This new tool has gained significant traction in numerous research areas including immunology, developmental and cancer biology, and is being continually improved in terms of the technology and analytical pipelines. Single cell RNA sequencing (scRNAseq) has enabled researchers to interrogate cellular phenotypes at an unprecedented resolution and led to the discovery of several new biological phenomena. CIPR can accurately predict the identity of a variety of cell clusters and can be used in various experimental contexts across a broad spectrum of research areas.
#Annotate app software
Platform independence owing to Shiny framework and the requirement for a minimal programming experience allows this software to be used by researchers from different backgrounds. ConclusionsĬIPR facilitates scRNAseq data analysis by annotating unknown cell clusters in an objective and efficient manner. Benchmarking CIPR against existing functionally similar software revealed that our algorithm is less computationally demanding, it performs significantly faster and provides accurate predictions for multiple cell clusters in a scRNAseq experiment involving tumor-infiltrating immune cells. The option to filter out lowly variable genes and to exclude irrelevant reference cell subsets from the analysis can improve the discriminatory power of CIPR suggesting that it can be tailored to different experimental contexts. CIPR provides 2 mouse and 5 human reference datasets, and its pipeline allows inter-species comparisons and the ability to upload a custom reference dataset for specialized studies. ResultsĬIPR employs multiple approaches for calculating the identity score at the cluster level and can accept inputs generated by popular scRNAseq analysis software. CIPR can be easily integrated into the current pipelines to facilitate scRNAseq data analysis. To improve the quality and efficiency of annotating cell clusters in scRNAseq data, we present a web-based R/Shiny app and R package, Cluster Identity PRedictor (CIPR), which provides a graphical user interface to quickly score gene expression profiles of unknown cell clusters against mouse or human references, or a custom dataset provided by the user. On the other hand, manually annotating single cell clusters by examining the expression of marker genes can be subjective and labor-intensive. The current solutions for annotating single cell clusters generally lack a graphical user interface, can be computationally intensive or have a limited scope. During the analysis of scRNAseq data, annotating the biological identity of cell clusters is an important step before downstream analyses and it remains technically challenging. Single cell RNA sequencing (scRNAseq) has provided invaluable insights into cellular heterogeneity and functional states in health and disease.
0 kommentar(er)
