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https://github.com/edsgard/trendsceek

Identify genes with spatial expression trends in single-cell gene expression data
https://github.com/edsgard/trendsceek

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Identify genes with spatial expression trends in single-cell gene expression data

Host: GitHub
URL: https://github.com/edsgard/trendsceek
Owner: edsgard
Created: 2017-06-14T13:27:09.000Z (about 7 years ago)
Default Branch: master
Last Pushed: 2018-03-20T17:02:02.000Z (over 6 years ago)
Last Synced: 2024-03-19T01:20:50.506Z (4 months ago)
Language: HTML
Homepage:
Size: 9.7 MB
Stars: 42
Watchers: 7
Forks: 17
Open Issues: 5
Metadata Files:
- Readme: README.md

Lists

awesome_single_cell - trendsceek - [R] - [Identification of spatial expression trends in single-cell gene expression data](https://www.nature.com/articles/nmeth.4634) (Software packages / RNA-seq)
awesome-single-cell - trendsceek - [R] - [Identification of spatial expression trends in single-cell gene expression data](https://www.nature.com/articles/nmeth.4634) (Software packages / RNA-seq)
awesome-single-cell - trendsceek - [R] - [Identification of spatial expression trends in single-cell gene expression data](https://www.nature.com/articles/nmeth.4634) (Software packages / RNA-seq)

README

        # trendsceek

Identify genes with spatial expression trends in single-cell gene-expression data 

## System requirements

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).

## Installation

Typical installation takes <10 minutes.

First, install the package dependencies which are available on bioconductor but

not on CRAN:

```R

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:

```R

library('devtools')

devtools::install_github('edsgard/trendsceek')

```

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

tarball from the shell:

```bash

R CMD INSTALL trendsceek_1.0.0.tar.gz

```

## Tutorial/Demo

Once you've installed trendsceek you'll be able to follow the

vignette-tutorial. You can open it by:

```R

vignette('trendsceek')

```

Expected run-time on a normal desktop computer is <10 min.

## Minimal example

```R

     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)	 

```

## Function reference manual

To get help for specific functions you can use ?fcn, for example:

```R

library('trendsceek')

?trendsceek_test

```

The 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:

```R

browseURL('https://github.com/edsgard/trendsceek/tree/master/inst/doc/refman.pdf')

```

## Citation

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