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