Consensus dendrogram using scipy
I construct five different dendrograms using scipy.cluster.hierarchy library (the dendrogram and linkage specifically) and now I need to do a consensus dendrogram based in this five dendrograms but I'm not getting anywhere.
I thought of saving the result in other format also used by other packages (like the newick format used in Biopython) but I'm struggling in converting the scipy output to these formats.
Does anyone there know how to find a consensus tree/dendrogram/matrix using Python?
I'm sorry, I'm kinda noob at this, maybe there are other packages that I can't find or something.
Thanks for your help!
python scipy hierarchical-clustering dendrogram consensus
edited Nov 11 at 15:22
Johan
2,1971518
asked Nov 11 at 13:29
abcd
1
add a comment |
I construct five different dendrograms using scipy.cluster.hierarchy library (the dendrogram and linkage specifically) and now I need to do a consensus dendrogram based in this five dendrograms but I'm not getting anywhere.
I thought of saving the result in other format also used by other packages (like the newick format used in Biopython) but I'm struggling in converting the scipy output to these formats.
Does anyone there know how to find a consensus tree/dendrogram/matrix using Python?
I'm sorry, I'm kinda noob at this, maybe there are other packages that I can't find or something.
Thanks for your help!
python scipy hierarchical-clustering dendrogram consensus
edited Nov 11 at 15:22
Johan
2,1971518
asked Nov 11 at 13:29
abcd
1
1
Welcome to SO! Please, provide relevant code you tried so far.
– fewlinesofcode
Nov 11 at 14:03
# Importing the needed libraries import pandas as pd import numpy as np from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage from scipy.spatial.distance import pdist # Importing datasets (5 web foods, in this case) w1 = pd.read_table(r"C:UserscatarDesktopThesis_relatedUPGMAweb1.txt") invertw1 = pd.DataFrame(np.linalg.pinv(w1.values), w1.columns, w1.index) # And I made this 5 times (for different webs) # UPGMA construction Z1e = linkage(invertw1, 'average','euclidean') # Also times 5
– abcd
Nov 11 at 18:01
And then I made the dendrogram / plot: # Calculate full dendrogram plt.figure(figsize=(10, 10)) plt.title('Hierarchical Clustering Dendrogram - Network1', fontweight='bold', fontsize=20, y=1.03) plt.xlabel('Different analysed indexes', fontweight='bold', fontsize=15, labelpad=20) plt.ylabel('Distance', fontweight='bold', fontsize=15, labelpad=20) d1 = dendrogram( Z1e, color_threshold=0, above_threshold_color='k', labels=invertw1.index, ) plt.show()
– abcd
Nov 11 at 18:02
add a comment |
I construct five different dendrograms using scipy.cluster.hierarchy library (the dendrogram and linkage specifically) and now I need to do a consensus dendrogram based in this five dendrograms but I'm not getting anywhere.
I thought of saving the result in other format also used by other packages (like the newick format used in Biopython) but I'm struggling in converting the scipy output to these formats.
Does anyone there know how to find a consensus tree/dendrogram/matrix using Python?
I'm sorry, I'm kinda noob at this, maybe there are other packages that I can't find or something.
Thanks for your help!
python scipy hierarchical-clustering dendrogram consensus
edited Nov 11 at 15:22
Johan
2,1971518
asked Nov 11 at 13:29
abcd
1
I construct five different dendrograms using scipy.cluster.hierarchy library (the dendrogram and linkage specifically) and now I need to do a consensus dendrogram based in this five dendrograms but I'm not getting anywhere.
I thought of saving the result in other format also used by other packages (like the newick format used in Biopython) but I'm struggling in converting the scipy output to these formats.
Does anyone there know how to find a consensus tree/dendrogram/matrix using Python?
I'm sorry, I'm kinda noob at this, maybe there are other packages that I can't find or something.
Thanks for your help!
python scipy hierarchical-clustering dendrogram consensus
python scipy hierarchical-clustering dendrogram consensus
edited Nov 11 at 15:22
Johan
2,1971518
asked Nov 11 at 13:29
abcd
1
edited Nov 11 at 15:22
Johan
2,1971518
asked Nov 11 at 13:29
abcd
1
edited Nov 11 at 15:22
Johan
2,1971518
edited Nov 11 at 15:22
Johan
2,1971518
edited Nov 11 at 15:22
Johan
2,1971518
2,1971518
asked Nov 11 at 13:29
abcd
1
asked Nov 11 at 13:29
abcd
1
asked Nov 11 at 13:29
abcd
1
1
1
Welcome to SO! Please, provide relevant code you tried so far.
– fewlinesofcode
Nov 11 at 14:03
# Importing the needed libraries import pandas as pd import numpy as np from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage from scipy.spatial.distance import pdist # Importing datasets (5 web foods, in this case) w1 = pd.read_table(r"C:UserscatarDesktopThesis_relatedUPGMAweb1.txt") invertw1 = pd.DataFrame(np.linalg.pinv(w1.values), w1.columns, w1.index) # And I made this 5 times (for different webs) # UPGMA construction Z1e = linkage(invertw1, 'average','euclidean') # Also times 5
– abcd
Nov 11 at 18:01
And then I made the dendrogram / plot: # Calculate full dendrogram plt.figure(figsize=(10, 10)) plt.title('Hierarchical Clustering Dendrogram - Network1', fontweight='bold', fontsize=20, y=1.03) plt.xlabel('Different analysed indexes', fontweight='bold', fontsize=15, labelpad=20) plt.ylabel('Distance', fontweight='bold', fontsize=15, labelpad=20) d1 = dendrogram( Z1e, color_threshold=0, above_threshold_color='k', labels=invertw1.index, ) plt.show()
– abcd
Nov 11 at 18:02
add a comment |
1
Welcome to SO! Please, provide relevant code you tried so far.
– fewlinesofcode
Nov 11 at 14:03
# Importing the needed libraries import pandas as pd import numpy as np from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage from scipy.spatial.distance import pdist # Importing datasets (5 web foods, in this case) w1 = pd.read_table(r"C:UserscatarDesktopThesis_relatedUPGMAweb1.txt") invertw1 = pd.DataFrame(np.linalg.pinv(w1.values), w1.columns, w1.index) # And I made this 5 times (for different webs) # UPGMA construction Z1e = linkage(invertw1, 'average','euclidean') # Also times 5
– abcd
Nov 11 at 18:01
And then I made the dendrogram / plot: # Calculate full dendrogram plt.figure(figsize=(10, 10)) plt.title('Hierarchical Clustering Dendrogram - Network1', fontweight='bold', fontsize=20, y=1.03) plt.xlabel('Different analysed indexes', fontweight='bold', fontsize=15, labelpad=20) plt.ylabel('Distance', fontweight='bold', fontsize=15, labelpad=20) d1 = dendrogram( Z1e, color_threshold=0, above_threshold_color='k', labels=invertw1.index, ) plt.show()
– abcd
Nov 11 at 18:02
1
1
Welcome to SO! Please, provide relevant code you tried so far.
– fewlinesofcode
Nov 11 at 14:03
Welcome to SO! Please, provide relevant code you tried so far.
– fewlinesofcode
Nov 11 at 14:03
# Importing the needed libraries import pandas as pd import numpy as np from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage from scipy.spatial.distance import pdist # Importing datasets (5 web foods, in this case) w1 = pd.read_table(r"C:UserscatarDesktopThesis_relatedUPGMAweb1.txt") invertw1 = pd.DataFrame(np.linalg.pinv(w1.values), w1.columns, w1.index) # And I made this 5 times (for different webs) # UPGMA construction Z1e = linkage(invertw1, 'average','euclidean') # Also times 5
– abcd
Nov 11 at 18:01
# Importing the needed libraries import pandas as pd import numpy as np from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage from scipy.spatial.distance import pdist # Importing datasets (5 web foods, in this case) w1 = pd.read_table(r"C:UserscatarDesktopThesis_relatedUPGMAweb1.txt") invertw1 = pd.DataFrame(np.linalg.pinv(w1.values), w1.columns, w1.index) # And I made this 5 times (for different webs) # UPGMA construction Z1e = linkage(invertw1, 'average','euclidean') # Also times 5
– abcd
Nov 11 at 18:01
And then I made the dendrogram / plot: # Calculate full dendrogram plt.figure(figsize=(10, 10)) plt.title('Hierarchical Clustering Dendrogram - Network1', fontweight='bold', fontsize=20, y=1.03) plt.xlabel('Different analysed indexes', fontweight='bold', fontsize=15, labelpad=20) plt.ylabel('Distance', fontweight='bold', fontsize=15, labelpad=20) d1 = dendrogram( Z1e, color_threshold=0, above_threshold_color='k', labels=invertw1.index, ) plt.show()
– abcd
Nov 11 at 18:02
And then I made the dendrogram / plot: # Calculate full dendrogram plt.figure(figsize=(10, 10)) plt.title('Hierarchical Clustering Dendrogram - Network1', fontweight='bold', fontsize=20, y=1.03) plt.xlabel('Different analysed indexes', fontweight='bold', fontsize=15, labelpad=20) plt.ylabel('Distance', fontweight='bold', fontsize=15, labelpad=20) d1 = dendrogram( Z1e, color_threshold=0, above_threshold_color='k', labels=invertw1.index, ) plt.show()
– abcd
Nov 11 at 18:02
add a comment |
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1
Welcome to SO! Please, provide relevant code you tried so far.
– fewlinesofcode
Nov 11 at 14:03
# Importing the needed libraries import pandas as pd import numpy as np from matplotlib import pyplot as plt from scipy.cluster.hierarchy import dendrogram, linkage from scipy.spatial.distance import pdist # Importing datasets (5 web foods, in this case) w1 = pd.read_table(r"C:UserscatarDesktopThesis_relatedUPGMAweb1.txt") invertw1 = pd.DataFrame(np.linalg.pinv(w1.values), w1.columns, w1.index) # And I made this 5 times (for different webs) # UPGMA construction Z1e = linkage(invertw1, 'average','euclidean') # Also times 5
– abcd
Nov 11 at 18:01
And then I made the dendrogram / plot: # Calculate full dendrogram plt.figure(figsize=(10, 10)) plt.title('Hierarchical Clustering Dendrogram - Network1', fontweight='bold', fontsize=20, y=1.03) plt.xlabel('Different analysed indexes', fontweight='bold', fontsize=15, labelpad=20) plt.ylabel('Distance', fontweight='bold', fontsize=15, labelpad=20) d1 = dendrogram( Z1e, color_threshold=0, above_threshold_color='k', labels=invertw1.index, ) plt.show()
– abcd
Nov 11 at 18:02