Time: Spring 2024 Members: Jia Zhao; Adriana Payan-Medina
The repo contains the code and data to reproduce all figures and results for the group project of 20.440 Analysis of Biological Network (MIT)
Resident tissue macrophages (RTMs) are present ubiquitously in every tissue and organ, yet how they contribute to cell-cell communication is largely unknown. As intercellular communication is critical for tissue homeostasis, our goal is to use computational methods to unveil cell-cell communication between fat RTMs and adipocytes/adipocyte progenitors to decode complex cellular circuits and infer novel functions of RTMs in adipogenesis. The use of scRNA-seq analysis was also conducted to validate and investigate adipose RTM function.
NicheNet: a computational algorithm to model intercellular communication
install.packages("devtools")
devtools::install_github("saeyslab/nichenetr")
Git repo of NicheNet: https://github.com/saeyslab/nichenetr/tree/master
Browaeys, R.; Saelens, W.; Saeys, Y. NicheNet: Modeling Intercellular Communication by Linking Ligands to Target Genes. Nat. Methods 2020, 17 (2), 159–162. https://doi.org/10.1038/s41592-019-0667-5.
DESeq2: find differentially expressed genes and plot
Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)
Scanpy: cluster and visualize cell types using marker genes, identify differentially expressed genes
Wolf, F., Angerer, P. & Theis, F. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018). https://doi.org/10.1186/s13059-017-1382-0
scikit-learn: clustering cell types and subtypes
Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
RNA-seq data of RTMs and other resident cells: Silva_WAT_cMAF_vs_WT.csv
Moura Silva Hernandez; Kitoko Jamil Zola; Queiroz Camila Pereira; Kroehling Lina; Matheis Fanny; Yang Katharine Lu; Reis Bernardo S.; Ren-Fielding Christine; Littman Dan R.; Bozza Marcelo Torres; Mucida Daniel; Lafaille Juan J. C-MAF–Dependent Perivascular Macrophages Regulate Diet-Induced Metabolic Syndrome. Sci. Immunol. 2021, 6 (64), eabg7506. https://doi.org/10.1126/sciimmunol.abg7506.
A Single-Cell Atlas of Human and Mouse White Adipose Tissue
Emont, M. P.; Jacobs, C.; Essene, A. L.; Pant, D.; Tenen, D.; Colleluori, G.; Di Vincenzo, A.; Jørgensen, A. M.; Dashti, H.; Stefek, A.; McGonagle, E.; Strobel, S.; Laber, S.; Agrawal, S.; Westcott, G. P.; Kar, A.; Veregge, M. L.; Gulko, A.; Srinivasan, H.; Kramer, Z.; De Filippis, E.; Merkel, E.; Ducie, J.; Boyd, C. G.; Gourash, W.; Courcoulas, A.; Lin, S. J.; Lee, B. T.; Morris, D.; Tobias, A.; Khera, A. V.; Claussnitzer, M.; Pers, T. H.; Giordano, A.; Ashenberg, O.; Regev, A.; Tsai, L. T.; Rosen, E. D. A Single-Cell Atlas of Human and Mouse White Adipose Tissue. Nature 2022, 603 (7903), 926–933. https://doi.org/10.1038/s41586-022-04518-2.
We have folders and subfolders in this repo:
- data/: the raw RNAseq data of mouse white adipose tissue RTMs and other resident cells
- scRNA_HWAT/: subfolder with human WAT scRNA-seq UMAP results, metadata, features, and barcodes. The Matrix file referenced in 'A Single-Cell Atlas of Human and Mouse White Adipose Tissue' was locally stored due to its considerable size and the uploading constraints of GitHub. However, it is openly accessible via the provided link.
- code/: source files for producing the results and figures
- figures/: the final figures included in the submission
- presentation_slides/: Final presentation slides.
R version 4.3.3
RStudio version: 2023.12.1.402
Packages: Relevant codes are in code/NicheNet_VAMs.Rmd
install.packages("devtools")
devtools::install_github("saeyslab/nichenetr")
install.packages("tidyverse")
install.packages("DESeq2")
install.packages("EnhancedVolcano")
Python version 3.11.5
Jupyter Notebook version 7.1.3
Packages: Relevant codes are in code/scRNA_cluster_DGE.ipynb
import pandas
import sklearn.cluster
import scanpy
import matplotlib.pyplot
import numpy
import os