The Open Source ImmGen project (GSE122108) is a collaborative effort devoted to RNAseq profiling of ex vivo sorted mononuclear phagocytes.
GAM-clustering provides metabolic variability within dataset using a novel network-based computational approach that utilizes cellular transcriptional profiles as proxies. The metabolic network of reactions from KEGG database is presented as a graph that has vertices corresponding to metabolites and the edges corresponding to the reactions with the expressed genes. In the graph the method tries to find a set of connected subgraphs, with each corresponding well to a certain gene expression pattern. Curret analysis reveals the major metabolic features associated with different subpopulations and highlights a number of metabolic modules that are specific to individual cell types, tissues of residence, or developmental stages.
To explore data visit the following interactive browser.
Raw counts are processed by rawDataProcessing.R script and the output object es.top12k has the following structure:
> load("Data/337_es.top12k.Rda")
> dplyr::glimpse(exprs(es.top12k))
## num [1:12000, 1:337] 14.7 12.7 13 12.1 13.4 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:12000] "Actb" "Cst3" "Fth1" "Eef1a1" ...
## ..$ : chr [1:337] "MF.64pLYVEpIIn.Ao.1" "MF.64pLYVEpIIn.Ao.2" "MF.64pLYVEpIIn.Ao.3" ...
> Biobase::exprs(es.top12k)[1:3,1:3]
## MF.64pLYVEpIIn.Ao.1 MF.64pLYVEpIIn.Ao.2 MF.64pLYVEpIIn.Ao.3
## Actb 14.71612 15.00513 14.76655
## Cst3 12.67268 12.83560 12.58577
## Fth1 12.95467 13.27726 13.06159The initial patterns are defined using k-means clustering on gene expression matrix and then are refined in an iterative process using the network connections (modulesDeriving.R).
The final output presents a set of specific subnetworks (also called metabolic modules) that reflect metabolic variability within a given transcriptional dataset.
Each metabolic module is a piece of metabolic network whose gene expression has correlated expression pattern across all dataset. The following graph and heatmap represent network and constituting genes' expression for module 5, correspondingly:
Averaged gene expression of all modules is represented at the following summary heatmap:
Functional annotation of obtained modules is based on KEGG and Reactome canonical pathways (modulesAnnotation.R).
The following example is devoted to module 5 (k - number of module genes in a particular pathway, K - number of genen in a particular pathway):
> paths <- data.table::fread("Data/m.5.pathways_mod.tsv")
> paths[1:3,]
## PATHID pval k K padj PATHNAME genes
## 1: R-MMU-191273 1.009604e-48 17 24 1.237774e-45 Cholesterol biosynthesis Hmgcs1 Hmgcr Msmo1 Cyp51 Mvd ...
## 2: R-MMU-8957322 9.801101e-39 17 67 6.008075e-36 Metabolism of steroids Hmgcs1 Hmgcr Msmo1 Cyp51 Mvd ...
## 3: R-MMU-556833 1.406903e-27 18 395 5.749543e-25 Metabolism of lipids Hmgcs1 Hmgcr Msmo1 Cyp51 Aacs ...