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Delandre, S.

Publications and source records attributed to Delandre, S..

2 recordsLinked to original sources

Visual quality control with CytoMDS, a Bioconductor package for low dimensional representation of cytometry sample distances

Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, identification of potential batch effects and outlying samples is paramount, to avoid mistaking these effects for true biological signal in downstream analyses. However, this task can prove to be delicate and tedious, especially for datasets with dozens of samples. Here, we present CytoMDS, a Bioconductor package implementing a dedicated method for low dimensional representation of cytometry samples composed of marker expressions for up to millions of single cells. This method allows a global representation of all samples of a study, with one single point per sample, in such a way that projected distances can be visually interpreted. CytoMDS uses Earth Movers Distance for assessing dissimilarities between multi-dimensional distributions of marker expression, and Multi Dimensional Scaling for low dimensional projection of distances. Some additional visualization tools, both for projection quality diagnosis and for user interpretation of the projection coordinates, are also provided in the package. We demonstrate the strengths and advantages of CytoMDS for QC of cytometry data on three real biological datasets, revealing the presence of low quality samples, batch effects and biological signal between sample groups.

bioinformatics↗

CytoPipeline & CytoPipelineGUI: An R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data

BackgroundWith the increase of the dimensionality in flow cytometry data over the past years, there is a growing need to replace or complement traditional manual analysis (i.e. iterative 2D gating) with automated data analysis pipelines. A crucial part of these pipelines consists of pre-processing and applying quality control filtering to the raw data, in order to use high quality events in the downstream analyses. This part can in turn be split into a number of elementary steps : signal compensation or unmixing, scale transformation, debris, doublets and dead cells removal, batch effect correction, etc. However, assembling and assessing the pre-processing part can be challenging for a number of reasons. First, each of the involved elementary steps can be implemented using various methods and R packages. Second, the order of the steps can have an impact on the downstream analysis results. Finally, each method typically comes with its specific, non standardized diagnostic and visualizations, making objective comparison difficult for the end user. ResultsHere, we present CytoPipeline and CytoPipelineGUI, two R packages to build, compare and assess pre-processing pipelines for flow cytometry data. To exemplify these new tools, we present the steps involved in designing a pre-processing pipeline on a real life dataset and demonstrate different visual assessment use cases. We also set up a benchmarking comparing two pre-processing pipelines differing by their quality control methods, and show how the package visualization utilities can provide crucial user insight into the obtained benchmark metrics. ConclusionCytoPipeline and CytoPipelineGUI are two Bioconductor R packages that help building, visualizing and assessing pre-processing pipelines for flow cytometry data. They increase productivity during pipeline development and testing, and complement benchmarking tools, by providing user intuitive insight into benchmarking results.

bioinformatics↗