Created
August 15, 2019 07:07
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# Works on very large datasets. | |
import pandas as pd | |
try: | |
import mkl | |
mkl.set_num_threads(1) | |
except: | |
pass | |
f = "/mnt/work/endrebak/epigenome_roadmap_analyses/H3K27me3/data/hg38/matrix/tfv_20000.txt" | |
df = pd.read_csv(f, sep="\t", index_col=list(range(0, 7)), nrows=None) | |
def column_order(df, metric="euclidean", method="single"): | |
from scipy.spatial.distance import pdist #, squareform | |
from scipy.cluster.hierarchy import dendrogram, linkage, leaves_list | |
_distance_matrix = pdist(df.T, metric=metric) | |
# distance_matrix = squareform(_distance_matrix) | |
linkage_matrix = linkage(_distance_matrix, method=method, metric=metric) | |
sort_order = leaves_list(linkage_matrix) | |
columns_in_sort_order = df.columns[sort_order] | |
return columns_in_sort_order | |
def row_labels(df, k): | |
from sklearn.cluster import MiniBatchKMeans | |
kmeans = MiniBatchKMeans(k) | |
kmeans.fit(df) | |
return kmeans.labels_ | |
def labeled_matrix(df, k, metric="euclidean", method="single"): | |
columns = column_order(df, metric, method) | |
labels = row_labels(df, k) | |
df = df[columns] | |
df.insert(0, "Label", labels) | |
df = df.set_index("Label", append=True) | |
return df | |
df2 = labeled_matrix(df, k=5, metric="euclidean", method="single") |
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