG_collapse_mode",
default="PointNumber",
help=
"the mode used to collapse branches. PointNumber,PointNumber_Extrema, PointNumber_Leaves,EdgesNumber or EdgesLength"
)
parser.add_argument(
"--EPG_collapse_par",
dest="EPG_collapse_par",
type=float,
default=5,
help=
"positive numeric, the cotrol paramter used for collapsing small branches"
)
parser.add_argument("--disable_EPG_optimize",
dest="flag_disable_EPG_optimize",
action="store_true",
help="disable optimizing branching")
parser.add_argument("--EPG_shift",
dest="flag_EPG_shift",
action="store_true",
help="shift branching point ")
parser.add_argument(
"--EPG_shift_mode",
dest="EPG_shift_mode",
default='NodeDensity',
help=
"the mode to use to shift the branching points NodePoints or NodeDensity"
)
parser.add_argument(
"--EPG_shift_DR",
dest="EPG_shift_DR",
type=float,
default=0.05,
help=
"positive numeric, the radius to be used when computing point density if EPG_shift_mode is NodeDensity"
)
parser.add_argument(
"--EPG_shift_maxshift",
dest="EPG_shift_maxshift",
type=int,
default=5,
help=
"positive integer, the maxium distance (as number of edges) to consider when exploring the branching point neighborhood"
)
parser.add_argument("--disable_EPG_ext",
dest="flag_disable_EPG_ext",
action="store_true",
help="disable extending leaves with additional nodes")
parser.add_argument(
"--EPG_ext_mode",
dest="EPG_ext_mode",
default='QuantDists',
help=
" the mode used to extend the graph,QuantDists, QuantCentroid or WeigthedCentroid"
)
parser.add_argument(
"--EPG_ext_par",
dest="EPG_ext_par",
type=float,
default=0.5,
help=
"the control parameter used for contribution of the different data points when extending leaves with nodes"
)
parser.add_argument("--DE_zscore_cutoff",
dest="DE_zscore_cutoff",
default=2,
help="Differentially Expressed Genes z-score cutoff")
parser.add_argument(
"--DE_logfc_cutoff",
dest="DE_logfc_cutoff",
default=0.25,
help="Differentially Expressed Genes log fold change cutoff")
parser.add_argument("--TG_spearman_cutoff",
dest="TG_spearman_cutoff",
default=0.4,
help="Transition Genes Spearman correlation cutoff")
parser.add_argument("--TG_logfc_cutoff",
dest="TG_logfc_cutoff",
default=0.25,
help="Transition Genes log fold change cutoff")
parser.add_argument("--LG_zscore_cutoff",
dest="LG_zscore_cutoff",
default=1.5,
help="Leaf Genes z-score cutoff")
parser.add_argument("--LG_pvalue_cutoff",
dest="LG_pvalue_cutoff",
default=1e-2,
help="Leaf Genes p value cutoff")
parser.add_argument(
"--umap",
dest="flag_umap",
action="store_true",
help="whether to use UMAP for visualization (default: No)")
parser.add_argument("-r",
dest="root",
default=None,
help="root node for subwaymap_plot and stream_plot")
parser.add_argument("--stream_log_view",
dest="flag_stream_log_view",
action="store_true",
help="use log2 scale for y axis of stream_plot")
parser.add_argument("-o",
"--output_folder",
dest="output_folder",
default=None,
help="Output folder")
parser.add_argument("--for_web",
dest="flag_web",
action="store_true",
help="Output files for website")
parser.add_argument(
"--n_genes",
dest="n_genes",
type=int,
default=5,
help=
"Number of top genes selected from each output marker gene file for website gene visualization"
)
args = parser.parse_args()
if (args.input_filename is None) and (args.new_filename is None):
parser.error("at least one of -m, --new required")
new_filename = args.new_filename
new_label_filename = args.new_label_filename
new_label_color_filename = args.new_label_color_filename
flag_stream_log_view = args.flag_stream_log_view
flag_gene_TG_detection = args.flag_gene_TG_detection
flag_gene_DE_detection = args.flag_gene_DE_detection
flag_gene_LG_detection = args.flag_gene_LG_detection
flag_web = args.flag_web
flag_first_PC = args.flag_first_PC
flag_umap = args.flag_umap
genes = args.genes
DE_zscore_cutoff = args.DE_zscore_cutoff
DE_logfc_cutoff = args.DE_logfc_cutoff
TG_spearman_cutoff = args.TG_spearman_cutoff
TG_logfc_cutoff = args.TG_logfc_cutoff
LG_zscore_cutoff = args.LG_zscore_cutoff
LG_pvalue_cutoff = args.LG_pvalue_cutoff
root = args.root
input_filename = args.input_filename
cell_label_filename = args.cell_label_filename
cell_label_color_filename = args.cell_label_color_filename
s_method = args.s_method
use_precomputed = args.use_precomputed
n_jobs = args.n_jobs
loess_frac = args.loess_frac
loess_cutoff = args.loess_cutoff
pca_n_PC = args.pca_n_PC
flag_log2 = args.flag_log2
flag_norm = args.flag_norm
flag_atac = args.flag_atac
dr_method = args.dr_method
nb_pct = args.nb_pct # neighbour percent
n_neighbors = args.n_neighbors
n_components = args.n_components #number of components to keep
clustering = args.clustering
damping = args.damping
n_clusters = args.n_clusters
EPG_n_nodes = args.EPG_n_nodes
EPG_lambda = args.EPG_lambda
EPG_mu = args.EPG_mu
EPG_trimmingradius = args.EPG_trimmingradius
EPG_alpha = args.EPG_alpha
flag_EPG_collapse = args.flag_EPG_collapse
EPG_collapse_mode = args.EPG_collapse_mode
EPG_collapse_par = args.EPG_collapse_par
flag_EPG_shift = args.flag_EPG_shift
EPG_shift_mode = args.EPG_shift_mode
EPG_shift_DR = args.EPG_shift_DR
EPG_shift_maxshift = args.EPG_shift_maxshift
flag_disable_EPG_optimize = args.flag_disable_EPG_optimize
flag_disable_EPG_ext = args.flag_disable_EPG_ext
EPG_ext_mode = args.EPG_ext_mode
EPG_ext_par = args.EPG_ext_par
output_folder = args.output_folder #work directory
n_genes = args.n_genes
if (flag_web):
flag_savefig = False
else:
flag_savefig = True
gene_list = []
if (genes != None):
if (os.path.exists(genes)):
gene_list = pd.read_csv(genes,
sep='\t',
header=None,
index_col=None,
compression='gzip' if genes.split('.')[-1]
== 'gz' else None).iloc[:, 0].tolist()
gene_list = list(set(gene_list))
else:
gene_list = genes.split(',')
print('Genes to visualize: ')
print(gene_list)
if (new_filename is None):
if (output_folder == None):
workdir = os.path.join(os.getcwd(), 'stream_result')
else:
workdir = output_folder
if (use_precomputed):
print('Importing the precomputed pkl file...')
adata = st.read(file_name='stream_result.pkl',
file_format='pkl',
file_path=workdir,
workdir=workdir)
else:
if (flag_atac):
print('Reading in atac zscore matrix...')
adata = st.read(file_name=input_filename,
workdir=workdir,
experiment='atac-seq')
else:
adata = st.read(file_name=input_filename, workdir=workdir)
print('Input: ' + str(adata.obs.shape[0]) + ' cells, ' +
str(adata.var.shape[0]) + ' genes')
adata.var_names_make_unique()
adata.obs_names_make_unique()
if (cell_label_filename != None):
st.add_cell_labels(adata, file_name=cell_label_filename)
else:
st.add_cell_labels(adata)
if (cell_label_color_filename != None):
st.add_cell_colors(adata, file_name=cell_label_color_filename)
else:
st.add_cell_colors(adata)
if (flag_atac):
print('Selecting top principal components...')
st.select_top_principal_components(adata,
n_pc=pca_n_PC,
first_pc=flag_first_PC,
save_fig=True)
st.dimension_reduction(adata,
method=dr_method,
n_components=n_components,
n_neighbors=n_neighbors,
nb_pct=nb_pct,
n_jobs=n_jobs,
feature='top_pcs')
else:
if (flag_norm):
st.normalize_per_cell(adata)
if (flag_log2):
st.log_transform(adata)
if (s_method != 'all'):
print('Filtering genes...')
st.filter_genes(adata, min_num_cells=5)
print('Removing mitochondrial genes...')
st.remove_mt_genes(adata)
if (s_method == 'LOESS'):
print('Selecting most variable genes...')
st.select_variable_genes(adata,
loess_frac=loess_frac,
percentile=loess_cutoff,
save_fig=True)
pd.DataFrame(adata.uns['var_genes']).to_csv(
os.path.join(workdir,
'selected_variable_genes.tsv'),
sep='\t',
index=None,
header=False)
st.dimension_reduction(adata,
method=dr_method,
n_components=n_components,
n_neighbors=n_neighbors,
nb_pct=nb_pct,
n_jobs=n_jobs,
feature='var_genes')
if (s_method == 'PCA'):
print('Selecting top principal components...')
st.select_top_principal_components(
adata,
n_pc=pca_n_PC,
first_pc=flag_first_PC,
save_fig=True)
st.dimension_reduction(adata,
method=dr_method,
n_components=n_components,
n_neighbors=n_neighbors,
nb_pct=nb_pct,
n_jobs=n_jobs,
feature='top_pcs')
else:
print('Keep all the genes...')
st.dimension_reduction(adata,
n_components=n_components,
n_neighbors=n_neighbors,
nb_pct=nb_pct,
n_jobs=n_jobs,
feature='all')
st.plot_dimension_reduction(adata, save_fig=flag_savefig)
st.seed_elastic_principal_graph(adata,
clustering=clustering,
damping=damping,
n_clusters=n_clusters)
st.plot_branches(
adata,
save_fig=flag_savefig,
fig_name='seed_elastic_principal_graph_skeleton.pdf')
st.plot_branches_with_cells(
adata,
save_fig=flag_savefig,
fig_name='seed_elastic_principal_graph.pdf')
st.elastic_principal_graph(adata,
epg_n_nodes=EPG_n_nodes,
epg_lambda=EPG_lambda,
epg_mu=EPG_mu,
epg_trimmingradius=EPG_trimmingradius,
epg_alpha=EPG_alpha)
st.plot_branches(adata,
save_fig=flag_savefig,
fig_name='elastic_principal_graph_skeleton.pdf')
st.plot_branches_with_cells(adata,
save_fig=flag_savefig,
fig_name='elastic_principal_graph.pdf')
if (not flag_disable_EPG_optimize):
st.optimize_branching(adata,
epg_trimmingradius=EPG_trimmingradius)
st.plot_branches(
adata,
save_fig=flag_savefig,
fig_name='optimizing_elastic_principal_graph_skeleton.pdf')
st.plot_branches_with_cells(
adata,
save_fig=flag_savefig,
fig_name='optimizing_elastic_principal_graph.pdf')
if (flag_EPG_shift):
st.shift_branching(adata,
epg_shift_mode=EPG_shift_mode,
epg_shift_radius=EPG_shift_DR,
epg_shift_max=EPG_shift_maxshift,
epg_trimmingradius=EPG_trimmingradius)
st.plot_branches(
adata,
save_fig=flag_savefig,
fig_name='shifting_elastic_principal_graph_skeleton.pdf')
st.plot_branches_with_cells(
adata,
save_fig=flag_savefig,
fig_name='shifting_elastic_principal_graph.pdf')
if (flag_EPG_collapse):
st.prune_elastic_principal_graph(
adata,
epg_collapse_mode=EPG_collapse_mode,
epg_collapse_par=EPG_collapse_par,
epg_trimmingradius=EPG_trimmingradius)
st.plot_branches(
adata,
save_fig=flag_savefig,
fig_name='pruning_elastic_principal_graph_skeleton.pdf')
st.plot_branches_with_cells(
adata,
save_fig=flag_savefig,
fig_name='pruning_elastic_principal_graph.pdf')
if (not flag_disable_EPG_ext):
st.extend_elastic_principal_graph(
adata,
epg_ext_mode=EPG_ext_mode,
epg_ext_par=EPG_ext_par,
epg_trimmingradius=EPG_trimmingradius)
st.plot_branches(
adata,
save_fig=flag_savefig,
fig_name='extending_elastic_principal_graph_skeleton.pdf')
st.plot_branches_with_cells(
adata,
save_fig=flag_savefig,
fig_name='extending_elastic_principal_graph.pdf')
st.plot_branches(
adata,
save_fig=flag_savefig,
fig_name='finalized_elastic_principal_graph_skeleton.pdf')
st.plot_branches_with_cells(
adata,
save_fig=flag_savefig,
fig_name='finalized_elastic_principal_graph.pdf')
st.plot_flat_tree(adata, save_fig=flag_savefig)
if (flag_umap):
print('UMAP visualization based on top MLLE components...')
st.plot_visualization_2D(adata,
save_fig=flag_savefig,
fig_name='umap_cells')
st.plot_visualization_2D(adata,
color_by='branch',
save_fig=flag_savefig,
fig_name='umap_branches')
if (root is None):
print('Visualization of subwaymap and stream plots...')
flat_tree = adata.uns['flat_tree']
list_node_start = [
value for key, value in nx.get_node_attributes(
flat_tree, 'label').items()
]
for ns in list_node_start:
if (flag_web):
st.subwaymap_plot(adata,
percentile_dist=100,
root=ns,
save_fig=flag_savefig)
st.stream_plot(adata,
root=ns,
fig_size=(8, 8),
save_fig=True,
flag_log_view=flag_stream_log_view,
fig_legend=False,
fig_name='stream_plot.png')
else:
st.subwaymap_plot(adata,
percentile_dist=100,
root=ns,
save_fig=flag_savefig)
st.stream_plot(adata,
root=ns,
fig_size=(8, 8),
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
else:
st.subwaymap_plot(adata,
percentile_dist=100,
root=root,
save_fig=flag_savefig)
st.stream_plot(adata,
root=root,
fig_size=(8, 8),
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
output_cell_info(adata)
if (flag_web):
output_for_website(adata)
st.write(adata)
if (flag_gene_TG_detection):
print('Identifying transition genes...')
st.detect_transistion_genes(adata,
cutoff_spearman=TG_spearman_cutoff,
cutoff_logfc=TG_logfc_cutoff,
n_jobs=n_jobs)
if (flag_web):
## Plot top5 genes
flat_tree = adata.uns['flat_tree']
list_node_start = [
value for key, value in nx.get_node_attributes(
flat_tree, 'label').items()
]
gene_list = []
for x in adata.uns['transition_genes'].keys():
gene_list = gene_list + adata.uns['transition_genes'][
x].index[:n_genes].tolist()
gene_list = np.unique(gene_list)
for ns in list_node_start:
output_for_website_subwaymap_gene(adata, gene_list)
st.stream_plot_gene(adata,
root=ns,
fig_size=(8, 8),
genes=gene_list,
save_fig=True,
flag_log_view=flag_stream_log_view,
fig_format='png')
else:
st.plot_transition_genes(adata, save_fig=flag_savefig)
if (flag_gene_DE_detection):
print('Identifying differentially expressed genes...')
st.detect_de_genes(adata,
cutoff_zscore=DE_logfc_cutoff,
cutoff_logfc=DE_logfc_cutoff,
n_jobs=n_jobs)
if (flag_web):
flat_tree = adata.uns['flat_tree']
list_node_start = [
value for key, value in nx.get_node_attributes(
flat_tree, 'label').items()
]
gene_list = []
for x in adata.uns['de_genes_greater'].keys():
gene_list = gene_list + adata.uns['de_genes_greater'][
x].index[:n_genes].tolist()
for x in adata.uns['de_genes_less'].keys():
gene_list = gene_list + adata.uns['de_genes_less'][
x].index[:n_genes].tolist()
gene_list = np.unique(gene_list)
for ns in list_node_start:
output_for_website_subwaymap_gene(adata, gene_list)
st.stream_plot_gene(adata,
root=ns,
fig_size=(8, 8),
genes=gene_list,
save_fig=True,
flag_log_view=flag_stream_log_view,
fig_format='png')
else:
st.plot_de_genes(adata, save_fig=flag_savefig)
if (flag_gene_LG_detection):
print('Identifying leaf genes...')
st.detect_leaf_genes(adata,
cutoff_zscore=LG_zscore_cutoff,
cutoff_pvalue=LG_pvalue_cutoff,
n_jobs=n_jobs)
if (flag_web):
## Plot top5 genes
flat_tree = adata.uns['flat_tree']
list_node_start = [
value for key, value in nx.get_node_attributes(
flat_tree, 'label').items()
]
gene_list = []
for x in adata.uns['leaf_genes'].keys():
gene_list = gene_list + adata.uns['leaf_genes'][
x].index[:n_genes].tolist()
gene_list = np.unique(gene_list)
for ns in list_node_start:
output_for_website_subwaymap_gene(adata, gene_list)
st.stream_plot_gene(adata,
root=ns,
fig_size=(8, 8),
genes=gene_list,
save_fig=True,
flag_log_view=flag_stream_log_view,
fig_format='png')
if ((genes != None) and (len(gene_list) > 0)):
print('Visualizing genes...')
flat_tree = adata.uns['flat_tree']
list_node_start = [
value for key, value in nx.get_node_attributes(
flat_tree, 'label').items()
]
if (root is None):
for ns in list_node_start:
if (flag_web):
output_for_website_subwaymap_gene(adata, gene_list)
st.stream_plot_gene(adata,
root=ns,
fig_size=(8, 8),
genes=gene_list,
save_fig=True,
flag_log_view=flag_stream_log_view,
fig_format='png')
else:
st.subwaymap_plot_gene(adata,
percentile_dist=100,
root=ns,
genes=gene_list,
save_fig=flag_savefig)
st.stream_plot_gene(adata,
root=ns,
fig_size=(8, 8),
genes=gene_list,
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
else:
if (flag_web):
output_for_website_subwaymap_gene(adata, gene_list)
st.stream_plot_gene(adata,
root=root,
fig_size=(8, 8),
genes=gene_list,
save_fig=True,
flag_log_view=flag_stream_log_view,
fig_format='png')
else:
st.subwaymap_plot_gene(adata,
percentile_dist=100,
root=root,
genes=gene_list,
save_fig=flag_savefig)
st.stream_plot_gene(adata,
root=root,
fig_size=(8, 8),
genes=gene_list,
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
else:
print('Starting mapping procedure...')
if (output_folder == None):
workdir_ref = os.path.join(os.getcwd(), 'stream_result')
else:
workdir_ref = output_folder
adata = st.read(file_name='stream_result.pkl',
file_format='pkl',
file_path=workdir_ref,
workdir=workdir_ref)
workdir = os.path.join(workdir_ref, os.pardir, 'mapping_result')
adata_new = st.read(file_name=new_filename, workdir=workdir)
st.add_cell_labels(adata_new, file_name=new_label_filename)
st.add_cell_colors(adata_new, file_name=new_label_color_filename)
if (s_method == 'LOESS'):
st.map_new_data(adata, adata_new, feature='var_genes')
if (s_method == 'all'):
st.map_new_data(adata, adata_new, feature='all')
if (flag_umap):
st.plot_visualization_2D(adata,
adata_new=adata_new,
use_precomputed=False,
save_fig=flag_savefig,
fig_name='umap_new_cells')
st.plot_visualization_2D(adata,
adata_new=adata_new,
show_all_colors=True,
save_fig=flag_savefig,
fig_name='umap_all_cells')
st.plot_visualization_2D(adata,
adata_new=adata_new,
color_by='branch',
save_fig=flag_savefig,
fig_name='umap_branches')
if (root is None):
flat_tree = adata.uns['flat_tree']
list_node_start = [
value for key, value in nx.get_node_attributes(
flat_tree, 'label').items()
]
for ns in list_node_start:
st.subwaymap_plot(adata,
adata_new=adata_new,
percentile_dist=100,
show_all_cells=False,
root=ns,
save_fig=flag_savefig)
st.stream_plot(adata,
adata_new=adata_new,
show_all_colors=False,
root=ns,
fig_size=(8, 8),
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
else:
st.subwaymap_plot(adata,
adata_new=adata_new,
percentile_dist=100,
show_all_cells=False,
root=root,
save_fig=flag_savefig)
st.stream_plot(adata,
adata_new=adata_new,
show_all_colors=False,
root=root,
fig_size=(8, 8),
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
if ((genes != None) and (len(gene_list) > 0)):
if (root is None):
for ns in list_node_start:
st.subwaymap_plot_gene(adata,
adata_new=adata_new,
percentile_dist=100,
root=ns,
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
else:
st.subwaymap_plot_gene(adata,
adata_new=adata_new,
percentile_dist=100,
root=root,
save_fig=flag_savefig,
flag_log_view=flag_stream_log_view)
st.write(adata_new, file_name='stream_mapping_result.pkl')
print('Finished computation.')
if (p["log2"]):
st.log_transform(adata)
st.filter_genes(adata,
min_num_cells=max(5, int(round(adata.shape[0] * 0.001))),
min_pct_cells=None,
expr_cutoff=1)
if (adata.shape[1] < 1000):
adata.uns['var_genes'] = gene_ids
adata.obsm['var_genes'] = adata.X
else:
st.select_variable_genes(adata, loess_frac=p["loess_frac"])
st.dimension_reduction(adata,
nb_pct=p["nb_pct"],
n_components=p["n_components"],
n_jobs=p["n_jobs"],
method=p["method"])
st.plot_dimension_reduction(adata,
n_components=p["n_components"],
save_fig=p["save_fig"])
st.plot_visualization_2D(adata, save_fig=p["save_fig"], nb_pct=p["nb_pct"])
st.seed_elastic_principal_graph(adata,
clustering=p["clustering"],
n_clusters=p["n_clusters"],
nb_pct=p["nb_pct"])
st.elastic_principal_graph(adata,
epg_alpha=p["epg_alpha"],
save_fig=p["save_fig"])
"8": "#FFFF00",
"9": "#FFD700",
"10": "#8B658B",
"11": "#FF6A6A",
"12": "#FFD39B",
"13": "#EE2C2C",
"14": "#BF3EFF"
}
adata.obs["label_color"] = [label_color[x] for x in adata.obs["label"]]
# st.select_variable_genes(adata,loess_frac=0.01, percentile=90)
print("### dimension_reduction")
st.dimension_reduction(adata,
n_neighbors=50,
feature="var_genes",
method="se",
n_components=3,
n_jobs=8)
st.plot_dimension_reduction(adata, save_fig=True, fig_size=[16, 16])
print("### seed_elastic_principal_graph")
st.seed_elastic_principal_graph(adata, n_clusters=10)
st.plot_branches(adata,
save_fig=True,
fig_size=[16, 16],
fig_name="branches_seed.pdf")
st.plot_branches_with_cells(adata,
fig_legend=False,
save_fig=True,
fig_size=[16, 16],
fig_name="branches_with_cells_seed.pdf")
### CHECK FOR VARIABLE GENES
# Check if the blue (loess) curve fits the points well
st.select_variable_genes(adata)
# Open plot file
plt.savefig('loess.png')
# Close Plot
plt.close('loess.png')
# Adjust the blue curve to fits better
st.select_variable_genes(adata, loess_frac=0.01)
plt.savefig('adjust_loess.png')
plt.close('adjust_loess.png')
# Dimension reduction
st.dimension_reduction(adata, method='se', n_components=3, nb_pct=0.01)
#st.plot_dimension_reduction(adata)
plt.savefig('se_reduction.png')
plt.close('se_reduction.png')
# Plot UMAP
st.plot_visualization_2D(adata)
plt.savefig('umap.png')
plt.close('umap.png')
# Clustering to get branches
st.seed_elastic_principal_graph(adata, clustering="kmeans")
st.plot_branches(adata)
plt.savefig('branches.png')
min_num_cells=max(5, int(round(adata.shape[0] * 0.001))),
min_pct_cells=None,
expr_cutoff=1)
if (adata.shape[1] < 1000):
adata.uns['var_genes'] = gene_ids
adata.obsm['var_genes'] = adata.X
else:
st.select_variable_genes(adata,
loess_frac=p["loess_frac"],
percentile=p["percentile"])
#p["nb_pct"] = p["nb_num"]/adata.obs.shape[0]
st.dimension_reduction(adata,
n_neighbors=p["n_neighbors"],
n_components=p["n_components"],
n_jobs=p["n_jobs"],
method=p["method"])
# st.plot_dimension_reduction(adata,n_components = p["n_components"],save_fig=p["save_fig"])
# st.plot_visualization_2D(adata,save_fig=p["save_fig"],n_neighbors=p["n_neighbors"])
st.seed_elastic_principal_graph(adata,
clustering=p["clustering"],
n_clusters=p["n_clusters"],
n_neighbors=p["n_neighbors"])
st.elastic_principal_graph(adata,
epg_alpha=p["epg_alpha"],
save_fig=p["save_fig"])
if (len(adata.obs['branch_id'].unique()) > 1):
if (not p["disable_EPG_optimize"]):
def stream_test_Nestorowa_2016():
workdir = os.path.join(_root, 'datasets/Nestorowa_2016/')
temp_folder = tempfile.gettempdir()
tar = tarfile.open(workdir + 'output/stream_result.tar.gz')
tar.extractall(path=temp_folder)
tar.close()
ref_temp_folder = os.path.join(temp_folder, 'stream_result')
print(workdir + 'data_Nestorowa.tsv.gz')
input_file = os.path.join(workdir, 'data_Nestorowa.tsv.gz')
label_file = os.path.join(workdir, 'cell_label.tsv.gz')
label_color_file = os.path.join(workdir, 'cell_label_color.tsv.gz')
comp_temp_folder = os.path.join(temp_folder, 'stream_result_comp')
try:
st.set_figure_params(dpi=80,
style='white',
figsize=[5.4, 4.8],
rc={'image.cmap': 'viridis'})
adata = st.read(file_name=input_file, workdir=comp_temp_folder)
adata.var_names_make_unique()
adata.obs_names_make_unique()
st.add_cell_labels(adata, file_name=label_file)
st.add_cell_colors(adata, file_name=label_color_file)
st.cal_qc(adata, assay='rna')
st.filter_features(adata, min_n_cells=5)
st.select_variable_genes(adata, n_genes=2000, save_fig=True)
st.select_top_principal_components(adata,
feature='var_genes',
first_pc=True,
n_pc=30,
save_fig=True)
st.dimension_reduction(adata,
method='se',
feature='top_pcs',
n_neighbors=100,
n_components=4,
n_jobs=2)
st.plot_dimension_reduction(adata,
color=['label', 'Gata1', 'n_genes'],
n_components=3,
show_graph=False,
show_text=False,
save_fig=True,
fig_name='dimension_reduction.pdf')
st.plot_visualization_2D(adata,
method='umap',
n_neighbors=100,
color=['label', 'Gata1', 'n_genes'],
use_precomputed=False,
save_fig=True,
fig_name='visualization_2D.pdf')
st.seed_elastic_principal_graph(adata, n_clusters=20)
st.plot_dimension_reduction(adata,
color=['label', 'Gata1', 'n_genes'],
n_components=2,
show_graph=True,
show_text=False,
save_fig=True,
fig_name='dr_seed.pdf')
st.plot_branches(adata,
show_text=True,
save_fig=True,
fig_name='branches_seed.pdf')
st.elastic_principal_graph(adata,
epg_alpha=0.01,
epg_mu=0.05,
epg_lambda=0.01)
st.plot_dimension_reduction(adata,
color=['label', 'Gata1', 'n_genes'],
n_components=2,
show_graph=True,
show_text=False,
save_fig=True,
fig_name='dr_epg.pdf')
st.plot_branches(adata,
show_text=True,
save_fig=True,
fig_name='branches_epg.pdf')
###Extend leaf branch to reach further cells
st.extend_elastic_principal_graph(adata,
epg_ext_mode='QuantDists',
epg_ext_par=0.8)
st.plot_dimension_reduction(adata,
color=['label'],
n_components=2,
show_graph=True,
show_text=True,
save_fig=True,
fig_name='dr_extend.pdf')
st.plot_branches(adata,
show_text=True,
save_fig=True,
fig_name='branches_extend.pdf')
st.plot_visualization_2D(
adata,
method='umap',
n_neighbors=100,
color=['label', 'branch_id_alias', 'S4_pseudotime'],
use_precomputed=False,
save_fig=True,
fig_name='visualization_2D_2.pdf')
st.plot_flat_tree(adata,
color=['label', 'branch_id_alias', 'S4_pseudotime'],
dist_scale=0.5,
show_graph=True,
show_text=True,
save_fig=True)
st.plot_stream_sc(adata,
root='S4',
color=['label', 'Gata1'],
dist_scale=0.5,
show_graph=True,
show_text=False,
save_fig=True)
st.plot_stream(adata,
root='S4',
color=['label', 'Gata1'],
save_fig=True)
st.detect_leaf_markers(adata,
marker_list=adata.uns['var_genes'][:300],
root='S4',
n_jobs=4)
st.detect_transition_markers(adata,
root='S4',
marker_list=adata.uns['var_genes'][:300],
n_jobs=4)
st.detect_de_markers(adata,
marker_list=adata.uns['var_genes'][:300],
root='S4',
n_jobs=4)
# st.write(adata,file_name='stream_result.pkl')
except:
print("STREAM analysis failed!")
raise
else:
print("STREAM analysis finished!")
print(ref_temp_folder)
print(comp_temp_folder)
pathlist = Path(ref_temp_folder)
for path in pathlist.glob('**/*'):
if path.is_file() and (not path.name.startswith('.')):
file = os.path.relpath(str(path), ref_temp_folder)
print(file)
if (file.endswith('pdf')):
if (os.path.getsize(os.path.join(comp_temp_folder, file)) > 0):
print('The file %s passed' % file)
else:
raise Exception('Error! The file %s is not matched' % file)
else:
checklist = list()
df_ref = pd.read_csv(os.path.join(ref_temp_folder, file),
sep='\t')
# print(df_ref.shape)
# print(df_ref.head())
df_comp = pd.read_csv(os.path.join(comp_temp_folder, file),
sep='\t')
# print(df_comp.shape)
# print(df_comp.head())
for c in df_ref.columns:
# print(c)
if (is_numeric_dtype(df_ref[c])):
checklist.append(all(np.isclose(df_ref[c],
df_comp[c])))
else:
checklist.append(all(df_ref[c] == df_comp[c]))
if (all(checklist)):
print('The file %s passed' % file)
else:
raise Exception('Error! The file %s is not matched' % file)
print('Successful!')
rmtree(comp_temp_folder, ignore_errors=True)
rmtree(ref_temp_folder, ignore_errors=True)
def main():
sns.set_style('white')
sns.set_context('poster')
parser = argparse.ArgumentParser(
description='%s Parameters' % __tool_name__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"-m",
"--data-file",
dest="input_filename",
default=None,
help="input file name, pkl format from Stream preprocessing module",
metavar="FILE")
parser.add_argument("-of",
"--of",
dest="output_filename_prefix",
default="StreamiFSOutput",
help="output file name prefix")
parser.add_argument("-nb_pct",
"--percent_neighbor_cells",
dest="nb_pct",
type=float,
default=None,
help="")
parser.add_argument("-n_comp_k",
dest="n_comp_k",
type=int,
default=None,
help="")
parser.add_argument("-feat", dest="feature", default=None, help="feature")
parser.add_argument("-method", dest="method", default=None, help="")
parser.add_argument("-nc_plot",
dest="nc_plot",
type=int,
default=None,
help="")
parser.add_argument("-comp1", dest="comp1", default=None, help="feature")
parser.add_argument("-comp2",
dest="comp2",
type=int,
default=None,
help="")
parser.add_argument("-fig_width",
dest="fig_width",
type=int,
default=8,
help="")
parser.add_argument("-fig_height",
dest="fig_height",
type=int,
default=8,
help="")
parser.add_argument("-n_jobs", dest="n_jobs", type=int, default=2, help="")
parser.add_argument("-fig_legend_ncol",
dest="fig_legend_ncol",
type=int,
default=None,
help="")
args = parser.parse_args()
print(args)
print('Starting dimension reduction procedure...')
workdir = "./"
adata = st.read(file_name=args.input_filename,
file_format='pkl',
experiment='rna-seq',
workdir=workdir)
print("Feature ", args.feature, type(args.feature))
st.dimension_reduction(adata,
method=args.method,
feature='var_genes',
nb_pct=args.nb_pct,
n_components=args.n_comp_k,
n_jobs=args.n_jobs,
eigen_solver=None)
fig_size = (args.fig_width, args.fig_height)
st.plot_dimension_reduction(adata,
n_components=args.nc_plot,
comp1=args.comp1,
comp2=args.comp2,
save_fig=True,
fig_name=(args.output_filename_prefix +
'_stddev_dotplot.png'),
fig_path="./",
fig_size=fig_size,
fig_legend_ncol=args.fig_legend_ncol)
st.write(adata,
file_name=(args.output_filename_prefix + '_stream_result.pkl'),
file_path='./',
file_format='pkl')
print('Output: ' + str(adata.obs.shape[0]) + ' cells, ' +
str(adata.var.shape[0]) + ' genes')
print('Finished computation.')