This repository houses Jupyter Notebooks that are used for reproducible analysis of T cells treated with bortezomib, lenalidomide, or dexamethasone and profiled using the trimodal single-cell assay TEA-seq.
These notebooks can be used within the Human Immune System Explorer (HISE) framework for traceable, reproducible analysis that results in a Certificate of Reproducibility.
Lauren Okada
Lucas T. Graybuck
This code is was written to perform analyses and generate figures related to the following manuscript:
Title: Deconvolution of the mechanisms of T cell drug response in multiple myeloma induction therapy
Authors: Lucas T. Graybuck, Lauren Okada, Wei-Ling Chang, Jessica Garber, Catalina Sakai, Morgan Weiss, Samir Rachid Zaim, Veronica Hernandez, Ziyuan He, Upaasana Krishnan, Priya Ravisankar, Julian Reading, Tao Peng, Ernie Coffey, Damian J. Green, Philip D. Greenberg, Thomas F. Bumol, Jimena Garcia, Mackenzie Kopp, Gregory L. Szeto, Xiao-Jun Li, Evan Newell, Peter J. Skene, Troy R. Torgerson.
Due to the use of submodules, be sure to use the --recurse-submodules flag when cloning this repository to obtain all files:
git clone --recurse-submodules https://github.com/aifimmunology/repro-vrd-tea-seq
Files that are used across multiple analyses are stored in common/.
Notebooks related to QC and cell type-based cell filtering are stored in 00-cell-filtering/.
Notebooks related to T cell type labeling are stored in 01-cell-type-labeling/.
Notebooks related to the use of the MAST framework to identify differentially expressed genes (DEGs) and intersections between MAST results from different conditions are stored in 02-mast-deg-testing/.
Notebooks related to Gene Set Enrichment Analysis are stored in 03-gsea-analysis/.
Notebooks related to analysis of scATAC-seq data and enrichment of TF motifs near DEGs are stored in 04-atac-analysis/.
Notebooks related to differential detection of cell surface antibodies (Antibody-derived tags, ADTs) using linear models are stored in 05-adt-lm-testing/.
Data and Notebooks related to flow cytometry are stored in 06-flow-cytometry/. This section is partially reproducible, as much of the analysis was carried out by manual gating in FlowJo. Summary statistics and outputs from FlowJo analysis are stored in 06-flow-cytometry/data/, which are used to generate secondary analyses and figures.
Notebooks for assembly of results into panels for use in figures are stored in the figures/ directory. Panels and the data backing those panels are in figures/output/figure_N or figures/output/supp_figure_N where N is the figure number.
Notebooks for assembly of results into supplementary tables are stored in the tables/ directory. Raw versions of supplementary tables are compressed using gzip and stored in tables/output/.
An interactive app for exploration of DEG results was implemented in the Dash framework for Python. Code used to generate this app is linked as a submodule to this repository in repro-vrd-tea-seq-deg-app/.
The notebooks in this repository are designed to work within the HISE system, where raw data are stored in a central repository and are accessed using the HISE SDK. However, the input data are also available for use outside of HISE on dbGaP and GEO.
Raw, FASTQ-level data from our experiments are deposited in the database of Genotypes and Phenotypes (dbGaP) at accession number phs003430.v1.p1.
Processed data, including scRNA-seq gene counts, ADT feature counts, and scATAC-seq fragment counts, are available in the Gene Expression Omnibus at accession number GSE236422.
The license for this repository is available on Github in the file LICENSE in this repository
We are not currently supporting this code, but simply releasing it to the community AS IS but are not able to provide any guarantees of support. The community is welcome to submit issues, but you should not expect an active response.