Figure5_MultilevelCorrelation

MultilevelCorrelations_SampleMatch.R

Purpose:

The script contains the code to run a multi-level partial correlation to investigate the effect of age and stain load on gene expression. Only 5XFAD samples with paired IHC and RNAseq data were selected (n=34); therefore, all animals in this analysis had one hemisphere fixed for IHC and the contralateral hippocampus dissected for bulk RNAseq.

Input files:

This script reads in the output of the default R/DESeq2 pipeline ‘DESeq_ModelOutput_Interaction_BySample.rds’. This file contains the normalized gene expression count data, the variance stabilizing transformed count data, and the sample metadata.

Output files:

Multi-level correlation output per stain is saved. These R objects have the correlation data formatted in two different formats (long and wide format).

• 'Hippo_AB1.42 _correlations_summary_results_SampleMatch.rds'
• ‘Hippo_NeuN_correlations_summary_results_SampleMatch.rds'
• ‘Hippo_GFAP_correlations_summary_results_SampleMatch.rds'
• ‘Hippo_Iba1_correlations_summary_results_SampleMatch.rds'

FDR-corrected p-values per multi-level correlation per stain:

• "MultilevelCorrOutput_Iba1.csv"
• “MultilevelCorrOutput_NeuN.csv”
• “MultilevelCorrOutput_GFAP.csv”
• “MultilevelCorrOutput_AB1.42.csv”

Multi-level correlation output per stain with FDR-corrected p-values for all stains:

• "MultilevelCorrOutput_FDRAdjusted_AllStains.csv"

Application in Manuscript:

Multi-level correlation results produced from this script are represented in Figure 5a-c and used as input data for the GSEA (output represented in Figure 6). The compiled output of this script is published as Supplemental Data 5 and is the input for the GSEA completed in WebGestalt (see input parameters in the WebGestalt_InputParameters.png).

StainLoad_GeneExpression_ScatterPlots.R

Purpose:

This script contains the code to normalize and transform gene expression count data to create boxplots and scatterplots relating hippocampal formation stain load and gene expression in the same samples.

Input files:

This script reads in the R object output of a DESeq model of the intercept (design = ~1): "DESeq_ModelOutput_Intercept_BySample.rds". This object includes gene expression count data of the 34 samples included in this analysis. The file SampleMatch_GroupKey.csv is also read into this script and includes the metadata and the hippocampal formation stain load of the 34 samples included in this analysis.

Output files:

Boxplots and scatterplots integrating hippocampal stain load and gene expression data are output according to the stain and gene of interest selected.

Application in Manuscript:

This transformed data and plotting functions used in this script are represented in Figure 5d.