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Adding an illustrated Walkthrough of the clustering example #3

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1c1e439
Update README.md
ZWael Nov 21, 2021
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Create Clustering Walkthrow.ipynb
ZWael Nov 21, 2021
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Delete Clustering Walkthrow.ipynb
ZWael Nov 21, 2021
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Add files via upload
ZWael Nov 21, 2021
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Walkthrough example and code modif to run properly
ZWael Nov 21, 2021
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A walkthrough runing well
ZWael Nov 21, 2021
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3 changes: 2 additions & 1 deletion README.md
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Expand Up @@ -7,6 +7,7 @@
1. Outliers detection and batch detection & correction are shown in <a href="https://nbviewer.jupyter.org/github/BostonGene/MFP/blob/master/Methods_Description_-_Batch_correction.ipynb">Methods_Description_-_Batch_correction.ipynb</a> (Melanoma data)
1. Clusters generation from scaled signatures is shown in <a href="clustering_example.py">clustering_example.py</a>. The process is the same for melanoma and pancan analysis
1. Finally, having a dataset with scaled signatures and known clusters we can classify another datasets using <a href="classification_example.py">classification_example.py</a>
1. A walkthrough of the clustering and classification (labeling to IE IE/F F or D) is illustarted in an illustrated python notebook.


_.ipynb files could by opened at https://nbviewer.jupyter.org/ or downloaded as HTML files from upstream_html folder_
Expand All @@ -30,4 +31,4 @@ The Molecular Functional (MF) Portrait is a planetary schematic representation o

Visual tool available at https://science.bostongene.com/tumor-portrait/<br>

© 2020 BostonGene Corporation.
© 2020 BostonGene Corporation.
48 changes: 48 additions & 0 deletions final_clusters.tsv
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MFP
GSM478912 D
GSM478913 D
GSM478937 D
GSM478939 D
GSM478946 D
GSM478947 D
GSM478948 D
GSM478914 F
GSM478915 F
GSM478916 F
GSM478925 F
GSM478943 F
GSM478932 F
GSM478924 F
GSM478942 F
GSM478944 F
GSM478945 F
GSM478956 F
GSM478917 D
GSM478920 IE
GSM478949 D
GSM478963 D
GSM478918 IE
GSM478919 IE
GSM478926 IE
GSM478927 IE
GSM478952 IE
GSM478960 IE
GSM478922 IE/F
GSM478923 IE/F
GSM478961 IE/F
GSM478955 F
GSM478934 IE
GSM478935 IE
GSM478954 IE
GSM478958 IE
GSM478950 IE
GSM478928
GSM478929
GSM478930
GSM478959
GSM478933 IE
GSM478936 D
GSM478938 D
GSM478940 IE/F
GSM478941 F
GSM478951 IE
4 changes: 2 additions & 2 deletions portraits/clustering.py
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@@ -1,6 +1,6 @@
import warnings

import community # louvain
import community.community_louvain as community
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
Expand Down Expand Up @@ -122,7 +122,7 @@ def clustering_profile_metrics_plot(cluster_metrics, num_clusters_ylim_max=7):
clusters_perc = pd.DataFrame([x.value_counts() for x in cluster_metrics.perc],
index=cluster_metrics.index).iloc[:, :10]

clusters_perc.plot(kind='bar', stached=True, ax=next(af), offset=.5)
clusters_perc.plot(kind='bar', stacked=True, ax=next(af)) #offset=.5 was specified in the original code

ax.set_xticks(ax.get_xticks() - .5)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
Expand Down
33 changes: 33 additions & 0 deletions portraits/detect_type.py
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import pandas as pd
from portraits.clustering import clustering_profile_metrics, clustering_profile_metrics_plot
from portraits.utils import read_gene_sets, ssgsea_formula, median_scale


def detect_type(data, threshold, scores):
ser = data.loc[threshold].perc # here threshold and ser were added to the original code
cmeans = pd.DataFrame({cg: scores.loc[samps.index].mean() for cg, samps in ser.groupby(ser)})
mapper = {}
deltas = (cmeans.loc[['Angiogenesis', 'Endothelium', 'CAF', 'Matrix', 'Matrix_remodeling']].mean() -
cmeans.loc[['MHCII', 'Antitumor_cytokines', 'Coactivation_molecules',
'B_cells', 'NK_cells', 'Checkpoint_inhibition',
'Effector_cells', 'T_cells', 'Th1_signature',
'T_cell_traffic', 'MHCI']].mean()).sort_values()

mapper[deltas.index[-1]] = 'F' # That's fibrotic
mapper[deltas.index[0]] = 'IE' # Immune enriched, non-fibrotic
cmeans.pop(deltas.index[-1])
cmeans.pop(deltas.index[0])

deltas = (cmeans.loc[['Angiogenesis', 'Endothelium', 'CAF', 'Matrix', 'Matrix_remodeling',
'Protumor_cytokines', 'Neutrophil_signature', 'Granulocyte_traffic',
'Macrophages', 'Macrophage_DC_traffic', 'MDSC_traffic', 'MDSC',
'Th2_signature', 'T_reg_traffic', 'Treg', 'M1_signatures', 'MHCII',
'Antitumor_cytokines', 'Coactivation_molecules', 'B_cells', 'NK_cells',
'Checkpoint_inhibition', 'Effector_cells', 'T_cells', 'Th1_signature',
'T_cell_traffic', 'MHCI', 'EMT_signature']].mean() -
cmeans.loc['Proliferation_rate']).sort_values()

mapper[deltas.index[-1]] = 'IE/F' # Immune enriched & fibrotic
mapper[deltas.index[0]] = 'D' # Desert
return ser.map(mapper).rename('MFP')

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