Process scRNA-seq Data with msmu¶

In [1]:

from pathlib import Path
import pandas as pd
import requests
import scanpy as sc
import tarfile

from pathlib import Path import pandas as pd import requests import scanpy as sc import tarfile

In [2]:

url = "https://github.com/bertis-informatics/msmu/releases/download/0.2.2/GSE201575.tar.gz"
meta = "https://github.com/bertis-informatics/msmu/releases/download/0.2.2/meta.csv"
base_dir = Path(url).name.split(".")[0]

r = requests.get(url)
r.raise_for_status()

with open(Path(url).name, "wb") as f:
    f.write(r.content)

with tarfile.open(Path(url).name, "r:gz") as tar:
    members = [m for m in tar.getmembers() if not Path(m.name).name.startswith("._")]
    tar.extractall(members=members)

url = "https://github.com/bertis-informatics/msmu/releases/download/0.2.2/GSE201575.tar.gz" meta = "https://github.com/bertis-informatics/msmu/releases/download/0.2.2/meta.csv" base_dir = Path(url).name.split(".")[0] r = requests.get(url) r.raise_for_status() with open(Path(url).name, "wb") as f: f.write(r.content) with tarfile.open(Path(url).name, "r:gz") as tar: members = [m for m in tar.getmembers() if not Path(m.name).name.startswith("._")] tar.extractall(members=members)

/var/folders/pp/7ts5fh4x5hl81rnn895l34ph0000gn/T/ipykernel_17737/3812210948.py:13: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.
  tar.extractall(members=members)

Read count matrix¶

In [3]:

path_list = Path(base_dir).glob("*.txt.gz")
path_list = sorted(path_list)
adata_list = []

for p in path_list:
    a = sc.read_text(p, delimiter="\t", first_column_names=True).T
    a.obs.index = [p.stem.split(".")[0].split("_")[1]]
    a.obs["filename"] = [p.stem]
    adata_list.append(a)

adata = sc.concat(adata_list)

path_list = Path(base_dir).glob("*.txt.gz") path_list = sorted(path_list) adata_list = [] for p in path_list: a = sc.read_text(p, delimiter="\t", first_column_names=True).T a.obs.index = [p.stem.split(".")[0].split("_")[1]] a.obs["filename"] = [p.stem] adata_list.append(a) adata = sc.concat(adata_list)

Add metadata and filter samples on use¶

In [4]:

meta_df = pd.read_csv(meta)
meta_df = meta_df.dropna()
meta_df.index = meta_df["sample_rna"].values

adata.obs = adata.obs.merge(meta_df, left_index=True, right_index=True, how="left")

adata = adata[adata.obs.dropna().index.to_list()].copy()
adata.obs.index = adata.obs["sample_id"].values

adata

meta_df = pd.read_csv(meta) meta_df = meta_df.dropna() meta_df.index = meta_df["sample_rna"].values adata.obs = adata.obs.merge(meta_df, left_index=True, right_index=True, how="left") adata = adata[adata.obs.dropna().index.to_list()].copy() adata.obs.index = adata.obs["sample_id"].values adata

Out[4]:

AnnData object with n_obs × n_vars = 70 × 40207
    obs: 'filename', 'set', 'sample_id', 'sample', 'cell', 'condition', 'sample_rna'

Filtering and normalization¶

In [5]:

adata.layers["counts"] = adata.X.copy()

sc.pp.filter_genes(adata, min_cells=3, inplace=True)
sc.pp.filter_cells(adata, min_genes=200, inplace=True)

sc.pp.normalize_total(adata)
sc.pp.log1p(adata)

adata

adata.layers["counts"] = adata.X.copy() sc.pp.filter_genes(adata, min_cells=3, inplace=True) sc.pp.filter_cells(adata, min_genes=200, inplace=True) sc.pp.normalize_total(adata) sc.pp.log1p(adata) adata

Out[5]:

AnnData object with n_obs × n_vars = 70 × 13451
    obs: 'filename', 'set', 'sample_id', 'sample', 'cell', 'condition', 'sample_rna', 'n_genes'
    var: 'n_cells'
    uns: 'log1p'
    layers: 'counts'

Save AnnData object¶

In [6]:

adata.write_h5ad("GSE201575.h5ad")

adata.write_h5ad("GSE201575.h5ad")