def main(save_folder, spatial_adata):
"""Read out data for ST and scRNA-seq DGE Analysis and create UMAPs for Figure 3A/E and Suppl. Figures 3
:return:
"""
spatial_cluster_label = 'tissue_type'
# load data
cytokines, allinone, cytoresps_dict = gene_lists.get_publication_cyto_resps(
)
leukocyte_markers = gene_lists.leukocyte_markers()
# remove all spots without a tissue label
spatial_adata = spatial_adata[
spatial_adata.obs[spatial_cluster_label] != 'Unknown']
# 1. get observable for cytokine genes
spatial_adata, obs_name = add_observables.convert_variable_to_observable(
adata=spatial_adata,
gene_names=cytokines,
task='cell_gene',
label='celltype',
condition=None)
spatial_adata, _ = add_observables.convert_variable_to_observable(
adata=spatial_adata,
gene_names=leukocyte_markers,
task='cell_gene',
label='celltype',
condition=None)
# # 2. Read out counts and metaData for DGE Analysis including double positive cytokine cells
# 2.1 Read out only leukocytes spots by 'CD2', 'CD3D', 'CD3E', 'CD3G', 'CD247' and 'PTPRC' surface markers
adata_leukocytes = get_celltypes_data(spatial_adata,
genes=leukocyte_markers)
# 2.2 Merge layers of epidermis and save it as epidermis and merge dermis depths and save it as dermis
adata_leukocytes = get_tissueregions(adata=adata_leukocytes,
tissue_label=spatial_cluster_label)
# 3. Highlicht tissues epidermis and dermis + cytokines and for each single cytokine
plot_tissueregions_cyto(adata=adata_leukocytes,
obs_name='cytokine_IL13',
title='Leukocytes_IL13',
save_folder=save_folder)
plot_tissueregions_cyto(adata=adata_leukocytes,
obs_name='cytokine_IFNG',
title='Leukocytes_IFNG',
save_folder=save_folder)
# 4. Read out all leukocyte positive spots
include_cytokine_dp(adata=adata_leukocytes,
cytokines=cytokines,
save_folder=save_folder,
label=spatial_cluster_label,
key='ST',
paper_figure='3AC_Leukocytes')
def exclude_cytokine_dp(adata, cytoresps_dict):
"""Exclusively label double positive cells as double positive
Parameters
----------
adata : annData
cytoresps_dict : dict
Returns
-------
adata : annData
obs_name : str
"""
cytodict = gene_lists.cyto_asdict()
# check if all genes are in adata
cells = list(set(adata.var.index) & set(cytodict.keys()))
remove_key = np.setdiff1d(list(cytoresps_dict.keys()), cells)
if len(remove_key) > 0:
cytodict.pop(remove_key[0], None)
adata, obs_name = add_observables.convert_variable_to_observable(
adata=adata,
gene_names=cytodict,
task='annotate_cells',
label='cytokines',
condition=[np.all, np.all, np.all])
return adata, obs_name
def main(save_folder, spatial_adata):
"""Read out data for ST DGE Analysis and create UMAPs for Figure 3A
:return:
"""
spatial_cluster_label = 'tissue_type'
# load data
cytokines, allinone, cytoresps_dict = gene_lists.get_publication_cyto_resps(
)
# remove all spots without a tissue label
spatial_adata = spatial_adata[
spatial_adata.obs[spatial_cluster_label] != 'Unknown']
# 1. get observable for cytokine genes
spatial_adata, obs_name = add_observables.convert_variable_to_observable(
adata=spatial_adata,
gene_names=cytokines,
task='cell_gene',
label='celltype',
condition=None)
# 2. Highlight tissues epidermis and dermis + cytokines and for each single cytokine
plot_tissuerlayers_cyto(adata=spatial_adata,
obs_name='cytokine_IL17A',
title='Wholedataset_IL17A',
save_folder=save_folder,
regions=spatial_cluster_label)
def main(save_folder, adata):
"""Read out spots for DGE analysis and create UMAP of single cell RNA-seq data for Suppl. Figures 4B
Parameters
----------
save_folder : str
adata : annData
Returns
-------
"""
sc_cluster_obs = 'cluster_labels'
# 1. load gene list
cytokines, allinone, cytoresps_dict = gene_lists.get_publication_cyto_resps(
)
leukocyte_markers = gene_lists.leukocyte_markers()
# 2. get observable for cytokine genes
adata, obs_name = add_observables.convert_variable_to_observable(
adata=adata,
gene_names=cytokines,
task='cell_gene',
label='celltype',
condition=None)
# Only Leukocytes:
# 3. Read out counts and metaData for DGE Analysis including double positive cytokine cells
# 3.1 Read out only T-cell spots by CD2 surface markers
adata_leukocytes = get_celltypes_data(adata, genes=leukocyte_markers)
# 3.3 Read out all leukocyte cells
include_cytokine_dp(adata=adata_leukocytes,
cytokines=cytokines,
save_folder=save_folder,
label=sc_cluster_obs,
key='SC_merged',
paper_figure='SC')
# 3.4 Add IL17A label to adata
adata_leukocytes = add_observables.add_columns_genes(
adata=adata_leukocytes, genes='IL17A', label='IL17A')
""" Figure 3D: Highlight IL-17A """
plot_annotated_cells(adata=adata_leukocytes,
color='IL17A_label',
paper_figure='D',
save_folder=save_folder,
key='SC',
title='Leukocytes_IL17A',
xpos=0.02,
ypos=0.95)
def get_expression_values(adata, gene):
"""Add gene count, label and cluster annotation as column to annData
Parameters
----------
adata : annData
gene : str or 'list' ['str']
Returns
-------
"""
# Get observable for gene
adata, obs_name = add_observables.convert_variable_to_observable(
adata=adata, gene_names=gene, task='cell_gene', label='celltype', condition=None)
return adata, obs_name
def main(save_folder, adata):
"""Read out spots for DGE analysis and create UMAP of single cell RNA-seq data for Suppl. Figures 4B
Parameters
----------
save_folder : str
adata : annData
Returns
-------
"""
sc_cluster_obs = 'cluster_labels'
# 1. load gene list
cytokines, allinone, cytoresps_dict = gene_lists.get_publication_cyto_resps()
leukocyte_markers = gene_lists.leukocyte_markers()
# 2. get observable for cytokine genes
adata, obs_name = add_observables.convert_variable_to_observable(
adata=adata, gene_names=cytokines, task='cell_gene', label='celltype', condition=None)
# Only Leukocytes:
# 3. Read out counts and metaData for DGE Analysis including double positive cytokine cells
# 3.1 Read out only T-cell spots by CD2 surface markers
adata_leukocytes = get_celltypes_data(adata, genes=leukocyte_markers)
# 3.3 Read out all leukocyte cells
include_cytokine_dp(adata=adata_leukocytes, cytokines=cytokines, save_folder=save_folder,
label=sc_cluster_obs, key='SC_merged', paper_figure='SC')
# 3.3 Read out all leukocyte cells but exclude double positive cytokine cells
adata_leukocytes, obs_name = exclude_cytokine_dp(adata=adata_leukocytes, cytoresps_dict=cytoresps_dict)
# Plot cytokines and highlight double positive
plot_annotated_cells(adata=adata_leukocytes, color='cytokines_others', paper_figure='',
save_folder=save_folder, key='SC', title="Leukocytes_IL17A_IFNG",
xpos=0.02, ypos=0.95, palette=["#ff7f00", "#377eb8", 'purple'])
# Add cytokine label to adata and Plot: Highlight cytokines
adata_leukocytes = add_observables.add_columns_genes(adata=adata_leukocytes, genes='IFNG', label='IFNG')
""" Suppl. Figure 4B: Highlight IFN-g """
plot_annotated_cells(adata=adata_leukocytes, color='IFNG_label', paper_figure='4B', save_folder=save_folder,
key='SC', title="Leukocyte_IFNG", xpos=0.02, ypos=0.95)
def main(save_folder, pp_adata, cluster_algorithm):
"""
1. scRNAseq data set
Read ou pre-processed Count matrix and apply Leiden clustering with resolution r = 0.1 for scRNAseq data set
Annotate clusters manually
2. ST data set
Visualise count matrix with tissue types
:return:
"""
# 1. load gene list
cytokines, allinone, cytoresps_dict = gene_lists.get_publication_cyto_resps(
)
# 2. Get observable for cytokine genes
pp_adata, _ = add_observables.convert_variable_to_observable(
adata=pp_adata,
gene_names=cytokines,
task='cell_gene',
label='celltype',
condition=None)
# 3. Apply cluster algorithm
pp_adata, key = apply_clusteralgo(adata=pp_adata,
algorithm=cluster_algorithm,
resolution=0.1)
# 4. Annotate clusters with expert opinion - before the best resolution r=0.1 was identified
pp_adata = annotate_cluster(adata=pp_adata,
cluster_algorithm=cluster_algorithm,
resolution=0.1)
# 5. Plot UMAP scRNAseq data
visualise_clusters(adata=pp_adata,
save_folder=save_folder,
key='cluster_labels',
title="SC")
def main(save_folder, spatial_adata):
"""
Read out data for ST and scRNA-seq DGE Analysis and create UMAPs for Figure 3A/E and Suppl. Figures 3
:return:
"""
spatial_cluster_label = 'tissue_type'
# 1. load gene lists
cytokines, allinone, cytoresps_dict = gene_lists.get_publication_cyto_resps(
)
leukocyte_markers = gene_lists.leukocyte_markers()
# 2. remove all spots without a tissue label
spatial_adata = spatial_adata[
spatial_adata.obs[spatial_cluster_label] != 'Unknown']
# 3. get observable for cytokine genes and leukocyte markers
spatial_adata, obs_name = add_observables.convert_variable_to_observable(
adata=spatial_adata,
gene_names=cytokines,
task='cell_gene',
label='celltype',
condition=None)
spatial_adata, obs_name = add_observables.convert_variable_to_observable(
adata=spatial_adata,
gene_names=leukocyte_markers,
task='cell_gene',
label='celltype',
condition=None)
# 4. Read out only leukocytes spots by 'CD2', 'CD3D', 'CD3E', 'CD3G', 'CD247' and 'PTPRC' surface markers
adata_leukocytes = get_celltypes_data(spatial_adata,
genes=leukocyte_markers)
# 5. add observable healthy_disease
spatial_adata = add_observables.add_disease_healthy_obs(spatial_adata)
# keys: 'patient', 'biopsy_type', 'disease', 'tissue_type'
# Suppl Figure 2A
visualise_clusters(adata=spatial_adata,
save_folder=save_folder,
key='healthy_disease',
title="Diagnoses")
# Suppl. Figure 2C
visualise_clusters(adata=adata_leukocytes,
save_folder=save_folder,
key='tissue_type',
title="Leukocytes_tissuelayers")
# 5. Read out spots which either have IL17A, IL13 or INFG genes
adata_leukocytes, obs_name = exclude_cytokine_dp(
adata=adata_leukocytes, cytoresps_dict=cytoresps_dict)
# 6. Merge layers of epidermis and save it as epidermis and merge dermis depths and save it as dermis
adata_leukocytes = get_tissueregions(adata=adata_leukocytes,
tissue_label=spatial_cluster_label)
# Suppl. Figure 2B
plot_tissueregions_cyto(adata=adata_leukocytes,
obs_name=obs_name,
title='Leukocytes_Cytokines',
save_folder=save_folder,
gene_colors=[
"#ff7f00", "#e41a1c", 'darkgoldenrod',
'purple', "#377eb8", 'deeppink'
])