UsingSingleRtoannotatesingl

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UsingSingleRtoannotatesingl

2023/7/6 17:07:13 ('互联网')

Using SingleR to annotate single-cell RNA-seq data

Aaron Lun*, Jared M. Andrews1, Friederike Dündar2 and Daniel Bunis3

1Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
2Applied Bioinformatics Core, Weill Cornell Medicine
3Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA

*infinite.monkeys.with.keyboards@gmail.com

Revised: December 18th, 2019Package

SingleR 1.2.4

1 Introduction

SingleR is an automatic annotation method for single-cell RNA sequencing (scRNAseq) data (Aran et al. 2019).Given a reference dataset of samples (single-cell or bulk) with known labels, it labels new cells from a test dataset based on similarity to the reference set.Specifically, for each test cell:

We compute the Spearman correlation between its expression profile and that of each reference sample.This is done across the union of marker genes identified between all pairs of labels.We define the per-label score as a fixed quantile (by default, 0.8(人均gdp是什么意思？人均GDP即人均国内生产总值（Real GDP per capita），是人们了解和把握一个国家或地区的宏观经济运行状况的重要指标之一。)) of the distribution of correlations.We repeat this for all labels and we take the label with the highest score as the annotation for this cell.We optionally perform a fine-tuning step:The reference dataset is subsetted to only include labels with scores close to the maximum.Scores are recomputed using only marker genes for the subset of labels.This is iterated until one label remains.

Automatic annotation provides a convenient way of transferring biological knowledge across datasets.In this manner, the burden of manually interpreting clusters and defining marker genes only has to be done once, for the reference dataset, and this knowledge can be propagated to new datasets in an automated manner.

2 Using the built-in references

SingleR provides several reference datasets (mostly derived from bulk RNA-seq or microarray data) through dedicated data retrieval functions.For example, we obtain reference data from the Human Primary Cell Atlas using the HumanPrimaryCellAtlasData() function,which returns a SummarizedExperiment object containing matrix of log-expression values with sample-level labels.

library(SingleR)hpca.se - HumanPrimaryCellAtlasData()hpca.se

## class: SummarizedExperiment ## dim: 19363 713 ## metadata(0):## assays(1): logcounts## rownames(19363): A1BG A1BG-AS1 ... ZZEF1 ZZZ3## rowData names(0):## colnames(713): GSM112490 GSM112491 ... GSM92233 GSM92234## colData names(3): label.main label.fine label.ont

Our test dataset will is taken from La Manno et al. (2016).
For the sake of speed, we will only label the first 100 cells from this dataset.

library(scRNAseq)hESCs - LaMannoBrainData('human-es')hESCs - hESCs[,1:100]# SingleR() expects log-counts, but the function will also happily take raw# counts for the test dataset. The reference, however, must have log-values.library(scater)hESCs - logNormCounts(hESCs)

We use our hpca.se reference to annotate each cell in hESCs via the SingleR() function, which uses the algorithm described above.Note that the default marker detection method is to take the genes with the largest positive log-fold changes in the per-label medians for each gene.

pred.hesc - SingleR(test = hESCs, ref = hpca.se, labels = hpca.se$$label.main)pred.hesc

## DataFrame with 100 rows and 5 columns## scores first.labels## matrix character ## 1772122_301_C02 0.347652:0.109547:0.123901:... Neuroepithelial_cell## 1772122_180_E05 0.361187:0.134934:0.148672:... Neuroepithelial_cell## 1772122_300_H02 0.446411:0.190084:0.222594:... Neuroepithelial_cell## 1772122_180_B09 0.373512:0.143537:0.164743:... Neuroepithelial_cell## 1772122_180_G04 0.357341:0.126511:0.141987:... Neuroepithelial_cell## ... ... ...## 1772122_299_E07 0.371989:0.169379:0.1986877:... Neuroepithelial_cell## 1772122_180_D02 0.353314:0.115864:0.1374981:... Neuroepithelial_

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