Spectre provides a number of options for performing quantitative, differential and statistical analysis of cytometry data after the initial analysis using clustering or similar methods. Here we provide a demonstration of a number these options.
For this tutorial, we will use one of the demo datasets included in Spectre: a dataset of cells isolated from murine brains, 7 days following mock infection, or infection with West Nile virus (WNV). The. demo.clustered dataset has already been subject to arcsinh transformation, clustering, and population annotation..
We will also provide some 'cell count' data for each sample (i.e. number of total leukocytes in each sample). We expsect
Key to the comparison of populations across samples, is the generation of 'summary' data. Where 'cellular' data consists of cells (rows) vs cell features (columns: e.g. CD4 expression, CD8 expression etc); 'summary' data consists of samples (rows) vs sample features (number of monocytes per sample, expression level of Ly6C on CD8 T cells, etc). This summary data can then be used to generate plots that compare these metrics between experimental groups.
First, let's examine the columns in the cellular dataset.
We can choose any number of these to be measured as 'dynamic' colums (dyn.cols), where we will measure the median expression of these markers on each population in each sample. In this case we will choose CD11b (#15) and Ly6C (#18).
To create the summary data, we can use the create.sumtable function.
Once the function is complete, we can review the data.
Each row represents a sample, and each column a feature of that sample (e.g. Percent of sample -- CD4 T cells, etc).
Review all of the sample 'features' that we have calculated.