AML Data Processing Notebook

This notebook processes the AML dataset obtained from GEO (GSE116256) and saves it as a single count matrix.

Data Sources

• The count matrix and annotations were downloaded from GEO (GSE116256).
• The compressed files are stored in:
  /scMEDAL_for_scRNAseq/Experiments/data/AML_data/zip_files
• The processed count matrix is saved in:
  /scMEDAL_for_scRNAseq/Experiments/data/AML_data/adata_merged

Environment: preprocess_and_plot_umaps_env

import sys
# Add the parent directory to the Python path
sys.path.append("../")
import os
# Now you can import from the parent directory
from paths_config import data_base_path

from scMEDAL.utils.preprocessing_utils import AML_data_reader
from scMEDAL.utils.utils import save_adata

data_base_path: /endosome/archive/bioinformatics/DLLab/src/AixaAndrade/gitfront/scMEDAL_for_scRNAseq/Experiments/AML/../data/AML_data
outputs_path: /endosome/archive/bioinformatics/DLLab/src/AixaAndrade/gitfront/scMEDAL_for_scRNAseq/Experiments/AML/../outputs/AML_outputs

# I downloaded the dataset count matrix and annotations from: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116256
# I saved the compressed files under /scMEDAL_for_scRNAseq/Experiments/data/AML_data/zip_files
# Path to the directory containing zip files
parent_path = os.path.join(data_base_path, "zip_files")
# 1.Read adata


AML_reader = AML_data_reader(parent_path)
# Get df_paths
df_paths = AML_reader.get_df_paths()
df_paths

         id Patient_group  counts
0   AML1012           AML       1
1   AML210A           AML       1
2    AML314           AML       2
3    AML328           AML       4
4    AML329           AML       3
5    AML371           AML       2
6   AML419A           AML       1
7   AML420B           AML       3
8    AML475           AML       2
9    AML556           AML       3
10  AML707B           AML       5
11  AML722B           AML       2
12   AML870           AML       2
13   AML916           AML       1
14  AML921A           AML       1
15   AML997           AML       2
16      BM1       control       1
17      BM2       control       1
18      BM3       control       1
19      BM4       control       1
20      BM5       control       2
21    MUTZ3      cellline       1
22      OCI      cellline       1

# Create a dict of adata objects
adata_dict = AML_reader.create_adata_dict(df_paths)
print(f"Created {len(adata_dict)} AnnData objects.")

/archive/bioinformatics/DLLab/shared/CondaEnvironments/Aixa_scDML/lib/python3.8/site-packages/anndata/_core/anndata.py:120: ImplicitModificationWarning: Transforming to str index.
  warnings.warn("Transforming to str index.", ImplicitModificationWarning)


Created 43 AnnData objects.

merged_adata = AML_reader.merge_adata_objects(adata_dict)

merged_adata

AnnData object with n_obs × n_vars = 41090 × 27899
    obs: 'Cell', 'NumberOfReads', 'AlignedToGenome', 'AlignedToTranscriptome', 'TranscriptomeUMIs', 'NumberOfGenes', 'CyclingScore', 'CyclingBinary', 'MutTranscripts', 'WtTranscripts', 'PredictionRF2', 'PredictionRefined', 'CellType', 'Score_HSC', 'Score_Prog', 'Score_GMP', 'Score_ProMono', 'Score_Mono', 'Score_cDC', 'Score_pDC', 'Score_earlyEry', 'Score_lateEry', 'Score_ProB', 'Score_B', 'Score_Plasma', 'Score_T', 'Score_CTL', 'Score_NK', 'NanoporeTranscripts', 'id', 'Day', 'unique_id', 'Patient_group'
    var: 'Gene'

merged_adata.obs

# Change to True to save adata
# I saved the cmerged count matrix under /scMEDAL_for_scRNAseq/Experiments/data/AML_data/adata_merged
save_data = False
if save_data:
    #save merged adata
    save_adata(merged_adata,output_path=data_base_path+"/adata_merged")

Created folder: /archive/bioinformatics/DLLab/AixaAndrade/data/Genomic_data/VanGallen_2019/adata_merged