Introduction

Mass Spectrometry (or CyTOF) is a single cell technology. It measures up to 50 protein markers on a single cell. The markers are antibodies labeled with stable isotopes and they might be markers of cell types or phenotypes.

CyTOF might be used to determine if there is any differences in cell abundances (type markers) or cell phenotypes (state markers) between two experimental conditions. In this package, we are proposing a tool to predict the power of a differential state test analysis.

Two packages are available on Bioconductor to perform differential state test analyses: CytoGLMM (Seiler 2020) and diffcyt (Weber 2019). These models are available in this package and their results, the adjusted p-value per marker, are used to compute the power an experiment.

Simulate in-silico data

In-silico CyTOF data are simulated using the following data generation process. We assumed two conditions for which one condition is the baseline (i.e. a control condition where no marker is different from the other) and the other condition contains some signals (i.e. at least one marker is differentially expressed). The parameters are defined as follow:

• the number of donors: the design of the data is paired, so the number of samples will be calculated accordingly, i.e. twice the number of donors;
• the subject effect: it defines how much the donors are different from each others;
• the number of cells per samples;
• the total number of markers tested in the experiment;
• the fold change of each differentially expressed marker: it represents how much different the marker is expected to be in comparison to the control condition. The mean of these markers will be multiplied by in the condition containing the differentially expressed markers;
• the distribution parameters for each marker: see details below.

The cell value’s mean of each marker is drawn from a Gamma distribution: μ_0, ij ∼ Γ(k, θ) where i the donor, j the marker, k the shape and θ the scale. These two last parameters are defined using the parameters provided by the user:

• cell value mean μ₀ in the control condition: with μ₀ = k ⋅ θ > 0;
• subject effect S: $S = \sqrt{k \cdot \theta^{2}}$.

The cell value mean of the differentially expressed marker(s) is then multiplied by the fold change defined earlier: μ_1, ij = μ_0, ij ⋅ ρ_j.

The cell values are drawn using a Negative Binomial:

• for non-differentially expressed markers: X_ij ∼ NB(μ_0, ij, σ)
• for differentially expressed markers: X_ij ∼ NB(μ_1, ij, σ)

Some of these parameters might need to be estimated using previous data or publicly available datasets. For instance, the dispersion and mean parameters of the negative binomial distribution might be estimated using fitdistr from the MASS package for each marker.

Power computation

The power is computed for the differentially expressed marker(s) (i.e. fold change different from 1). It is based on the adjusted p-values reported by the models: counting how many times the null hypothesis is correctly rejected. It is also important to note that the power returned by these computations uses the threshold of α = 0.05 as a significance level.

Shiny app

The shiny app is divided into two tabs: (1) the precomputed dataset tab: the power was pre-computed for multiple combinations of parameters and the user is able to search this grid of parameters; (2) the personalized dataset tab: the data and the power is computed on request based on the parameters chosen by the user.

library(CyTOFpower)
CyTOFpower()

Session information

sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
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#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
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#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
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#> [1] stats     graphics  grDevices utils     datasets  methods   base     
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#> [1] BiocStyle_2.35.0
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#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.37       R6_2.5.1            fastmap_1.2.0      
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#>  [7] knitr_1.49          htmltools_0.5.8.1   rmarkdown_2.29     
#> [10] buildtools_1.0.0    lifecycle_1.0.4     cli_3.6.3          
#> [13] sass_0.4.9          jquerylib_0.1.4     compiler_4.4.2     
#> [16] sys_3.4.3           tools_4.4.2         evaluate_1.0.1     
#> [19] bslib_0.8.0         yaml_2.3.10         BiocManager_1.30.25
#> [22] jsonlite_1.8.9      rlang_1.1.4