henhaobio
Sign in

Life Science AllianceBioinformaticsPeer-reviewed journal article

PieParty: Visualizing cells from scRNA-seq data as pie charts

Stefan KurtenbachiD, James Jarad DollariD, Anthony M. CruziD, Michael A. DuranteiD, Christina L. DecaturiD, J. William HarbouriD

PDFDOIPreprintCode

Abstract

Single cell RNA sequencing (scRNA-seq) has been a transformative technology in many research fields. Dimensional reduction techniques such as UMAP and tSNE are used to visualize scRNA-seq data in two or three dimensions in order for cells to be clustered in biologically meaningful ways. Subsequently, gene expression is frequently mapped onto these plots to show the distribution of gene expression across the plots, for instance to distinguish cell types. However, plotting each cell with only one color leads to repetitive and unintuitive representations. Here, we present Pie Party, which allows scRNA-seq data to be plotted such that every cell is represented as a pie chart, and every slice in the pie charts corresponds to the gene expression of individual genes. This allows for the simultaneous visualization of the expression of multiple genes and gene networks. The resulting figures are information dense, space efficient and highly intuitive. PieParty is publicly available on GitHub at https://github.com/harbourlab/PieParty.

Citations
2
FWCI
0.16
Field percentile
36th percentile

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISKotliar '21Kitanovski '24Çakır '20Wang '19Zhao '22Bhattachary '22
68% semantic matchBioinformatics
Dylan Kotliar, Andrés Colubri·Bioinformatics·Sep 30, 2021· 5 citations