essHIC.dist

essHIC.dist(filename,metafile=None)

The dist class provides methods to analyze distance matrices. You can apply clustering and dimensional reduction techniques to the distance matrix, as well as assay the quality of the classifier by comparing distances between HiC experiments to their known labeling.

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Parameters:

filename: string

path of the file containing the distance matrix.

metfile: string, default=None

path of the metadata file containing cell type information and a list of the outliers to remove

Attributes:

metafile: string

the metadata file containing the cell types of the HiC matrices and a list of outliers.

pseudo: numpy ndarray

which experiments are pseudo-replicates according to the metadata file.

col2lab: dictionary

dictionary which turns integer "color" numbers into the corresponding cell type label.

lab2col: dictionary

dictionary which turns cell type labels into the corresponding integer "color" number.

colors: numpy ndarray

list of the integer "color" numbers of the experiments, which encode their cell type.

mask: numpy ndarray

which experiments are being removed from the distance matrix according to the metadata file.

dist: numpy masked ndarray

masked array which contains the distances between all couples of experiments in the dataset. It masks the outliers according to the metadata file.

mdist: numpy ndarray

array which contains distances between all couples of experiments except the outliers given by the metadata file.

mcol: numpy ndarray

list of the integer "color" numbers of the experiments, except the outliers.

mpsd: numpy ndarray

list of the pseudo-replicate experiments after removing outliers.

dlist: numpy ndarray

list of the distances at which the ROC curve has been computed.

roc: numpy ndarray

ROC curve values.

sim_map: numpy ndarray

affinity map computed from the distance matrix.

MDSrep: numpy ndarray

n-dimensional positions of the experiments according to multidimensional scaling embedding.

clusters: numpy ndarray

list of the clusters labels.

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Methods

method	function
print_dist	prints the distance to a file.
order	orders experiments according to their cell type.
get_cmap	builds a matrix which is one when two experiments have the same cell-type and zero otherwise.
get_roc_area	returns the area under the ROC curve.
get_roc	computes the ROC curve.
get_gauss_sim	computes a affity matrix from the distances using a gaussian kernel.
MDS	computes the multidimensional scaling embedding of the distance matrix.
spec_clustering	computes clusters using spectral clustering.
hier_clustering	computes hierarchical clustering.
get_dunn_score	computes dunn score.
get_quality_score	computes the quality score.
get_purity_score	computes the purity score.
plot	plots the distance matrix.
plot_masked	plots the masked distance matrix.
plot_squares	plots colored squares over the distance matrix to point out experiments with the same cell-type.
plot_similarity	plots the affinity matrix.
plot_roc	plots ROC curve.
show_hist	plots and dispays a histogram of the distribution of the distances. Same cell type, different cell type, and pseudoreplicate experiments are colored differently.
show_MDS	plots and displays the MDS embedding of the matrix.
show_clusters	plots and displays a cartoon of the clusters.
show_dendrogram	plots and displays a dendrogram of the clusters.