>>> import pandas as pd
>>> import scipy.io as sio
>>> import numpy as np

>>> mat = sio.mmread('mat.mtx') #Is a centered and scaled matrix (scaling input is optional)
>>> mat.shape
(3850, 1999)

>>> meta = pd.read_csv('metadata.csv')
>>> meta.head()
 Unnamed: 0          sample_name  smartseq_cluster_id  smartseq_cluster  ... n_genes percent_mito pass_count_filter  pass_mito_filter
0  SM-GE4R2_S062_E1-50  SM-GE4R2_S062_E1-50                   46   Nr5a1_9|11 Rorb  ...    9772          0.0              True              True
1  SM-GE4SI_S356_E1-50  SM-GE4SI_S356_E1-50                   46   Nr5a1_9|11 Rorb  ...    8253          0.0              True              True
2  SM-GE4SI_S172_E1-50  SM-GE4SI_S172_E1-50                   46   Nr5a1_9|11 Rorb  ...    9394          0.0              True              True
3   LS-15034_S07_E1-50   LS-15034_S07_E1-50                   42  Nr5a1_4|7 Glipr1  ...   10643          0.0              True              True
4   LS-15034_S28_E1-50   LS-15034_S28_E1-50                   42  Nr5a1_4|7 Glipr1  ...   10550          0.0              True              True

>>> cellTypes = list(meta.smartseq_cluster)
>>> sexLabels = list(meta.sex_label)
>>> len(sexLabels)
3850

>>> from MCML.modules import MCML, bMCML

>>> mcml = MCML(n_latent = 50, epochs = 100) #Initialize MCML class

>>> latentMCML = mcml.fit(mat, np.array([cellTypes,sexLabels]) , fracNCA = 0.8 , silent = True) #Run MCML
>>> latentMCML.shape
(3850, 50)

>>> mcml.plotLosses(figsize=(10,3),axisFontSize=10,tickFontSize=8) #Plot loss over epochs

>>> bmcml = bMCML(n_latent = 50, epochs = 100) #Initialize bMCML class


>>> latentbMCML = bmcml.fit(mat, np.array(cellTypes), np.array(sexLabels), silent=True) #Run bMCML
>>> latentbMCML.shape
(3850, 50)

>>> bmcml.plotLosses(figsize=(10,3),axisFontSize=10,tickFontSize=8) #Plot loss over epochs

>>> from MCML import tools as tl

#Pairwise distances between centroids of cells in each label
>>> cDists = tl.getCentroidDists(mat, np.array(cellTypes)) 
>>> len(cDists)
784

#Avg pairwise distances between cells of *both* sexes, for each cell type
>>> interDists = tl.getInterVar(mat, np.array(cellTypes), np.array(sexLabels))  
>>> len(interDists)
27

#Avg pairwise distances between cells of the *same* sex, for each cell type
>>> intraDists = tl.getIntraVar(mat, np.array(cellTypes), np.array(sexLabels)) 
>>> len(intraDists)
53

#Fraction of neighbors for each cell with same label as cell itself (also returns which labels neighbors have)
>>> neighbor_fracs, which_labels = tl.frac_unique_neighbors(mat, np.array(cellTypes), metric = 1,neighbors = 30)

#Get nearest neighbors for any embedding
>>> orig_neigh = tl.getNeighbors(mat, n_neigh = 15, p=1)
>>> latent_neigh = tl.getNeighbors(latentMCML, n_neigh = 15, p=1)

#Get Jaccard distance between latent and ambient nearest neighbors
>>> jac_dists = tl.getJaccard(orig_neigh, latent_neigh)
>>>len(jac_dists)
3850

>>> help(MCML)
>>> help(bMCML)
>>> help(tl)