LOY–AML

Human bulk RNA-seq, single-cell RNA-seq, CITE-seq and figure-generation code accompanying Non-concomitant Y chromosome loss shapes the genetic and immune landscape of acute myeloid leukemia and predicts clinical outcome.

Bulk RNA-seq · Single-cell analysis · Preprocessing · Data inputs

1. System requirements

• R: 4.4.1 for the recorded analysis environments; 4.4.3 for the Y-signature renderer.
• Software: workflow-specific dependencies and versions are listed in the installation guide, package inventory and environment records.
• Operating system: recorded analysis sessions used macOS 15.6 on Apple Silicon; additional session records include macOS 26.5.1. These records describe the original analysis environments.
• Hardware: a standard CPU desktop; no GPU or other specialized hardware is required by the downstream workflows.

Timing estimates below assume a desktop with 4 CPU cores, 16 GB RAM and an SSD. They are estimates, not measured benchmarks.

2. Installation

git clone https://github.com/OSU-BMBL/LOY-AML.git
cd LOY-AML

For the demo, install the following in R 4.4:

install.packages(c("BiocManager", "Seurat", "ggplot2", "dplyr", "tibble"))
BiocManager::install(version = "3.19")
BiocManager::install("DESeq2")

For study workflows, install the additional packages in the installation guide. Estimated installation time: 20–60 minutes for the demo environment; 1–2 hours for the full workflow dependencies. Source compilation and download speed affect installation time.

3. Demo

The repository includes synthetic counts for 400 genes and 120 cells from six donors in demo/data/. Run from the repository root:

Rscript demo/run_demo.R

Expected files in demo/output/:

• synthetic_pseudobulk_DE.csv — gene-level fold changes, test statistics, P values and adjusted P values.
• synthetic_volcano.pdf — a volcano plot of the synthetic results.
• sessionInfo.txt — R and package versions.

Estimated runtime: 1–5 minutes, excluding installation. See the demo guide for input and output schemas.

4. Use with study data

Arrange count matrices, metadata and reference inputs according to Data inputs. Set file locations in the workflow configuration, then select a command:

Rscript scripts/run.R --list
Rscript scripts/run.R bulk-de
Rscript scripts/run.R single-cell bach2-expression

Inputs default to data/bulk/ and data/single_cell/; outputs are written to the corresponding directories under results/. The workflow READMEs describe preprocessing order, analysis commands and figure inputs. Study data are distributed separately from the code.

Code availability and license

Source code and the synthetic demo are available in this repository and as a single ZIP archive. Project code is released under the MIT License. Third-party dependencies retain their respective licenses.