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Markov Affinity-based Graph Imputation of Cells (MAGIC) is an algorithmfor denoising and imputation of single cells applied to single-cell RNAsequencing data, as described in Van Dijk D et al. (2018), RecoveringGene Interactions from Single-Cell Data Using Data Diffusion, Cellhttps://www.cell.com/cell/abstract/S0092-8674(18)30724-4.
- MAGIC imputes missing data values on sparse data sets, restoring thestructure of the data
- It also proves dimensionality reduction and gene expressionvisualizations
- MAGIC can be performed on a variety of datasets
- Here, we show the usage of MAGIC on a toy dataset
- You can view further examples of MAGIC on real data in our notebooksunder
inst/examples:
Table of Contents
- Installation
- Installation from CRAN andPyPi
- Installation with devtools andreticulate
- Installation from source
- Quick Start
- Tutorial
- Issues
- FAQ
- Help
Installation
To use MAGIC, you will need to install both the R and Python packages.
If python or pip are not installed, you will need to install them.We recommend Miniconda3 to installPython and pip together, or otherwise you can install pip fromhttps://pip.pypa.io/en/stable/installing/.
Installation from CRAN
In R, run this command to install MAGIC and all dependencies:
install.packages("Rmagic")In a terminal, run the following command to install the Pythonrepository.
pip install --user magic-imputeInstallaton from source
To install the very latest version of MAGIC, you can install from GitHubwith the following commands run in a terminal.
git clone https://github.com/KrishnaswamyLab/MAGICcd MAGIC/pythonpython setup.py install --usercd ../RmagicR CMD INSTALL .Quick Start
If you have loaded a data matrix data in R (cells on rows, genes oncolumns) you can run PHATE as follows:
library(phateR)data_phate <- phate(data)Tutorial
Extra packages for the tutorial
We’ll install a couple more tools for this tutorial.
if (!require(viridis)) install.packages("viridis")if (!require(ggplot2)) install.packages("ggplot2")if (!require(phateR)) install.packages("phateR")If you have never used PHATE, you should also install PHATE from thecommand line as follows:
pip install --user phateLoading packages
We load the Rmagic package and a few others for convenience functions.
library(Rmagic)#> Loading required package: Matrixlibrary(ggplot2)#> Warning: package 'ggplot2' was built under R version 3.5.3library(viridis)#> Loading required package: viridisLitelibrary(phateR)#> #> Attaching package: 'phateR'#> The following object is masked from 'package:Rmagic':#> #> library.size.normalizeLoading data
The example data is located in the MAGIC R package.
# load datadata(magic_testdata)magic_testdata[1:5,1:10]#> A1BG-AS1 AAMDC AAMP AARSD1 ABCA12 ABCG2 ABHD13#> 6564 0.0000000 0.0000000 0.0000000 0 0 0 0.0000000#> 3835 0.0000000 0.8714711 0.0000000 0 0 0 0.8714711#> 6318 0.7739207 0.0000000 0.7739207 0 0 0 0.0000000#> 3284 0.0000000 0.0000000 0.0000000 0 0 0 0.0000000#> 1171 0.0000000 0.0000000 0.0000000 0 0 0 0.0000000#> AC007773.2 AC011998.4 AC013470.6#> 6564 0 0 0#> 3835 0 0 0#> 6318 0 0 0#> 3284 0 0 0#> 1171 0 0 0Running MAGIC
Running MAGIC is as simple as running the magic function.
# run MAGICdata_MAGIC <- magic(magic_testdata, genes=c("VIM", "CDH1", "ZEB1"))We can plot the data before and after MAGIC to visualize the results.
ggplot(magic_testdata) + geom_point(aes(VIM, CDH1, colour=ZEB1)) + scale_colour_viridis(option="B")The data suffers from dropout to the point that we cannot infer anythingabout the gene-gene relationships.
ggplot(data_MAGIC) + geom_point(aes(VIM, CDH1, colour=ZEB1)) + scale_colour_viridis(option="B")As you can see, the gene-gene relationships are much clearer afterMAGIC.
The data is sometimes a little too smooth - we can decrease t from theautomatic value to reduce the amount of diffusion. We pass the originalresult to the argument init to avoid recomputing intermediatesteps.
data_MAGIC <- magic(magic_testdata, genes=c("VIM", "CDH1", "ZEB1"), t=6, init=data_MAGIC)ggplot(data_MAGIC) + geom_point(aes(VIM, CDH1, colour=ZEB1)) + scale_colour_viridis(option="B")We can look at the entire smoothed matrix with genes='all_genes',passing the original result to the argument init to avoid recomputingintermediate steps. Note that this matrix may be large and could take upa lot ofmemory.
data_MAGIC <- magic(magic_testdata, genes="all_genes", t=6, init=data_MAGIC)as.data.frame(data_MAGIC)[1:5, 1:10]#> A1BG-AS1 AAMDC AAMP AARSD1 ABCA12 ABCG2#> 6564 0.02565716 0.06303703 0.1726791 0.01559474 0.03114244 0.01423031#> 3835 0.02535551 0.06286382 0.1678011 0.01547390 0.03017628 0.01428737#> 6318 0.02619089 0.06298015 0.1744098 0.01514747 0.03145176 0.01477152#> 3284 0.02517645 0.06254417 0.1684572 0.01559623 0.03015758 0.01414733#> 1171 0.02651602 0.06289360 0.1729842 0.01514780 0.03162480 0.01480426#> ABHD13 AC007773.2 AC011998.4 AC013470.6#> 6564 0.07100262 0.001129400 0.001880153 0.003215547#> 3835 0.06989726 0.001086716 0.001847604 0.002833342#> 6318 0.07165035 0.001203505 0.002044504 0.003550067#> 3284 0.07066602 0.001039065 0.001723499 0.002822357#> 1171 0.07094679 0.001236082 0.002133401 0.003450875Visualizing MAGIC values on PCA
We can visualize the results of MAGIC on PCA as follows.
data_MAGIC_PCA <- as.data.frame(prcomp(data_MAGIC)$x)ggplot(data_MAGIC_PCA) + geom_point(aes(x=PC1, y=PC2, color=data_MAGIC$result$VIM)) + scale_color_viridis(option="B") + labs(color="VIM")Visualizing MAGIC values on PHATE
We can visualize the results of MAGIC on PHATE as follows. We set tand k manually, because this toy dataset is really too small to makesense with PHATE; however, the default values work well for single-cellgenomic data.
data_PHATE <- phate(magic_testdata, k=3, t=15)#> Argument k is deprecated. Using knn instead.ggplot(data_PHATE) + geom_point(aes(x=PHATE1, y=PHATE2, color=data_MAGIC$result$VIM)) + scale_color_viridis(option="B") + labs(color="VIM")Issues
FAQ
- Should genes (features) by rows or columns?
To be consistent with common functions such as PCA(stats::prcomp) and t-SNE (Rtsne::Rtsne), we require that cells(observations) be rows and genes (features) be columns of your inputdata.
- I have installed MAGIC in Python, but Rmagic says it is notinstalled!
Check your reticulate::py_discover_config("magic") and compare it tothe version of Python in which you installed PHATE (run which pythonand which pip in a terminal.) Chances are reticulate can’t find theright version of Python; you can fix this by adding the following lineto your ~/.Renviron:
PATH=/path/to/my/python
You can read more about Renviron athttps://cran.r-project.org/package=startup/vignettes/startup-intro.html.
Help
Please let us know of any issues at the GitHubrepository. If youhave any questions or require assistance using MAGIC, please read thedocumentation by running help(Rmagic::magic) or contact us athttps://krishnaswamylab.org/get-help.
