import eschr as es
import pandas as pd
import anndata
# Read in data from a csv file.
# The method expects features as columns
# Use commented out ".T" if you have features as rows
# Remove "index_col = 0" if your csv does not have row indices included.
# Also ensure that data has already been preprocessed/scaled/normalized
# as appropriate for your data type.
data_filepath = "/path/to/your/data.csv"
data = pd.read_csv(data_filepath, index_col=0) # .T
# Create AnnData object
adata = anndata.AnnData(X=data)
# Specify path for creating the zarr store that will be used for interacting with your data
zarr_loc = "/path/to/data.zarr"
# Run ESCHR consensus clustering
# This function will return the AnnData object with ESCHR hard clsuter assignments,
# soft cluster memberhsips, and uncertainty scores added.
adata = es.tl.consensus_cluster(
adata=adata,
zarr_loc=zarr_loc,
# nprocs=10 #optionally specify number of cores to use for multiprocesssing
)
# Plot soft membership matrix heatmap visualization
es.pl.smm_heatmap(adata, output_path="/where/to/save/figure.png")
# Plot umap visualization
es.pl.umap(adata, output_path="/where/to/save/figure.png")