Zhun Miao
2018-06-21
DEsingle
is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It will detect differentially expressed genes between two groups of cells in a scRNA-seq raw read counts matrix.
DEsingle
employs the Zero-Inflated Negative Binomial model for differential expression analysis. By estimating the proportion of real and dropout zeros, it not only detects DE genes at higher accuracy but also subdivides three types of differential expression with different regulatory and functional mechanisms.
For more information, please refer to the manuscript by Zhun Miao, Ke Deng, Xiaowo Wang and Xuegong Zhang.
If you use DEsingle
in published research, please cite:
Zhun Miao, Ke Deng, Xiaowo Wang, Xuegong Zhang (2018). DEsingle for detecting three types of differential expression in single-cell RNA-seq data. Bioinformatics, bty332. 10.1093/bioinformatics/bty332.
To install DEsingle
from Bioconductor:
```{r Installation from Bioconductor, eval = FALSE} if(!require(BiocManager)) install.packages("BiocManager") BiocManager::install("DEsingle")
To install the *developmental version* from [**GitHub**](https://github.com/miaozhun/DEsingle/):
```{r Installation from GitHub, eval = FALSE}
if(!require(devtools)) install.packages("devtools")
devtools::install_github("miaozhun/DEsingle", build_vignettes = TRUE)
To load the installed DEsingle
in R:
```{r Load DEsingle, eval = FALSE} library(DEsingle)
## Input
**`DEsingle`** takes two inputs: `counts` and `group`.
The input `counts` is a scRNA-seq **raw read counts matrix** or a **`SingleCellExperiment`** object which contains the read counts matrix. The rows of the matrix are genes and columns are cells.
The other input `group` is a vector of factor which specifies the two groups in the matrix to be compared, corresponding to the columns in `counts`.
## Test data
Users can load the test data in **`DEsingle`** by
```{r Load TestData}
library(DEsingle)
data(TestData)
The toy data counts
in TestData
is a scRNA-seq read counts matrix which has 200 genes (rows) and 150 cells (columns).
```{r counts} dim(counts) counts[1:6, 1:6]
The object `group` in `TestData` is a vector of factor which has two levels and equal length to the column number of `counts`.
```{r group}
length(group)
summary(group)
Here is an example to run DEsingle
with read counts matrix input:
```{r demo1, eval = FALSE}
library(DEsingle) data(TestData)
group <- factor(c(rep(1,50), rep(2,100)))
results <- DEsingle(counts = counts, group = group)
results.classified <- DEtype(results = results, threshold = 0.05)
### With SingleCellExperiment input
The [`SingleCellExperiment`](http://bioconductor.org/packages/SingleCellExperiment/) class is a widely used S4 class for storing single-cell genomics data. **`DEsingle`** also could take the `SingleCellExperiment` data representation as input.
Here is an example to run **`DEsingle`** with `SingleCellExperiment` input:
```{r demo2, eval = FALSE}
# Load library and the test data for DEsingle
library(DEsingle)
library(SingleCellExperiment)
data(TestData)
# Convert the test data in DEsingle to SingleCellExperiment data representation
sce <- SingleCellExperiment(assays = list(counts = as.matrix(counts)))
# Specifying the two groups to be compared
# The sample number in group 1 and group 2 is 50 and 100 respectively
group <- factor(c(rep(1,50), rep(2,100)))
# Detecting the DE genes with SingleCellExperiment input sce
results <- DEsingle(counts = sce, group = group)
# Dividing the DE genes into 3 categories at threshold of FDR < 0.05
results.classified <- DEtype(results = results, threshold = 0.05)
DEtype
subdivides the DE genes found by DEsingle
into 3 types: DEs
, DEa
and DEg
.
DEs
refers to “different expression status”. It is the type of genes that show significant difference in the proportion of real zeros in the two groups, but do not have significant difference in the other cells.
DEa
is for “differential expression abundance”, which refers to genes that are significantly differentially expressed between the groups without significant difference in the proportion of real zeros.
DEg
or “general differential expression” refers to genes that have significant difference in both the proportions of real zeros and the expression abundances between the two groups.
The output of DEtype
is a matrix containing the DE analysis results, whose rows are genes and columns contain the following items:
theta_1
, theta_2
, mu_1
, mu_2
, size_1
, size_2
, prob_1
, prob_2
: MLE of the zero-inflated negative binomial distribution's parameters of group 1 and group 2.total_mean_1
, total_mean_2
: Mean of read counts of group 1 and group 2.foldChange
: total_mean_1/total_mean_2.norm_total_mean_1
, norm_total_mean_2
: Mean of normalized read counts of group 1 and group 2.norm_foldChange
: norm_total_mean_1/norm_total_mean_2.chi2LR1
: Chi-square statistic for hypothesis testing of H0.pvalue_LR2
: P value of hypothesis testing of H20 (Used to determine the type of a DE gene).pvalue_LR3
: P value of hypothesis testing of H30 (Used to determine the type of a DE gene).FDR_LR2
: Adjusted P value of pvalue_LR2 using Benjamini & Hochberg's method (Used to determine the type of a DE gene).FDR_LR3
: Adjusted P value of pvalue_LR3 using Benjamini & Hochberg's method (Used to determine the type of a DE gene).pvalue
: P value of hypothesis testing of H0 (Used to determine whether a gene is a DE gene).pvalue.adj.FDR
: Adjusted P value of H0's pvalue using Benjamini & Hochberg's method (Used to determine whether a gene is a DE gene).Remark
: Record of abnormal program information.Type
: Types of DE genes. DEs represents differential expression status; DEa represents differential expression abundance; DEg represents general differential expression.State
: State of DE genes, up represents up-regulated; down represents down-regulated.To extract the significantly differentially expressed genes from the output of DEtype
(note that the same threshold of FDR should be used in this step as in DEtype
):
```{r extract DE, eval = FALSE}
results.sig <- results.classified[results.classified$pvalue.adj.FDR < 0.05, ]
To further extract the three types of DE genes separately:
```{r extract subtypes, eval = FALSE}
# Extract three types of DE genes separately
results.DEs <- results.sig[results.sig$Type == "DEs", ]
results.DEa <- results.sig[results.sig$Type == "DEa", ]
results.DEg <- results.sig[results.sig$Type == "DEg", ]
DEsingle
integrates parallel computing function with BiocParallel
package. Users could just set parallel = TRUE
in function DEsingle
to enable parallelization and leave the BPPARAM
parameter alone.
```{r demo3, eval = FALSE}
library(DEsingle)
results <- DEsingle(counts = counts, group = group, parallel = TRUE)
Advanced users could use a `BiocParallelParam` object from package `BiocParallel` to fill in the `BPPARAM` parameter to specify the parallel back-end to be used and its configuration parameters.
### For Unix and Mac users
The best choice for Unix and Mac users is to use `MulticoreParam` to configure a multicore parallel back-end:
```{r demo4, eval = FALSE}
# Load library
library(DEsingle)
library(BiocParallel)
# Set the parameters and register the back-end to be used
param <- MulticoreParam(workers = 18, progressbar = TRUE)
register(param)
# Detecting the DE genes in parallelization with 18 cores
results <- DEsingle(counts = counts, group = group, parallel = TRUE, BPPARAM = param)
For Windows users, use SnowParam
to configure a Snow back-end is a good choice:
```{r demo5, eval = FALSE}
library(DEsingle) library(BiocParallel)
param <- SnowParam(workers = 8, type = "SOCK", progressbar = TRUE) register(param)
results <- DEsingle(counts = counts, group = group, parallel = TRUE, BPPARAM = param)
See the [*Reference Manual*](https://bioconductor.org/packages/release/bioc/manuals/BiocParallel/man/BiocParallel.pdf) of [`BiocParallel`](http://bioconductor.org/packages/BiocParallel/) package for more details of the `BiocParallelParam` class.
## Visualization of results
Users could use the `heatmap()` function in `stats` or `heatmap.2` function in `gplots` to plot the heatmap of the DE genes DEsingle found, as we did in Figure S3 of the [*manuscript*](https://doi.org/10.1093/bioinformatics/bty332).
## Interpretation of results
For the interpretation of results when **`DEsingle`** applied to real data, please refer to the *Three types of DE genes between E3 and E4 of human embryonic cells* part in the [*Supplementary Materials*](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty332/4983067#supplementary-data) of our [*manuscript*](https://doi.org/10.1093/bioinformatics/bty332).
## Help
Use `browseVignettes("DEsingle")` to see the vignettes of **`DEsingle`** in R after installation.
Use the following code in R to get access to the help documentation for **`DEsingle`**:
```{r help1, eval = FALSE}
# Documentation for DEsingle
?DEsingle
```{r help2, eval = FALSE}
?DEtype
```{r help3, eval = FALSE}
# Documentation for TestData
?TestData
?counts
?group
You are also welcome to view and post DEsingle tagged questions on Bioconductor Support Site of DEsingle or contact the author by email for help.
Zhun Miao <[email protected]>
MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China.
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