def setUp(self):
# Get Haber count data
data_raw = pd.read_csv("./data/haber_counts.csv")
salm_indices = [0, 1, 2, 3, 8, 9]
salm_df = data_raw.iloc[salm_indices, :]
data_salm = dat.from_pandas(salm_df, covariate_columns=["Mouse"])
data_salm.obs["Condition"] = data_salm.obs["Mouse"].str.replace(r"_[0-9]", "")
self.data = data_salm
def test_from_pandas(self):
# Get Haber Salmonella data
data_raw = pd.read_csv("./data/haber_counts.csv")
salm_indices = [0, 1, 2, 3, 8, 9]
salm_df = data_raw.iloc[salm_indices, :]
data_salm = dat.from_pandas(salm_df, covariate_columns=["Mouse"])
data_salm.obs["Condition"] = data_salm.obs["Mouse"].str.replace(r"_[0-9]", "")
# Only check size of x, obs
x_shape = (data_salm.X.shape == (6, 8))
obs_shape = (data_salm.obs.shape == (6, 2))
self.assertTrue(x_shape & obs_shape)
#%% Haber data on multiple categories
cell_counts = pd.read_csv(
"C:\\Users\\Johannes\\Documents\\Uni\\Master's_Thesis\\compositionalDiff-johannes_tests_2\\data\\haber_counts.csv"
)
print(cell_counts)
# Convert data to anndata object
# Filter out salmonella data
salm_indices = [0, 1, 2, 3, 8, 9]
salm_df = cell_counts.iloc[salm_indices, :]
data_salm = dat.from_pandas(salm_df, covariate_columns=["Mouse"])
# Extract condition from mouse name and add it as an extra column to the covariates
data_salm.obs["Condition"] = data_salm.obs["Mouse"].str.replace(r"_[0-9]", "")
print(data_salm.X)
print(data_salm.obs)
#%%
salm_df.index = pd.Series([0, 1, 2, 3, 4, 5])
print(salm_df.index)
data_salm_2 = dat.from_pandas(salm_df, covariate_columns=["Mouse"])
data_salm_2.obs["Condition"] = data_salm_2.obs["Mouse"].str.replace(
r"_[0-9]", "")
#%%
# read metadata
with open(data_path + "/sample-metadata.tsv", "rb") as f:
metadata = pd.read_csv(f, sep="\t", index_col="sample-id").iloc[1:, :]
metadata_columns = [
"subject", "reported-antibiotic-usage", "days-since-experiment-start",
"body-site"
]
# add subject to count data
biom_data = pd.merge(biom_data,
metadata[metadata_columns],
left_index=True,
right_index=True)
data = dat.from_pandas(biom_data, metadata_columns)
data.obs = data.obs.rename(
columns={
"reported-antibiotic-usage": "antibiotic",
"body-site": "site",
"days-since-experiment-start": "days_since_start"
})
print(data.obs)
#%%
importlib.reload(viz)
sns.set(style="ticks", font_scale=2)
args_swarmplot = {"hue": "subject", "size": 10, "palette": "Reds"}
viz.boxplot_facets(data, feature="site")
# read metadata
with open(data_path + "/sample-metadata.tsv", "rb") as f:
metadata = pd.read_csv(f, sep="\t", index_col="sample-id").iloc[1:, :]
metadata_columns = [
"subject", "reported-antibiotic-usage", "days-since-experiment-start",
"body-site"
]
# add subject to count data
biom_data = pd.merge(biom_data,
metadata[metadata_columns],
left_index=True,
right_index=True)
data = dat.from_pandas(biom_data, metadata_columns)
data.obs = data.obs.rename(
columns={
"reported-antibiotic-usage": "antibiotic",
"body-site": "site",
"days-since-experiment-start": "days_since_start"
})
print(data.obs)
#%%
def plot_one_stackbar(y, type_names, title, level_names):
plt.figure(figsize=(20, 10))
cell_counts = celltypes.loc[:, ["sample_id", "cell_type", "counts"]].\
set_index(["sample_id", "cell_type"]).unstack(fill_value=0).fillna(0).reset_index()
print(cell_counts)
#%%
cell_counts_2 = cell_counts
cell_counts_2.columns = cell_counts_2.columns.droplevel(0)
cell_counts_2 = cell_counts_2.rename(columns={"": "sample_id"})
cell_counts_2["Condition"] = cell_counts_2["sample_id"].str.replace(
r"[0-9]", "")
print(cell_counts_2)
#%%
importlib.reload(dat)
data = dat.from_pandas(cell_counts_2, ["sample_id", "Condition"])
print(data.X)
print(data.obs)
print(data.var)
#%%
cells = cell_counts.iloc[:, 1:].to_numpy().astype("int")
print(cells)
obs = pd.DataFrame(cell_counts["sample_id"])
obs["Condition"] = obs["sample_id"].str.replace(r"[0-9]", "")
print(obs)
var = pd.DataFrame(index=cell_counts.iloc[:, 1:].columns.droplevel(0))
print(var)
# Remove otus with low counts (<100 total) --> Leaves 250 OTUs
# Leaving in low expression OTUs leads to nonconvergence for shorter chains (1000 samples),
# longer chains cant be done on my computer
counts_bal = data_bal.iloc[:, :-33]
counts_bal = counts_bal.loc[:, np.sum(counts_bal, axis=0) >= 100]
data_bal_expr = pd.merge(counts_bal,
data_bal.loc[:, col],
right_index=True,
left_index=True)
print(data_bal_expr)
data_scdcdm = dat.from_pandas(data_bal_expr, col)
print(data_scdcdm.X.shape)
# Free up some memory
del ([counts_bal, data, data_bal, metadata, meta_rel, file, otus, split])
#%%
# Experimental model configuration
class NoBaselineModelExperimental(dm.CompositionalModel):
""""
implements statistical model for compositional differential change analysis without specification of a baseline cell type