_    ____    __  __ _                      _ _        
    / \  |  _ \  |  \/  (_) ___ _ __ ___   __ _| (_) __ _  
   / _ \ | | | | | |\/| | |/ __| '__/ _ \ / _` | | |/ _` | 
  / ___ \| |_| | | |  | | | (__| | | (_) | (_| | | | (_| | 
 /_/   \_\____/  |_|  |_|_|\___|_|  \___/ \__, |_|_|\__,_| 
  __  __       _ _   _                    |___/            
 |  \/  |_   _| | |_(_)       ___  _ __ ___ (_) ___ ___    
 | |\/| | | | | | __| |_____ / _ \| '_ ` _ \| |/ __/ __|   
 | |  | | |_| | | |_| |_____| (_) | | | | | | | (__\__ \   
 |_|__|_|\__,_|_|\__|_| _    \___/|_| |_| |_|_|\___|___/   
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   | | | '_ \ / _ \/ __| / __|                             
   | | | | | |  __/\__ \ \__ \                             
   |_| |_| |_|\___||___/_|___/                             
                                                                                

https://github.com/LoganLarlham/BINP52_Omic_Analysis

Overview

transcriptomic and proteomic analysis of LPS response in EFAD mice.

Environments

The environment.yml contains all dependencies for Python-based transcriptomic analysis.
The renv lockfiles include all R packages required for proteomic analysis.

Project Structure

data/
transcriptomic/
raw/
processed/
proteomic/
raw/
processed/
scripts/
transcriptomic/ # Jupyter notebook and helper Python modules
proteomic/ # R analysis script
results/
transcriptomic/
figures/ tables/
proteomic/
figures/ tables/

Usage
scripts/transcriptomic/ contains one large notebook that performs all quality control, clustering, pseudobulk differential expression, and gene set enrichment analysis of microglial single-cell RNA-seq data
scripts/proteomic/analysis.R runs the mass spectrometry–based proteomic analysis pipeline

Raw Data
Note data/*/raw/ contains large files that are not tracked due to size. Request access if needed.

Analysis Overview
Two complementary pipelines are implemented to characterize microglial responses to lipopolysaccharide priming in FAD mice with human APOE alleles
Transcriptomic Analysis (scripts/transcriptomic)
• single-cell RNA sequencing of isolated microglia
• quality filtering, normalization, and mitochondrial/ribosomal regression using Scanpy and Anndata
• dimensionality reduction (PCA, UMAP) and Leiden clustering for identification of distinct microglial states
• differential abundance testing by three-way ANOVA using statsmodels
• pseudobulk differential expression with Decoupler and PyDESeq2
• gene set enrichment analysis on Wald statistics with Decoupler and MSigDB hallmark sets
Proteomic Analysis (scripts/proteomic/analysis.R)
• data acquired by DIA–PASEF on timsTOF HT and processed in Spectronaut v19
• log2-transformed label-free quantification (LFQ) values imported into R and structured with SummarizedExperiment
• quality control, missing value imputation (MinProb) and normalization with DEP
• differential abundance analysis using limma with empirical Bayes shrinkage
• competitive gene set testing against KEGG pathways using Camera

Tools and Dependencies
Python environment (environment.yml)
• scanpy, anndata, numpy, pandas, scipy, statsmodels, decoupler, pydeseq2, matplotlib
R environment (renv)
• tidyverse (tidyr, dplyr), SummarizedExperiment, DEP, limma, camera, ggplot2

Code Structure
transcriptomic notebooks
scrnaseq_analysis.ipynb func_lib.py

proteomic R script
analysis.R

Results and Outputs
Processed matrices, figures, and tables are available under results/transcriptomic and results/proteomic directories
Generated figures include UMAP embeddings, cluster proportion plots, volcano plots, GSEA heatmaps, PCA of proteomic data, volcano and pathway enrichment plots