Yano

Spatial dissimilarity analysis in single cells

Introduction

Yano represents an R/C toolkit designed for conducting spatial dissimilarity analysis on single-cell RNA sequencing data. This method revolves around the core concept of examining the distinct expression patterns of a given feature (e.g. exon, snp allele) in relation to its associated binding feature (typically a gene or another allele at the same genomic locus) within the context of cell lineage (1D), spatial position (2D), or the multi-dimensional PCA space. The discernible differences in feature expression patterns and their binding features provide insights into a range of biological phenomena, including alternative splicing, cis-antisense RNA regulation, allele-specific gene expression, and more.

Yano is seamlessly integrated with Seurat, building upon the Seurat object’s framework. Users can perform conventional cell clustering analyses using the state-of-the-art Seurat pipeline and then incorporate exon, SNP counts as new “assays” within the Seurat objects. Subsequently, Yano facilitates the assessment of spatial dissimilarity between these two assays. For more details about the method, please refer to our manuscript.

INSTALL

if (!require("BiocManager")) install.packages('BiocManager') 
BiocManager::install("shiquan/Yano")

Notice: Multithread mode is disabled on macOS by default due to the lack of OpenMP support. However, data.table provides a useful tutorial on how to enable OpenMP on macOS. For more information, please refer to this guide: https://github.com/Rdatatable/data.table/wiki/Installation#enable-openmp-for-macos.

Get started

A typical workflow with Yano starts by using a built-in dataset.

require(Yano)
data("glbt_small")
DefaultAssay(glbt_small) <- "RNA"
glbt_small <- NormalizeData(glbt_small) %>% RunUMAP(dim = 1:20)

DimPlot(glbt_small, label = TRUE, label.size = 5)

DefaultAssay(glbt_small) <- "exon"
glbt_small <- NormalizeData(glbt_small)
glbt_small <- ParseExonName(glbt_small)
grep("_wm$",names(glbt_small), value=TRUE)

character(0)

glbt_small <- RunAutoCorr(glbt_small)
grep("_wm$",names(glbt_small), value=TRUE)

[1] "pca_wm"

glbt_small <- SetAutoCorrFeatures(glbt_small)
glbt_small <- RunBlockCorr(glbt_small, bind.name = "gene_name", bind.assay = "RNA")

# Manhattan plot for spatial dissimilarity test result
FbtPlot(glbt_small, val = "gene_name.padj")

FeaturePlot(glbt_small, features = c("chr19:16095264-16095454/+/TPM4", "TPM4"), order=TRUE)

# Track plot for gene coverage at different cell types
db <- gtf2db("./gencode.v44.annotation.gtf.gz")

[2025-03-21 13:12:43] GTF loading..
[2025-03-21 13:12:55] Load 62700 genes.

TrackPlot(bamfile="./Parent_SC3v3_Human_Glioblastoma_possorted_genome_bam.bam", gtf =db, gene = "TPM4", junc = TRUE, cell.group = Idents(glbt_small), highlights = c(16095264,16095454))

See short cases for more details.

Short cases

• Alternative splicing analysis for scRNA-seq
• Allele-specific gene expression analysis for scRNA-seq
• Annotating and prioritizing genetic variants for scRNA-seq
• Perform alternative splicing analysis for multiple Visium samples

Changelog

1.0 2025/02/19

First stable release.

0.0.0.9999 2023/03/22

Init version.

Issues or questions

• https://github.com/shiquan/Yano/issues
• https://github.com/shiquan/Yano/discussions

Functions

Function(s)	Description
annoBED	Annotate BED with preload GTF.
annoVAR	Annotate genetic variants with preload GTF and/or VCF databases.
AutoCorrFeatures	Select spatial autocorrelated features.
FbtPlot	Generate a mahattan plot for features.
FindAltExp	Find group specific alternative expressed features.
FindAllAltExp	Find alternative expressed features for all groups.
GetWeights	Get weight matrix.
gtf2db	Load GTF database into memory.
mergeMatrix	Merge two or more count matrix into one.
ParseBED	Parse chromsome name, locations and strand from BED names.
ParseExonName	Parse chromsome name, locations, strand and gene name from Exon/Junction names.
ParseVarName	Parse chromsome name, locations and strand from genetic variant names.
PSIPlot	Plot Percent Spliced-IN value on a reduction map.
RatioPlot	Plot ratio of feature and binding feature expression on a reduction map.
ReadPISA	Load raw matrix which generated by PISA.
RunAutoCorr	Perform spatial autocorrelation test for features.
RunBlockCorr	Perform spatial dissimilarity test for features.
RunDEXSeq	Run alternative expression analysis with DEXSeq.
QuickRecipe	Run Seurat pipeline.
SetAutoCorrFeatures	Setup spatial autocorrelated features.
TrackPlot	Generate tracks plot from BAM(s).
varanno	Low level version of annoVAR, need specify chr, position manually.