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https://github.com/kieranrcampbell/switchde
Inference of switch-like differential expression along single-cell trajectories
https://github.com/kieranrcampbell/switchde
gene-expression genomics single-cell
Last synced: 23 days ago
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Inference of switch-like differential expression along single-cell trajectories
- Host: GitHub
- URL: https://github.com/kieranrcampbell/switchde
- Owner: kieranrcampbell
- Created: 2015-07-13T15:29:18.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-10-24T22:52:08.000Z (over 6 years ago)
- Last Synced: 2024-05-16T19:23:30.239Z (about 2 months ago)
- Topics: gene-expression, genomics, single-cell
- Language: R
- Homepage:
- Size: 259 KB
- Stars: 19
- Watchers: 3
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Lists
- awesome_single_cell - switchde - [R] - Differential expression analysis across pseudotime. Identify genes that exhibit switch-like up or down regulation along single-cell trajectories along with where in the trajectory the regulation occurs. (Software packages / RNA-seq)
README
# switchde
[](https://travis-ci.org/kieranrcampbell/switchde)
Inference of switch-like differential expression along single-cell trajectories
## Installation
`switchde` is available on both Bioconductor and Github.
### Installation from Bioconductor
```r
source("https://bioconductor.org/biocLite.R")
biocLite("switchde")
```
### Installation from Github using Devtools
```r
# install.packages("devtools") # if devtools not already installed
devtools::install_github("kieranrcampbell/switchde")
```
## Introduction
`switchde` is an `R` package for detecting switch-like differential expression along single-cell RNA-seq trajectories. It assumes genes follow a sigmoidal pattern of gene expression and tests for differential expression using a likelihood ratio test. It also returns maximum likelihood estimates (MLE) for the sigmoid parameters, which allows filtering of genes for up or down regulation as well as where along the trajectory the regulation occurs.
The parametric form of gene expression assumed is sigmoidal:
Governed by three parameters:
* `mu_0` The half-peak expression
* `k` The 'activation strength'. If positive, the gene is upregulated along the trajectory; if negative, the gene is downregulated. The magnitude of `k` corresponds to how fast the gene is up or down regulated.
* `t_0` The 'activation time', or where in the trajectory this behaviour occurs. Note this parameter should be interpreted with respect to the overall range of the pseudotimes supplied.
## Usage
`switchde` accepts either an `SingleCellExperiment` from [SingleCellExperiment](https://github.com/drisso/SingleCellExperiment) or a matrix of gene expression measurents. These should ideally be in `log(TPM + 1)` form, but any logged non-negative expression measurements will work.
We begin with an `SingleCellExperiment` called `sce`, or equivalently a gene-by-cell expression matrix `X = assay(sce, "exprs")`. We also require a pseudotime vector `pseudotime`. Then call
```r
sde <- switchde(sce, pseudotime)
```
or equivalently
```r
sde <- switchde(X, pseudotime)
```
This outputs a `data.frame` with six columns:
```r
sde
# Source: local data frame [5,000 x 6]
#
# gene pval qval mu0 k t0
#
# 1 ENSG00000225976.4 1.393383e-22 1.024546e-20 104.86694912 -0.061517122 -68.87160
# 2 ENSG00000126522.12 2.185632e-01 6.067830e-01 1.22577161 -0.018819499 45.04442
# 3 ENSG00000239917.3 9.300623e-01 1.000000e+00 0.07908401 0.013177035 45.04440
# 4 ENSG00000151413.12 8.434079e-01 1.000000e+00 1.54634312 -0.005008349 45.04431
# 5 ENSG00000163814.3 6.217089e-02 2.634360e-01 0.18162897 -0.151326785 47.80757
# 6 ENSG00000197472.10 5.324570e-05 6.969332e-04 0.46516141 -45.928518652 23.94368
# 7 ENSG00000224908.1 1.309708e-01 4.336783e-01 0.02591063 137.415319733 60.60278
# 8 ENSG00000086717.13 8.203174e-02 3.190731e-01 0.04509236 256.638830394 49.59851
# 9 ENSG00000215183.4 2.127059e-01 5.991007e-01 0.02842577 -0.900165012 46.86811
# 10 ENSG00000127884.4 7.516905e-01 1.000000e+00 5.62698376 0.001580912 45.04440
# .. ... ... ... ... ... ...
```
with columns:
* `gene` The gene name, taken from either `featureNames(sce)` or `rowNames(X)`
* `pval` The p-value associated with differential expression
* `qval` The Benjamini-Hochberg corrected q-value associated with differential expression
* `mu0` The MLE estimate of `mu_0`
* `k` The MLE estimate of `k`
* `t0` The MLE estimate of `t_0`
We can also extract the parameters and plot the results:
```r
gene <- sde$gene[1]
pars <- extract_pars(sde, gene)
print(pars)
# mu0 k t0
# 104.86694912 -0.06151712 -68.87160316
switchplot(exprs(sce)[gene, ], pseudotime, pars)
```
## Authors
Kieran Campbell & Christopher Yau
Wellcome Trust Centre for Human Genetics, University of Oxford