Software

Software developed as part of my research

The following R packages support the analysis and visualization of cytometry and gene expression data.

• SPADEVizR — SPADEVizR helps explore cell populations identified by SPADE and other clustering methods in cytometry data. It combines plots of marker expression and cell abundance with statistical comparisons between conditions, making it useful for interpreting changes in immune cell populations.

  Publication: Gautreau et al. SPADEVizR: an R package for visualization, analysis and integration of SPADE results. Bioinformatics (2017).

• CytoCompare — CytoCompare compares cell populations from flow or mass cytometry using the distributions of their marker expression. It provides measures and visualizations of phenotypic similarity to help compare populations across clustering methods, analysis settings, or biological studies.

  Publication: Platon et al. A computational approach for phenotypic comparisons of cell populations in high-dimensional cytometry data. Methods (2018).

• MDSRefMaps — MDSRefMaps builds a multidimensional scaling (MDS) reference map from high-dimensional data and projects additional samples or variables onto the same map. This is useful for comparing gene expression profiles with a reference dataset or viewing new samples alongside previously analyzed ones.

  Related publication: Tchitchek et al. Specific mutations in H5N1 mainly impact the magnitude and velocity of the host response in mice. BMC Systems Biology (2013).

• CytoBackBone — CytoBackBone combines information from cytometry panels that measure some markers in common. It uses these shared "backbone" markers to match cells with similar profiles and merge their marker information, allowing measurements from complementary panels to be explored together.

  Publication: Leite Pereira et al. CytoBackBone: an algorithm for merging of phenotypic information from different cytometric profiles. Bioinformatics (2019).

• SVD-MDS — SVD-MDS produces a low-dimensional map from pairwise distances between samples or other objects, using singular value decomposition to initialize multidimensional scaling. It can be used to visualize relationships in gene expression data and reports how well the resulting map preserves distances and local neighborhoods.

  Publication: Bécavin et al. Improving the efficiency of multidimensional scaling in the analysis of high-dimensional data using singular value decomposition. Bioinformatics (2011).

• CDS — CDS is a statistical test for identifying genes with different or similar expression levels between two biological conditions. It uses fold-change criteria and measurement variability to assess both kinds of result, and can also calculate confidence intervals for fold changes.

  Publication: Tchitchek et al. CDS: a fold-change based statistical test for concomitant identification of distinctness and similarity in gene expression analysis. Genomics, Proteomics & Bioinformatics (2012).

Database

• MONOCLdb — MONOCLdb is an interactive database of long non-coding RNA expression in mouse lungs following influenza A or SARS-CoV infection. It lets researchers explore expression profiles, gene annotations, co-expression networks, and predicted functional associations to investigate these RNAs in the context of respiratory infection.

  Publication: Josset et al. Annotation of long non-coding RNAs expressed in collaborative cross founder mice in response to respiratory virus infection reveals a new class of interferon-stimulated transcripts. RNA Biology (2014).