snRNASeq_Brain_Tumor_Data_Analysis

Analysis of single nucleus-RNAseq Brain Dataset - Stefan Pfister

The analysis pipeline

The analysis pipeline is explained below. The scripts are located under bin folder. After running each script, the relevant plots are created under plots folder. The out_data folder (the AnnData objects that you asked) mentioned below is available in the following link:

https://drive.google.com/drive/folders/1fKj4oA9kTr7WQDy0lTDm6KhA5pKx0Dd7?usp=sharing

The out_data folder should be located under data folder to reproduce the results. The order of executions and brief explanations about the scripts are available below:

• qc_preprocess applies preprocessing and filtering steps on each sample individually. Before running the script, the raw data (count matrices) should be located under data folder as data/b06x-g/G703/eblanco/projects/Aniello_ITCC-P4/results/count_matrices_to_share/snRNAseq/6.1.0. This scripts saves the resulting AnnData objects under data/out_data folder (e.g. data/out_data/sample01_B062_007+pdx01-x_human_filtered.h5ad). The script first seperates PDX samples into two as human and mouse samples and applies QC and filtering on the samples. The script can be run as follows:

    python qc_preprocess.py

• merge.py merges the samples coming from mice (PDX+control) and human samples(PDX-tumor + tumor). It takes three arguments : (i) -i or --input_dir represents input directory containing the preprocessed AnnData object , (ii) -o or --output_dir argument representes the directory for the merged AnnData objects and (iii) -st or --sample_type argument represents the samples to be merged (i.e., human or mouse). Once the script is run the human_merged.h5ad or mouse_merged.h5ad files are created under the specified output folder (in this case ../data/out_data). This script can be run as follows:

    # mouse_merged.h5ad created
    python merge.py -i ../data/out_data -o ../data/out_data  -st mouse 
    # human_merged.h5ad created
    python merge.py -i ../data/out_data -o ../data/out_data  -st human # for human

• integrate.py works similar to the merge.py. The arguments are the same except that -i or --input_dir argument takes the path of the merged sample of interest. human_integrated.h5ad or mouse_integrated.h5ad files are created under the specified output folder (in this case ../data/out_data). This script can be run as follows:

    python integrate.py -i ../data/out_data/mouse_merged.h5ad -o ../data/out_data -st mouse
    python integrate.py -i ../data/out_data/human_merged.h5ad -o ../data/out_data -st human

• For cluster_annotate.py, the arguments are the same except that -i or --input_dir argument takes the path of the integrated sample file of interest. human_integrated.h5ad or mouse_integrated.h5ad files are updated/created under the specified output folder (in this case ../data/out_data). It performs clustering based on different resolution parameters and perform cell type annotation using the marker genes. The DEG for each cluster are also computed and plotted. The csv files are also created under data/out_data folder for each clustering run (e.g.human_deg_leiden_res_0.1.csv). Here, the group column represents the cluster number. The cluster keys are labeled as leiden_<resolution> in AnnData objects. This script can be run as follows:

    python cluster_annotate.py -i ../data/out_data/mouse_integrated.h5ad -o ../data/out_data -st mouse
    python cluster_annotate.py -i ../data/out_data/human_integrated.h5ad -o ../data/out_data -st human

• downstream_analysis.py has the same parameters as above. This scripts runs PROGENy for pathway activity estimation.human_integrated_progeny_act.h5ad or mouse_integrated_progeny_act.h5ad files are created under the specified output folder (in this case ../data/out_data). In these AnnData objects, columns represent 14 different pathways instead of genes. The relevant plots are available under plots/downstream folder. This script can be run as follows:

    python downstream_analysis.py -i ../data/out_data/mouse_integrated.h5ad -o ../data/out_data -st mouse
    python downstream_analysis.py -i ../data/out_data/human_integrated.h5ad -o ../data/out_data -st human