| Name | pybayesprism JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Python implementation of BayesPrism |
| upload_time | 2024-08-01 15:40:35 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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This is a Python implementation of [BayesPrism](https://github.com/Danko-Lab/BayesPrism).
Usage
```python
import os
import pandas as pd
from pybayesprism import process_input, prism, extract
os.system("curl -L -O https://github.com/ziluwang829/pyBayesPrism/raw/main/data/data.tar.gz")
os.system("mkdir -p BP_data")
os.system("tar -xzvf data.tar.gz -C BP_data")
bk_dat = pd.read_csv("BP_data/bk_dat.csv", sep=",", index_col=0)
sc_dat = pd.read_csv("BP_data/sc_dat.csv", sep=",", index_col=0)
cell_state_labels = pd.read_csv("BP_data/cell_state_labels.csv", header=None).iloc[:,0].tolist()
cell_type_labels = pd.read_csv("BP_data/cell_type_labels.csv", header=None).iloc[:,0].tolist()
sc_dat_filtered = process_input.cleanup_genes(sc_dat, "count.matrix", "hs", \
["Rb", "Mrp", "other_Rb", "chrM", "MALAT1", "chrX", "chrY"], 5)
sc_dat_filtered_pc = process_input.select_gene_type(sc_dat_filtered, ["protein_coding"])
my_prism = prism.Prism.new(reference = sc_dat_filtered_pc,
mixture = bk_dat, input_type = "count.matrix",
cell_type_labels = cell_type_labels,
cell_state_labels = cell_state_labels,
key = "tumor",
outlier_cut = 0.01,
outlier_fraction = 0.1)
bp_res = my_prism.run(n_cores = 36, update_gibbs = True)
theta = extract.get_fraction(bp_res, "final", "type")
```
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