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SPRINT

SPRINT is an efficient method to identify genes with spatial expression pattern. The intended applications are spatially resolved RNA-sequencing from e.g. Spatial Transcriptomics, or in situ gene expression measurements from e.g. SeqFISH, Merfish.

System requirements

SPRINT has been tested on R 3.3.1 and is platform independent (tested on Linux, OS X and Windows) Installation can then be done via the devtools package:

library('devtools')
devtools::install_github('xzhoulab/SPRINT')

Alternatively, installation can then be done from a local binary package from the shell:

R CMD INSTALL SPRINT_1.0.0.tar.gz

Sample Code: Analysis of Breast Cancer Data

    library('SPRINT')
    load("~/data/Layer2_BC_Count.rds")
     
    ## rawcount matrix of genes by cells/spots
    rawcount[1:5,1:5]
17.907x4.967 18.965x5.003 18.954x5.995 17.846x5.993 20.016x6.019
GAPDH 1 7 5 1 2
USP4 1 0 0 0 0
MAPKAPK2 1 1 0 0 1
CPEB1 0 0 0 0 0
LANCL2 0 0 0 0 0 
    ## extract the coordinates from the rawdata
    info <- cbind.data.frame(x=as.numeric(sapply(strsplit(colnames(rawcount),split="x"),"[",1)),
                            y=as.numeric(sapply(strsplit(colnames(rawcount),split="x"),"[",2)),
                            total_counts=apply(rawcount,2,sum))
    rownames(info) <- colnames(rawcount)

    ## filter genes and cells/spots and create the SPRINT object for following analysis
    sprint <- CreateSPRINTObject(counts=rawcount, location=info[,1:2],
                                 prectage = 0.1, 
                                 min_total_counts = 10)

    ## total counts for each cell/spot
    sprint@lib_size <- apply(sprint@counts, 2, sum)

    ## Take the first ten genes as an example
    sprint@counts   <- sprint@counts[1:10,]

    ## Estimating Parameter Under Null
    sprint <- sprint.vc(sprint,covariates = NULL, lib_size=sprint@lib_size, num_core=1,verbose=F)

    ## Calculating pval
    sprint <- sprint.test(sprint, check_positive = T, verbose=F)
    
    ## Check pvals 
    head(sprint@res_mtest[,c("combined_pvalue","adjusted_pvalue")])
combined_pvalue adjusted_pvalue
GAPDH 7.477171e-09 1.683453e-07
MAPKAPK2 1.016078e-01 5.952118e-01
MCL1 1.149519e-08 1.683453e-07
TMEM109 4.303998e-01 1.000000e+00
TMEM189 6.189064e-01 1.000000e+00
ITPK1 7.213287e-01 1.000000e+00