This repository contains the scripts used for the analysis shown in Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single cell resolution. Available here.
Folders contain the following scripts:
01_integration: integration of samples and unsupervised clustering using Seurat
- 01_explore_individual_sample: creation of Seurat objects from individual raw count matrices created with CellRanger, QC filtering and exploratory plots.
- 02a_integrate_samples_young: integration of 5 young samples, unsupervised clustering and manual annotation.
- 02b_integrate_samples_senior: integration of 3 elderly samples.
- 02c_integrate_samples_MDS: integration of 4 MDS samples.
- 03_integrate_samples_different_condition: integration of young and elderly to create a shared UMAP
- 04_proportion_test: test for differences in cell type proportion
02_glmnet_classification: scripts for the cell type classification method based on GLMnet
- 01_binary_models: build classification models for individual cell types
- 02_final_classification: assign final cell type labels by comparing the results from the binary models and choosing the one with higher scores.
03_differential_expression: differential expression analysis between cell types and conditions and subsequent GSEA
- 01_differential_expression: script to perform differential expression
- 02_GSEA_young_elderly: GSEA for differential expression between young and elderly and plot results
- 03_GSEA_young_elderly_mds: GSEA for differential expression between MDS and both young and elderly and plot results
04_trajectory_analysis: scripts to perform trajectory inference with Stream and Palantir and downstream analysis
- 01_stream: scripts for Stream
- GenerateData: prepare data to run Stream
- Stream: run Stream on young samples and project elderly samples on the resulting trajectory
- 02_palantir: scripts for Palantir
- 01_seurat_to_loom: prepare data to run Palantir
- 02_palantir_young: run Palantir on young samples
- 03_knn_final_cells: find knn cells in elderly samples to use as final states in Palantir
- 04_palantir_elderly: run Palantir on elderly samples
- 05_palantir_mds: run Palantir on elderly samples
- 06_palantir_stats: test for differences in Palantir results between young and elderly
- 07_compute_gene_trends_script: run Palantir gene trends
- 08_read_trends_and_cluster: cluster gene trends
- 09_monocyte_analysis: downstream analysis for the comparison of monocytes branch in young and elderly
- 10_erythroid_analysis: downstream analysis for the comparison of erythroid branch in young, elderly and MDS
05_GRN: gene regulatory networks analysis
- 01_GenerateData: prepare data for scenic
- 02_pyscenic: run python implementation of scenic
- 03_RSS: calculate regulon specificity score per cell type
- 04_downstream_analysis: create regulons heatmap based on scenic results and perform term over-representation analysis
- 05_CytoscapeVisualization: format scenic results for visualization in cytoscape
figures: additional scripts to reproduce paper figures.
metadata: UMAP coordinates and cell type annotation for every cell.