IMPRINT & Born immune data analyses

Bifidobacteria-mediated immune system imprinting early in life (Henrick et al.)

Table of contents

• General info
• Dependencies
• Repo description
  • Figure 1
  • Figure 2
  • Figure 3
  • Figure 4
  • Figure 5
  • Figure 6

General info

This project used multiple omics data:

• Plasma protein expression (Olink - NPX values)
• Cell abundance (CyTOF - Grid cell abundance)
• Metagenome data (Shotgun metagenomic sequencing - CPM)
• Targeted single-cell RNA-seq (BD Rhapsody - MolsPerCell)

Dependencies

Project is created with:

• RStudio version: 4.0.2
• Python version: 3.8

Repo description

Figure 2

Metagenomics

• 2A: PCoA from bray-curtis distance matrix at family level created in R using ... Script available in figure2/2A.R.
• 2B: areaplot of family level relative abundances created in python using pandas, numpy, matplotlib and seaborn. Guide available in figure2/2B.html.
• 2C: ...plot of Bifidobacterium species relative abundance trajectory per individual. Created in R using ... Script available in figure2/2C.R.

Figure 5

Cytokines and Metagenomics

• 5C: Radar plot of 0-1 normalized cytokine medians created in R using tidyverse, ggsignif, ggiraphExtra, ggpubr and forcats. Script available in Cytokines_and_Metagenomics.R.
• 5D: Heatmap of cytokine and bacterial family relative abundances created in R using tidyverse, RColorBrewer, ggsignif, ggpubr, psych and forcats. Guide available in Cytokines_and_Metagenomics.R.

Figure 6

Targeted BD Rhapsody data

• 6B & 6D: UMAP of each cytokine condition and volcano plot displaying differentially expresed mRNA as well as processing of output files found in figure6/BD_CD4Tpolarization_exp1.R
• 6F: Dot plot of different conditions for all cytokine cultures as well as processing of output files found in figure6/BD_CD4Tpolarization_exp2.R