aeacus 0.6.0

pip install aeacus

from source:

git clone https://github.com/pandey-ps/aeacus.git
cd aeacus
pip install -e .

Usage

from aeacus import Profiler

result = Profiler(test_input="query.h5ad").load().profile()
result.obs["malignancy_call"].value_counts()

Input

Parameters

norm_type

model was trained on CPM + log1p normalized data, choose norm_type based on your input:

Examples:

# raw counts - normalize 
Profiler(test_input="raw_counts.h5ad", norm_type="cpm_log1p")

# normalized - skip
Profiler(test_input="normalized.h5ad", norm_type=False)

# TPM - log1p
Profiler(test_input="tpm_data.h5ad", norm_type="tpm_log1p")

use_raw

use_raw chooses which data slot to read from:

Example - AnnData with raw counts stored in .raw:

Profiler(
    test_input="adata.h5ad",
    use_raw=True,          # read from adata.raw.X
    norm_type="cpm_log1p", # then normalize
)

Inference

result = Profiler(test_input="data.h5ad", norm_type="cpm_log1p").load().profile()

Output

all predictions are added to result.obs:

result.obs[["malignancy_call", "malignancy_score"]].head()

Note

• missing genes are filled with zeros after aligning to geneorder.tsv; warning showed if >20% of model genes are missing.