fcoex: co-expression for single-cell data

Computational Systems Biology Laboratory, University of São Paulo, Brazil

Introduction and basic pipeline

The goal of fcoex is to provide a simple and intuitive way to generate co-expression modules for single cell data. It’s targeted as a tool for exploratory data analysis, and as a stepping stone for obtaining insights from single cell data.

It is based in 3 steps:

• Pre-processing and label assignement (prior to fcoex)
• Discretization of gene expression
• Correlation and module detection via the FCBF algorithm (Fast Correlation-Based Filter)

First of all, we will load an a already preprocessed single cell dataset from 10XGenomics. It was preprocessed according to the OSCA pipeline, 14/08/2019). It contains peripheral blood mononuclear cells and the most variable genes.

library(fcoex, quietly = TRUE)

## Warning: replacing previous import 'Matrix::head' by 'utils::head' when loading
## 'fcoex'

library(SingleCellExperiment, quietly = TRUE)
data("mini_pbmc3k")

The mini_pbmc3k object is an object of the class SingleCellExperiment that we will explores in the vignette. For more information on the class, check this Introduction to the SingleCellExperiment Class.

Creating the fcoex object

The fcoex object is created from 2 different pieces: a previously normalized expression table (genes in rows) and a target factor with classes for the cells. (Note: fcoex does not need names, but only clusters, so you can run it even if you do not have names for your clusters.)

# Get clusters from the pre-processing
target <- colData(mini_pbmc3k)
target <- target$clusters

# Get normalized table from the pre-processing
exprs <- as.data.frame(assay(mini_pbmc3k, 'logcounts'))

# Create fcoex object
fc <- new_fcoex(data.frame(exprs),target)

Once you have set up your fcoex object, the first step is to convert a count matrix into a binarized dataframe. The binarization is needed for the fcoex algorithm and empirically we notice that significant biological signal is preserved with the default parameters.

The standard of fcoex works as follows:

For each gene, the maximum and minimum values are stored. This range is divided in n bins of equal width (parameter to be set). The first bin is assigned to the class “low” and all the others to the class “high”.

fc <- discretize(fc, number_of_bins = 8)

Getting the modules

Other discretization methods are avaible too, and this step affects the final results in many ways. If you have the time, you can try different discretization parameters and observe the impact in the data.

After the discretization, fcoex builds a co-expression network and extracts modules. Correlations are calculated via a information-theory method called Symmetrical Uncertainty. Three steps are present:

1 - Selection of n genes to be considered, ranked by correlation to the target variable.

2 - Detection of predominantly correlated genes, a feature selection approach defined in the FCBF algorithm

3 - Building of modules around selected genes. Correlations between two genes are kept if they are more correlated to each other than to the target.

You can choose either to have a non-parallel processing, with a progress bar, or a faster parallel processing without progress bar. Up to you.

fc <- find_cbf_modules(fc,n_genes_selected_in_first_step = 200, verbose = FALSE, is_parallel = FALSE)

## [1] "Number of features features =  1700"
## [1] "Number of prospective features =  199"
## [1] "Number of final features =  28"

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The last step created an adjacency matrix and extracted some co-expression modules. We can present that information in a visual way by using the get_nets function, that generates network plots for each module. The plots are created in the ggplot2 format and stored inside the fcoex object.

The visualizations it generates were heavily inspired by the CEMiTool package, as much of the code in fcoex was.

We will take a look at the first two networks using the show_net function.

fc <- get_nets(fc)

# Taking a look at the first two networks: 
network_plots <- show_net(fc)

network_plots[["CD79A"]]