# SPADEVizR: an R package for Visualization, Analysis and Integration of SPADE results

Guillaume Gautreau, David Pejoski, Roger Le Grand, Antonio Cosma, Anne-Sophie Beignon, Nicolas Tchitchek

## ► To cite this version:

Guillaume Gautreau, David Pejoski, Roger Le Grand, Antonio Cosma, Anne-Sophie Beignon, et al. SPADEVizR: an R package for Visualization, Analysis and Integration of SPADE results. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM) 2016, Jun 2016, Lyon, France. 10.7490/f1000research.1112446.1 . hal-04862508

HAL Id: hal-04862508

https://hal.science/hal-04862508v1

Submitted on 3 Jan 2025

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## Acquisition

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## Identification of cell clusters

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Cytometry is used to characterize cell phenotypes at a single cell level. The increase of measurable cell markers (up to 50 markers), thanks to mass cytometry [1] has led to the development of new computational approaches to identify groups of cells (cell clusters) having similar marker expressions. For this purpose, different algorithms have been proposed such as viSNE, ACCENSE and SPADE [2].

The SPADE algorithm is a popular tool to identify cell clusters with objective to explore mass-cytometry data.

SPADE performs the following computation:

1. All samples are down-sampled using a density-based approach

2. A hierarchical agglomerative clustering is performed to identify clusters of cells having similar expressions for selected markers (called clustering markers)

3. A minimal spanning tree is built to link similar cell clusters

4. An up-sampling procedure is performed to associate all cells of each sample to the cluster with the closer phenotype

Thanks to their stable similar phenotypes, the SPADE clusters behaviors can be understood in terms of variations of cell abundance between samples, simplifying comparisons of biological conditions.

<figure><img src="image_5.png" /><figcaption>The SPADE workflow: an example with a two-parameter dataset</figcaption></figure>

## Analyses using SPADEVizR, an R package designed to provide deeper interpretations and characterizations of identified cell clusters

SPADEVizR identifies and visualizes cell clusters with relevant biological similarities, dynamics and behaviors.

The package involves statistical calculations and representation techniques such as multidimensional scaling (MDS), parallel coordinates, volcano plots and streamgraph.

All representations use the ggplot2 library and can then be modified by the user.

We present here some capabilities of our SPADEVizR R package on a dataset obtained in the context of a macaque vaccine study [3] based on 5 individuals at 3 time points (15 samples with 27 markers each).

## Identification of cell clusters having statistically relevant behaviors

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Six cell clusters have been identified as *Abundant Clusters*, that is to say, clusters having an abundance statistically greater than 2% (*p*-value < 0.01).

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Six cell clusters have been identified as *Differentially Abundant Clusters*, that is to say, cluster having a statistically different abundance between 8 days and 28 days after the post boost vaccination with a fold-change greater than 2 (*p*-value < 0.05).

<figure><img src="image_8.png" /></figure>

Cluster 9 has been identified as a *Correlated Cluster*, that is to say, a cluster having an abundance statistically correlated to a biological variable with a coefficient of correlation above 0.8 (*p*-value < 0.05).

## Characterization of cell cluster phenotypes

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**Heatmap representation** showing marker median relative expressions for all clusters. The mean of the median expression of each marker has been classified in 5 different categories.

Hierarchical clustering have been performed at both the marker and cluster levels (represented using dendrograms). Markers used by SPADE as clustering markers are shown in bold.

<figure><img src="image_10.png" /></figure>

**Parallel coordinates representation**

showing the marker median expressions of each sample for a specific cluster (cluster 9). This representation helps to easily characterize the phenotype of a the cluster (or a set of clusters) for all cell markers. In addition, this representation contribute to estimate the homogeneity or heterogeneity of cluster phenotypes between samples or biological conditions. The grey ribbon allows to evaluate marker expressions in regards to the overall marker expressions.

## Visualization and comparison of biological conditions

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**Kinetics representation** showing the abundance evolution of two cell clusters (9 and 10) for each individual across the 3 time points of baseline, 8 days and 28 days after the post boost vaccination.

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**Multidimensional scaling (MDS) representation** showing the similarities between selected samples. In such representation, each dot represents a sample and the distance between the dots are proportional to the Euclidean distance between these objects. Samples of the same biological condition are surrounded and colored together. It can be inferred from this MDS representation, that the three distinct conditions are, as expected, correctly segregated.

<figure><img src="image_13.png" /></figure>

**Boxplot representation** showing the abundance of the cluster 9 at baseline, 8 days and 28 days after the post boost vaccination.

<figure><img src="image_14.png" /></figure>

**Streamgraph representation** showing absolute abundances for a set of specific clusters across all the samples. Samples are ordered in a way that each triplet of samples corresponds to an individual at baseline, 8 days and 28 days after the post boost vaccination.

<footnote>[1] Bendall, S. C. et al. Single-Cell Mass Cytometry of Differential, 332, 687-697 (2011).</footnote>

<footnote>[2] Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886-891 (2011).</footnote>

<footnote>[3] Pejoski, D. et al. Identification of vaccine-altered circulating B cell phenotypes using mass cytometry and a two-step clustering analysis. J. Immunol. in press, (2016)</footnote>