What is conos? Conos is an R package to wire together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. It focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes.
How does it work? Conos applies one of many error-prone methods to align each pair of samples in a collection, establishing weighted inter-sample cell-to-cell links. The resulting joint graph can then be analyzed to identify subpopulations across different samples. Cells of the same type will tend to map to each other across many such pairwise comparisons, forming cliques that can be recognized as clusters (graph communities).
Conos processing can be divided into three phases:
* Phase 1: Filtering and normalization Each individual dataset in the sample panel is filtered and normalized using standard packages for single-dataset processing: either
Seurat. Specifically, Conos relies on these methods to perform cell filtering, library size normalization, identification of overdispersed genes and, in the case of pagoda2, variance normalization. (Conos is robust to variations in the normalization procedures, but it is recommended that all of the datasets be processed uniformly.)
* Phase 2: Identify multiple plausible inter-sample mappings Conos performs pairwise comparisons of the datasets in the panel to establish an initial error-prone mapping between cells of different datasets.
* Phase 3: Joint graph construction These inter-sample edges from Phase 2 are then combined with lower-weight intra-sample edges during the joint graph construction. The joint graph is then used for downstream analysis, including community detection and label propagation. For a comprehensive description of the algorithm, please refer to our publication.
What does it produce? In essence, conos will take a large, potentially heterogeneous panel of samples and will produce clustering grouping similar cell subpopulations together in a way that will be robust to inter-sample variation:
What are the advantages over existing alignment methods? Conos is robust to heterogeneity of samples within a collection, as well as noise. The ability to resolve finer subpopulation structure improves as the size of the panel increases.
Given a list of individual processed samples (
pl), conos processing can be as simple as this:
# Construct Conos object, where pl is a list of pagoda2 objects con <- Conos$new(pl) # Build joint graph con$buildGraph() # Find communities con$findCommunities() # Generate embedding con$embedGraph() # Plot joint graph con$plotGraph() # Plot panel with joint clustering results con$plotPanel()
To see more documentation on the class
Please see the following tutorials for detailed examples of how to use conos:
Note that for integration with Scanpy, users need to save conos files to disk from an R session, and then load these files into Python.
Load conos files into Scanpy: * Jupyter Notebook
First of all, in order to obtain an RNA velocity plot from a
Conos object you have to use the dropEst pipeline to align and annotate your single-cell RNA-seq measurements. You can see this tutorial and this shell script to see how it can be done. In this example we specifically assume that when running dropEst you have used the -V option to get estimates of unspliced/spliced counts from the dropEst directly. Secondly, you need the velocyto.R package for the actual velocity estimation and visualisation.
After running dropEst you should have 2 files for each of the samples:
sample.rds (matrix of counts)
sample.matrices.rds (3 matrices of exons, introns and spanning reads)
.matrices.rds files are the velocity files. Load them into R in a list (same order as you give to conos). Load, preprocess and integrate with conos the count matrices (
.rds) as you normally would. Before running the velocity, you must at least create an embedding and run the leiden clustering. Finally, you can estimate the velocity as follows:
### Assuming con is your Conos object and cms.list is the list of your velocity files ### library(velocyto.R) # Preprocess the velocity files to match the Conos object vi <- velocityInfoConos(cms.list = cms.list, con = con, n.odgenes = 2e3, verbose = TRUE) # Estimate RNA velocity vel.info <- vi %$% gene.relative.velocity.estimates(emat, nmat, cell.dist = cell.dist, deltaT = 1, kCells = 25, fit.quantile = 0.05, n.cores = 4) # Visualise the velocity on your Conos embedding # Takes a very long time! # Assign to a variable to speed up subsequent recalculations cc.velo <- show.velocity.on.embedding.cor(vi$emb, vel.info, n = 200, scale = 'sqrt', cell.colors = ac(vi$cell.colors, alpha = 0.5), cex = 0.8, grid.n = 50, cell.border.alpha = 0, arrow.scale = 3, arrow.lwd = 0.6, n.cores = 4, xlab = "UMAP1", ylab = "UMAP2") # Use cc=cc.velo$cc when running again (skips the most time consuming delta projections step) show.velocity.on.embedding.cor(vi$emb, vel.info, cc = cc.velo$cc, n = 200, scale = 'sqrt', cell.colors = ac(vi$cell.colors, alpha = 0.5), cex = 0.8, arrow.scale = 15, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 2, do.par = F, cell.border.alpha = 0.1, n.cores = 4, xlab = "UMAP1", ylab = "UMAP2")
To install the latest version of
install.packages('devtools') devtools::install_github('kharchenkolab/conos', build_vignettes = TRUE)
Please note that the package
conos depends on data in a data package (
conosPanel) that is available through a
drat repository on GitHub. To use the
conos package, you will need to install
conosPanel. There are two equally valid options to install this package:
A) Users could install
conosPanel by adding the
drat archive to the list of repositories your system will query when adding and updating R packages. Once you do this, you can install
install.packages(), using the command:
library(drat) addRepo("kharchenkolab") install.packages("conosPanel")
The following command is also a valid approach:
install.packages('conosPanel', repos='https://kharchenkolab.github.io/drat/', type='source')
Please see the drat documentation for more comprehensive explanations and vignettes.
B) Another way to install the package
conosPanel is to use
Note: If you are using pagoda2, you should also install the auxiliary package
install.packages('p2data', repos='https://kharchenkolab.github.io/drat/', type='source')
The dependencies are inherited from pagoda2:
To install system dependencies using
apt-get, use the following:
sudo apt-get update sudo apt-get -y install libcurl4-openssl-dev libssl-dev
For Red Hat distributions using
yum, use the following command:
yum install openssl-devel libcurl-devel
Using the Mac OS package manager Homebrew, try the following command:
brew install openssl curl-openssl
(You may need to run
brew uninstall curl in order for
brew install curl-openssl to be successful.)
As of version 1.3.1,
conos should successfully install on Mac OS. However, if there are issues, please refer to the following wiki page for further instructions on installing
conos with Mac OS: Installing conos for Mac OS
If your system configuration is making it difficult to install
conos natively, an alternative way to get
conos running is through a docker container.
The docker distribution has the latest version and also includes the pagoda2 package. To start a docker container, first install docker on your platform and then start the
pagoda2 container with the following command in the shell:
docker run -p 8787:8787 -e PASSWORD=pass pkharchenkolab/conos:latest
The first time you run this command, it will download several large images so make sure that you have fast internet access setup. You can then point your browser to http://localhost:8787/ to get an Rstudio environment with
conos installed (please log in using credentials username=
pass). Explore the docker --mount option to allow access of the docker image to your local files.
Note: If you already downloaded the docker image and want to update it, please pull the latest image with:
docker pull pkharchenkolab/conos:latest
If you want to build image by your own, download the Dockerfile (available in this repo under
/dockers) and run to following command to build it:
docker build -t conos .
This will create a "conos" docker image on your system (please be patient, as the build could take approximately 30-50 minutes to finish). You can then run it using the following command:
docker run -d -p 8787:8787 -e PASSWORD=pass --name conos -it conos
If you find this software useful for your research, please cite the corresponding paper:
Barkas N., Petukhov V., Nikolaeva D., Lozinsky Y., Demharter S., Khodosevich K., & Kharchenko P.V. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nature Methods, (2019). doi:10.1038/s41592-019-0466-z
The R package can be cited as:
Viktor Petukhov, Nikolas Barkas, Peter Kharchenko, and Evan Biederstedt (2021). conos: Clustering on Network of Samples. R package version 1.4.0.
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