Identify genes with spatial expression trends in single-cell gene-expression data
trendsceek has been tested on R 3.3.1 and is platform independent (tested on Linux, OS X and Windows). For parallel execution, trendsceek has been tested on a shared-memory server (120 cores, Intel Xeon 2.3GHz, x86_64, 512Gb RAM).
Typical installation takes <10 minutes.
First, install the package dependencies which are available on bioconductor but not on CRAN:
source("http://www.bioconductor.org/biocLite.R")
deps = c('BiocParallel', 'genefilter', 'DESeq2')
new_deps = deps[!(deps %in% installed.packages()[,"Package"])]
if(length(new_deps) != 0){biocLite(new_deps)}Installation can then be done via the devtools package:
library('devtools')
devtools::install_github('edsgard/trendsceek')Alternatively, installation can then be done from a local binary package tarball from the shell:
R CMD INSTALL trendsceek_1.0.0.tar.gzOnce you've installed trendsceek you'll be able to follow the vignette-tutorial. You can open it by:
vignette('trendsceek')Expected run-time on a normal desktop computer is <10 min.
library('trendsceek')
##create synthetic dataset
pp = sim_pois(300)
low_expr = c(10, 10)
high_expr = c(20, 50)
pp = add_markdist_hotspot(pp, low_expr, high_expr)
##run trendsceek
trendstat_list = trendsceek_test(pp, nrand = 100, ncores = 1)
head(trendstat_list[['supstats_wide']])
##show significant genes
sig_list = extract_sig_genes(trendstat_list, alpha = 0.1)
sig_genes = sig_list[['markcorr']][, 'gene']
print(sig_genes)
plot_trendstats(trendstat_list, sig_genes)
pp_sig = pp_select(pp, sig_genes)
plot_pp_scatter(pp_sig, log_marks = FALSE, scale_marks = TRUE, pal.direction = -1)
##cells located in high-expressing regions of the significant genes
cellpeaks_siggenes = cellsceek_test(pp_sig)
sig_cells = get_sigcells(cellpeaks_siggenes)
plot_pp_density(pp_sig, log_marks = FALSE, cells2highlight = sig_cells) To get help for specific functions you can use ?fcn, for example:
library('trendsceek')
?trendsceek_testThe complete function reference manual for all functions can be found at "doc/refman.pdf" within the installed library directory (to find your R library directories you can call .libPaths() from within R). You can also view the latest version by:
browseURL('https://github.com/edsgard/trendsceek/tree/master/inst/doc/refman.pdf')If you use trendsceek, please cite it as follows:
Edsgärd D. et al., Identification of spatial expression
trends in single-cell gene expression data, Nature Methods, 2018
doi:10.1038/nmeth.4634
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