popsicleR: Pipeline of Preprocessing for Single Cell Experiment

In bicciatolab/PoPsicleR: Pipeline of Preprocessing for Single Cell Experiment

Pipeline of Preprocessing for Single Cell Experiment

Description

Here we present popsicleR, a flexible R package aimed at providing most of the necessary quality controls and preliminary information for complete and detailed analysis of single cell RNA-seq data. In its workflow, popsicleR integrates all the pivotal steps necessary to assess sample quality, apply data tailored filters and perform doublets calculation and annotation analysis.

Details

popsicleR builds on the R package Seurat and extends its functionalities with new ad hoc generated plots and calculations. Furthermore, popsicleR grants the possibility to investigate and analyze both human and mouse data through organism specific annotation packages.

Functions Index:

• PrePlots

• FilterPlots

• CalculateDoublets

• Normalize

• ApplyRegression

• CalculateCluster

• MakeAnnotation

Author(s)

Francesco Grandi, Jimmy Caroli, Oriana Romano, Matteo Marchionni, Mattia Forcato and Silvio Bicciato

Maintainers:

• Jimmy Caroli [e-mail]

• Francesco Grandi [e-mail]

References

Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, III WMM, Hao Y, Stoeckius M, Smibert P, Satija R (2019). “Comprehensive Integration of Single-Cell Data.” Cell, 177, –1888-1902–. https://doi.org/10.1016/j.cell.2019.05.031.

Samuel L. Wolock, Romain Lopez, Allon M. Klein, "Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data" (2019). Cell System, Volume 8,–281-291–.e9,ISSN 2405-4712,https://doi.org/10.1016/j.cels.2018.11.005.

Germain PL, Lun A, Macnair W and Robinson MD. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research (2021), 10:979 https://doi.org/10.12688/f1000research.73600.1

Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, Butte AJ, Bhattacharya M (2019). “Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.” Nat. Immunol., 20, –163-172–. doi: 10.1038/s41590-018-0276-y.

Sun H, Zhou Y, Fei L, Chen H, Guo G. "scMCA: A Tool to Define Mouse Cell Types Based on Single-Cell Digital Expression." (2019) Methods Mol Biol., 1935, –91-96–. doi: 10.1007/978-1-4939-9057-3_6. PMID: 30758821.

See Also

Useful links:

• https://github.com/bicciatolab/popsicleR

• Report bugs at https://github.com/bicciatolab/popsicleR/issues

Examples

PrePlots('breast_single_cell', input_data="/path/to/data", 0.1, 200, cellranger = TRUE, c('TP53','PTEN'), organism="human")

FilterPlots(umi_object, G_RNA_low = 500, G_RNA_hi= Inf, U_RNA_low = 0, U_RNA_hi = 37000, percent_mt_hi = 10, percent_ribo_hi= 100, percent_disso_hi = 100)

## first run:

umi_object <- CalculateDoublets(umi_object, method = "scrublet", dbs_thr='none', dbs_remove=FALSE)

## second run:

CalculateDoublets(umi_object, method = "scrublet", dbs_thr=0.22, dbs_remove=FALSE)

Normalize(umi_object, variable_genes=2000)

## first run:

ApplyRegression(UMI= umi_object, organism= "human", variables= "none", explore_PC=FALSE)

## second run:

ApplyRegression(UMI= umi_object, organism= "human", variables= c("nCount_RNA", "percent_mt", "S.Score", "G2M.Score"), explore_PC=TRUE)

CalculateCluster(umi_object, 12, organism= "human", cluster_res= 0.8)

MakeAnnotation(umi_object, organism="human", cluster_res=0.8)

bicciatolab/PoPsicleR documentation built on May 7, 2024, 7:26 a.m.