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See for latest version. See too-many-peaks for more information about scATAC-seq usage.

See the publication (and please cite!) for more information about the algorithm.



too-many-cells is a suite of tools, algorithms, and visualizations focusing on the relationships between cell clades. This includes new ways of clustering, plotting, choosing differential expression comparisons, and more! While too-many-cells was intended for single cell RNA-seq, any abundance data in any domain can be used. Rather than opt for a unique positioning of each cell using dimensionality reduction approaches like t-SNE, UMAP, and LSA, too-many-cells recursively divides cells into clusters and relates clusters rather than individual cells. In fact, by recursively dividing until further dividing would be considered noise or random partitioning, we can eliminate noisy relationships at the fine-grain level. The resulting binary tree serves as a basis for a different perspective of single cells, using our birch-beer visualization and tree measures to describe simultaneously large and small populations, without additional parameters or runs. See below for a full list of features.

New features for v2.2.0.0

  • --no-edger replaced with --edger as the default is now Kruskal-Wallis.
  • Can now use backgrounds for motifs.
  • Can specify motif for genome analysis (i.e. from HOMER).
  • Temporary directories are now variables to correctly specify location.
  • Added q-values for differential.
  • Updated documentation for too-many-peaks.

New features for v2.0.0.0

  • Support for scATAC-seq for chromatin state relationships with too-many-peaks !
  • Find enchriched regions as peaks for scATAC-seq with peaks.
  • Find motifs from differential chromatin state using motifs.
  • Linear relationships across the tree as pseudotime with paths.
  • Classify single-cell data from bulk with classify.
  • New dimensionality reductions with --lsa.
  • Output transformed matrix with matrix-output.
  • Bypass labels.csv with -Z quick labels.
  • MADs-from-median-based thresholds for multi-gene overlay plots
  • Multiple normalization application
  • And much more!

New features since initial launch

  • Now packaged for the functional package manager nix (Linux only)! No more dependency shuffling or root for Docker needed!
  • A new R wrapper was written to quickly get data to and from too-many-cells from R. Check it out here!
  • Now works with Cellranger 3.0 matrices in addition to Cellranger 2.0
  • Can prune (make into leaves) specified nodes with --custom-cut.
  • Can analyze sets of features averaged together (e.g. gene sets). Breaks API, so update your =–draw-leaf “DrawItem (DrawContinuous "Cd4")”= argument to =–draw-leaf “DrawItem (DrawContinuous ["Cd4"])”= (notice the list notation).
  • Outputs values from differential entry point plots (from --features), and can aggregate features by average.


We provide multiple ways to install too-many-cells. We recommend installing with nix . nix will provide all dependencies in the build, supports Linux, and should be reproducible, so try that first. We also have docker images and a Dockerfile to use in any system in case you have a custom build (for instance, a non-standard R installation) or difficulty installing. macOS and Windows users: too-many-cells was built and tested on Linux, so we highly recommend using the docker image (which is a completely isolated environment which requires no compiling or installation, other than docker itself) as there may be difficulties in installing the dependencies.


too-many-cells can be installed using the functional package manager nix . While you will need sudo to install, no sudo is required after the correct setup. First, install nix following the instructions on the website. Then, with an unset LD_LIBRARY_PATH,

git clone
cd too-many-cells
nix-env -f default.nix -i too-many-cells

Stack (unsupported in too-many-cells >= v2.0.0.0, use nix)


You may require the following dependencies to build and run (from Ubuntu 14.04, use the appropriate packages from your distribution of choice):

  • build-essential
  • libgmp-dev
  • libblas-dev
  • liblapack-dev
  • libgsl-dev
  • libgtk2.0-dev
  • libcairo2-dev
  • libpango1.0-dev
  • graphviz
  • r-base
  • r-base-dev

To install them, in Ubuntu:

sudo apt install build-essential libgmp-dev libblas-dev liblapack-dev libgsl-dev libgtk2.0-dev libcairo2-dev libpango1.0-dev graphviz r-base r-base-dev

too-many-cells also uses the following packages from R:

  • cowplot
  • ggplot2
  • edgeR
  • jsonlite

To install them in R,

install.packages(c("ggplot2", "cowplot", "jsonlite"))

Install stack

See for more details.

curl -sSL | sh
stack setup

Install too-many-cells


Probably the easiest method if you don’t want to mess with dependencies (outside of the ones above).

git clone
cd too-many-cells
stack install


We only require stack (or cabal), you do not need to download any source code (but you might need the stack.yaml.old dependency versions), just run the following command to place too-many-cells < v2.0.0.0 in your ~/.local/bin/:

mv stack.yaml.preV2 stack.yaml
stack install too-many-cells

If you run into errors like Error: While constructing the build plan, the following exceptions were encountered:, then follow the advice. Usually you just need to follow the suggestion and add the dependencies to the specified file. For a quick yaml configuration, refer to


We recommend using docker on macOS. The following is written for too-many-cells < v2.0.0.0. If you must compile too-many-cells, you should get the above dependencies. For some dependencies, you can use brewer, then install too-many-cells (in the cloned folder, don’t forget to install the R dependencies above):

brew cask install xquartz
brew install glib cairo gtk gettext fontconfig freetype

brew tap brewsci/bio
brew tap brewsci/science
brew install r zeromq graphviz pkg-config gsl libffi gobject-introspection gtk+ gtk+3

# Needed so pkg-config and libraries can be found.
# For the second path, use the ouput of "brew info libffi".
export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:/usr/local/opt/libffi/lib/pkgconfig

# Tell gtk that it's quartz
stack install --flag gtk:have-quartz-gtk


Different computers have different setups, operating systems, and repositories. Do put the entire program in a container to bypass difficulties (with the other methods above), we user docker. So first, install docker.

To get too-many-cells (replace with any version needed):

docker pull gregoryschwartz/too-many-cells:

To run too-many-cells in a docker container:

sudo docker run -it --rm -v "/home/username:/home/username" gregoryschwartz/too-many-cells: -h

Now you can follow the tutorial below with the addition of the docker paths and commands. If you add yourself to the docker group, sudo is not needed. For instance:

docker run -it --rm -v "/home/username:/home/username" \
    gregoryschwartz/too-many-cells: make-tree \
    --matrix-path /home/username/path/to/input \
    --labels-file /home/username/path/to/labels.csv \
    --draw-collection "PieRing" \
    --output /home/username/path/to/out \
    > clusters.csv

Make sure to increase the memory that can be used by docker containers if you use macOS or Windows. Also, docker won’t be able to find your files by default. You need to mount the folders with -v in order to have docker read and write from and to the filesystem, respectively. Read the documentation about volumes for more information. You can simply mount your entire relevant path as in the above example to handle both input and output, or just mount your entire user directory as above. Specifically, -v "/home/username:/home/username"= for the whole directory or each individual =-v /path/to/matrix/on/host:/input_matrix with -m /input_matrix is what you want, where before the : is on the host filesystem while after the : is what the docker program sees. Then you can write the output in the same way: -v /path/to/output/on/host:/output will write the output to the folder before the :.

To build the too-many-cells image yourself if you want:

nix-build docker.nix
docker load < /nix/store/${NAME_OF_OUTPUT_IMAGE}.tar.gz


Using nix, I’m getting shared object not found errors.

Be sure to have LD_LIBRARY_PATH unset when running nix-env to make sure the linked libraries are in /nix/store.

I am getting errors like AesonException "Error in $.packages.cassava.constraints.flags... when running stack commands

Try upgrading stack with stack upgrade. The new installation will be in ~/.local/bin, so use that binary.

I use conda or custom ld library locations and I cannot install too-many-cells or run into weird R errors

stack and too-many-cells assume system libraries and programs. To solve this issue, first install the dependencies above at the system level, including system R. Then to every stack and too-many-cells command, prepend ~PATH=”$HOME/.local/bin:/usr/bin:$PATH”~ to all commands. For instance:

  • PATH="$HOME/.local/bin:/usr/bin:$PATH" stack install
  • PATH="$HOME/.local/bin:/usr/bin:$PATH" too-many-cells make-tree -h

If your shared libraries are abnormal and use from non-system locations, be sure to also have LD_LIBRARY_PATH=/usr/lib/:$LD_LIBRARY_PATH when installing (and / or the location of R libraries, such as /usr/local/lib/R/lib/).

I am still having issues with installation

Open an issue! While working on the issue, try out the docker for too-many-cells, it requires no installation at all (other than docker).

I am on macOS/Windows with docker and too-many-cells silently crashes.

Docker containers may run into this issue if the memory given to the containers is insufficient. Make sure to increase the memory that can be used by docker containers.

Included projects

This project is a collection of libraries and programs written specifically for too-many-cells:

Generate a tree for displaying a hierarchy of groups with colors, scaling, and more.
Find the modularity of a network.
Library for spectral clustering.
Hierarchical spectral clustering of a graph.
Finds out whether an entity comes from different distributions (statuses).


too-many-cells has several entry points depending on the desired analysis.

make-treeGenerate the tree from single cell data with various measurement outputs and visualize tree
interactiveInteractive visuzalization of the tree, very slow
differentialFind differentially expressed features between two nodes
diversityConduct diversity analyses of multiple cell populations
pathsThe binary tree equivalent of the so called “pseudotime”, or 1D dimensionality reduction

The main workflow is to first generate and plot the population tree using too-many-cells make-tree, then use the rest of the entry points as needed.

At any point, use -h to see the help of each entry point.

Also, check out tooManyCellsR for an R wrapper!


too-many-cells make-tree generates a binary tree using hierarchical spectral clustering. We start with all cells in a single node. Spectral clustering partitions the cells into two groups. We assess the clustering using Newman-Girvan modularity: if \(Q > 0\) then we recursively continue with hierarchical spectral clustering. If not, then there is only a single community and we do not partition – the resulting node is a leaf and is considered the finest-grain cluster.

The most important argument is the –prior argument. Making the tree may take some time, so if the tree was already generated and other analysis or visualizations need to be run on the tree, point the --prior argument to the output folder from a previous run of too-many-cells. If you do not use --prior, the entire tree will be recalculated even if you just wanted to change the visualization!

The main input is the --matrix-path argument. When a directory is supplied, too-many-cells interprets the folder to have matrix.mtx, genes.tsv, and barcodes.tsv files (cellranger outputs, see cellranger for specifics). If a file is supplied instead of a directory, we assume a csv file containing feature row names and cell column names. This argument can be called multiple times to combine multiple single cell matrices: --matrix-path input1 --matrix-path input2.

The second most important argument is --labels-file. Supply with a csv with a format and header of “item,label” to provide colorings and statistics of the relationships between labels. Here the “item” column contains the name of each cell (barcode) and the label is any property of the cell (the tissue of origin, hour in a time course, celltype, etc.). You can also now use -Z as a list for each matching -m in order to manually give the entire matrix that label (useful for situations like -m ./t-all -Z T-ALL -m ./control -Z Control). To get the newly generated labels with =-Z into a labels.csv file, specify --labels-output and the labels.csv will be in the output folder.

To see the full list of options, use too-many-cells -h and -h for each entry point (i.e. too-many-cells make-tree -h).


too-many-cells make-tree generates several files in the output folder. Below is a short description of each file.

clumpiness.csvWhen labels are provided, uses the clumpiness measure to determine the level of aggregation between each label within the tree.
clumpiness.pdfWhen labels are provided, a figure of the clumpiness between labels.
cluster_diversity.csvWhen labels are provided, the diversity, or “effective number of labels”, of each cluster.
cluster_info.csvVarious bits of information for each cluster and the path leading up to each cluster, from that cluster to the root. For instance, the size column has cluster_size/parent_size/parent_parent_size/.../root_size
cluster_list.jsonThe json file containing a list of clusterings.
cluster_tree.jsonThe json file containing the output tree in a recursive format.
dendrogram.svgThe visualization of the tree. There are many possible options for this visualization included. Can rename to choose between PNG, PS, PDF, and SVG using --dendrogram-output.
graph.dotA dot file of the tree, with less information than the tree in cluster_results.json.
node_info.csvVarious information of each node in the tree.
projection.pdfWhen --projection is supplied with a file of the format “barcode,x,y”, provides a plot of each cell at the specified x and y coordinates (for instance, when looking at t-SNE plots with the same labelings as the dendrogram here).

Outline with options

The basic outline of the default matrix pre-processing pipeline with some relevant options is as follows (there are many additional options including cell whitelists that can be seen using too-many-cells make-tree -h):

  1. Read matrix.
  2. Optionally remove cells with less than X counts (--filter-thresholds).
  3. Optionally remove features with less than X count (--filter-thresholds).
  4. Term frequency-inverse document frequency normalization (--normalization).
  5. Optionally use dimensionality reduction (--lsa).
  6. Finish.



We start with our input matrix. Here,

ls ./input
barcodes.tsv  genes.tsv  matrix.mtx

Note that the input can be a directory (with the cellranger matrix format above) or a file (a csv file). You can also point to a cellranger >= 3.0 folder which has matrix.mtx.gz, features.tsv.gz, and barcodes.tsv.gz files instead. You don’t need to use scRNA-seq data! You can use any data that has observations (cells) and features (genes), as long as you agree that the observations are related by their feature abundances. If you do upstream batch effect correction, LSA, normalization, or anything else, be sure to use --normalization NoneNorm (and --shift-positive for LSA) to avoid wrong filters and scalings! If using dimensionality reduction such as PCA and t-SNE, we highly recommend generating your own similarity matrix for use with our cluster-tree program and plot with birch-beer, as we emphasize a feature matrix in too-many-cells and dimensionality reduction algorithms transform counts (our input which works with cosine similarity) into more nebulous information (which may not work with cosine similarity). cluster-tree, however, can be used with adjacency and similarity matrices. As for formats, the matrix market format contains three files like so:

The matrix.mtx file is in matrix market format.

%%MatrixMarket matrix coordinate integer general
23433 1981 4255069
4 1 1
5 1 1
11 1 2
23 1 2
25 1 2
40 1 2
48 1 1

The genes.tsv file (or features.tsv.gz) contains the features of each cell and corresponds to the rows of matrix.mtx. Here, both columns were the same gene symbols, but you can have Ensembl as the first column and gene symbol as the second, etc. The columns and column orders don’t matter, but make sure all matrices have the same format and specify the symbols you want to use (for overlaying gene expression, differential expression, etc.) with --feature-column COLUMN. So to use the second column for gene expression, you would use --feature-column 2.

Xkr4	Xkr4
Rp1	Rp1
Sox17	Sox17
Mrpl15	Mrpl15
Lypla1	Lypla1
Tcea1	Tcea1
Rgs20	Rgs20
Atp6v1h	Atp6v1h
Oprk1	Oprk1
Npbwr1	Npbwr1

The barcodes.tsv file contains the ids of each cell or observation and corresponds to the columns of matrix.mtx.


For a csv file, the format is dense (observation columns (cells), feature rows (genes)):


We also know where each cell came from, so we mark that down as well in a labels.csv file.


This can be easily accomplished with sed:

cat barcodes.tsv | sed "s/-1/-1,Marrow/" | s/-2/etc... > labels.csv

For cellranger, note that the -1, -2, etc. postfixes denote the first, second, etc. label in the aggregation csv file used as input for cellranger aggr.

Default run

We can now run the too-many-cells algorithm on our data. The resulting cells with assigned clusters will be printed to stdout (don’t forget to use --normalization NoneNorm on preprocessed data, as stated here). While older versions had default filter thresholds for (MINCELL, MINFEATURE) counts, since v2.0.0.0 the default is now no filtering to account for multiple assay types.

too-many-cells make-tree \
    --matrix-path input \
    --labels-file labels.csv \
    --filter-thresholds "(250, 1)" \
    --draw-collection "PieRing" \
    --output out \
    > clusters.csv


Pruning tree

Large cell populations can result in a very large tree. What if we only want to see larger subpopulations rather than the large (inner nodes) and small (leaves)? We can use the --min-size 100 argument to set the minimum size of a leaf to 100 in this case. Alternatively, we can specify --smart-cutoff 4 in addition to --min-size 1 to set the minimum size of a node to \(4 * \text{median absolute deviation (MAD)}\) of the nodes in the original tree. Varying the number of MADs varies the number of leaves in the tree. --smart-cutoff should be used in addition to --min-size, --max-proportion, --min-distance, or --min-distance-search to decide which cutoff variable to use. The value supplied to the cutoff variable is ignored when --smart-cutoff is specified. We’ll prune the tree for better visibility in this document.

Note: the pruning arguments change the tree file, not just the plot, so be sure to output into a different directory.

Also, we do not need to recalculate the entire tree! We can just supply the previous results using --prior (we can also remove --matrix-path with --prior to speed things up, but miss out on some features if needed):

too-many-cells make-tree \
    --prior out \
    --labels-file labels.csv \
    --smart-cutoff 4 \
    --min-size 1 \
    --draw-collection "PieRing" \
    --output out_pruned \
    > clusters_pruned.csv


Pie charts

What if we want pie charts instead of showing each individual cell (the default)?

too-many-cells make-tree \
    --prior out \
    --labels-file labels.csv \
    --smart-cutoff 4 \
    --min-size 1 \
    --draw-collection "PieChart" \
    --output out_pruned \
    > clusters_pruned.csv


Node numbering

Now that we see the relationships between clusters and nodes in the dendrogram, how can we go back to the data – which nodes represent which node IDs in the data?

too-many-cells make-tree \
    --prior out \
    --labels-file labels.csv \
    --smart-cutoff 4 \
    --min-size 1 \
    --draw-collection "PieChart" \
    --draw-node-number \
    --output out_pruned \
    > clusters_pruned.csv


Branch width

We can also change the width of the nodes and branches, for instance if we want thinner branches:

too-many-cells make-tree \
    --prior out \
    --labels-file labels.csv \
    --smart-cutoff 4 \
    --min-size 1 \
    --draw-collection "PieChart" \
    --draw-max-node-size 40 \
    --output out_pruned \
    > clusters_pruned.csv


No scaling

We can remove all scaling for a normal tree and still control the branch widths:

too-many-cells make-tree \
    --prior out \
    --labels-file labels.csv \
    --smart-cutoff 4 \
    --min-size 1 \
    --draw-collection "PieChart" \
    --draw-max-node-size 40 \
    --draw-no-scale-nodes \
    --output out_pruned \
    > clusters_pruned.csv


How strong is each split? We can tell by drawing the modularity of the children on top of each node:

too-many-cells make-tree \
    --prior out \
    --labels-file labels.csv \
    --smart-cutoff 4 \
    --min-size 1 \
    --draw-collection "PieChart" \
    --draw-mark "MarkModularity" \
    --output out_pruned \
    > clusters_pruned.csv


Gene expression

What if we want to draw the gene expression onto the tree in another folder (requires --matrix-path, may take some time depending on matrix size. Defaults to all black if the feature name is not present in the matrix, so check the first column of the feature file)? Note: the feature names are from the genes.tsv or features.tsv.gz file. Usually, cellranger has Ensembl identifiers as the first column and gene symbol as the second column, so if you want to specify gene symbol, use --feature-column 2 (1 is default).

too-many-cells make-tree \
    --prior out \
    --matrix-path input \
    --labels-file labels.csv \
    --filter-thresholds "(250, 1)" \
    --smart-cutoff 4 \
    --min-size 1 \
    --feature-column 2 \
    --draw-leaf "DrawItem (DrawContinuous [\"Cd4\"])" \
    --output out_gene_expression \
    > clusters_pruned.csv


Notice that Cd4 is within a list ([]), so multiple features can be listed and the average of those values for each cell will be used. While this representation shows the expression of Cd4 in each cell and blends those levels together, due to the sparsity of single cell data these cells and their respective subtrees may be hard to see without additional processing. Let’s scale the saturation to more clearly see sections of the tree with our desired expression (when choosing other high and low colors with --draw-colors, scaling the saturation will only affect non-grayscale colors).

too-many-cells make-tree \
    --prior out \
    --matrix-path input \
    --labels-file labels.csv \
    --filter-thresholds "(250, 1)" \
    --smart-cutoff 4 \
    --min-size 1 \
    --feature-column 2 \
    --draw-leaf "DrawItem (DrawContinuous [\"Cd4\"])" \
    --draw-scale-saturation 10
    --output out_gene_expression \
    > clusters_pruned.csv


There, much better! Now it’s clearly enriched in the subtree containing the thymus, where we would expect many T cells to be. While this tree makes the expression a bit more visible, there is another tactic we can use. Instead of the continuous color spectrum of expression values, we can have a binary “high” and “low” expression. Here, we’ll continue to have the red and gray colors represent high and low expressions respectively using the --draw-colors argument. Note that this binary expression technique can be used for multiple features, hence it’s a list of features with cutoffs (Exact for specified cutoffs or MadMedian for how many MADs from the median) so you can be high in a gene and low in another gene, etc. for all possible combinations.

too-many-cells make-tree \
    --prior out \
    --matrix-path input \
    --labels-file labels.csv \
    --filter-thresholds "(250, 1)" \
    --smart-cutoff 4 \
    --min-size 1 \
    --feature-column 2 \
    --draw-leaf "DrawItem (DrawThresholdContinuous [(\"Cd4\", Exact 0), (\"Cd8a\", Exact 0)])" \
    --draw-colors "[\"#e41a1c\", \"#377eb8\", \"#4daf4a\", \"#eaeaea\"]" \
    --draw-scale-saturation 10 \
    --output out_gene_expression \
    > clusters_pruned.csv


Now we can see the expression of both Cd4 and Cd8a at the same time!


We can also see an overview of the diversity of cell labels within each subtree and leaves.

too-many-cells make-tree \
    --prior out \
    --matrix-path input \
    --filter-thresholds "(250, 1)" \
    --labels-file labels.csv \
    --smart-cutoff 4 \
    --min-size 1 \
    --draw-leaf "DrawItem DrawDiversity" \
    --output out_diversity \
    > clusters_pruned.csv


Here, the deeper the red, the more diverse (a larger “effective number of cell states”) the cell labels in that group are. Note that the inner nodes are colored relative to themselves, while the leaves are colored relative to all leaves, so there are two different scales.


The interactive entry point has a basic GUI interface for quick plotting with a few features. We recommend limited use of this feature, however, as it can be quite slow at this stage, has fewer customizations, and requires specific dependencies.

too-many-cells interactive \
    --prior out \
    --labels-file labels.csv


A main use of single cell clustering is to find differential genes between multiple groups of cells. The differential aids in this endeavor by allowing comparisons with edgeR. Let’s find the differential genes between the liver group and all other cells. Consider our pruned tree from earlier:


We can see the id of each group with --draw-node-number.


We need to define two groups to compare. Well, it looks like node 98 defines the liver cluster. Then, since we don’t want 98 to be in the other group, we say that all other cells are within nodes 89 and 1. As a result, we end up with a tuple containing two lists: ([89, 1], [98]). Then our differential genes for (liver / others) can be found with differential (sent to stdout):

too-many-cells differential \
    --matrix-path input \
    --filter-thresholds "(250, 1)" \
    -n "([89, 1], [98])" \
    > differential.csv

If we wanted to make the same comparison, but compare the liver subtree with liver cells from all other subtrees, we can use the --labels argument:

too-many-cells differential \
    --matrix-path input \
    --labels-file labels.csv \
    --filter-thresholds "(250, 1)" \
    -n "([89, 1], [98])" \
    --labels "([\"Liver\"], [\"Liver\"])" \
    > differential_liver.csv

We can also look at the distribution of abundance for individual genes using the --features and --plot-output arguments.

Furthermore, we can compare each node to all other cells by specifying no nodes at all. The output file will contain the top --top-n genes for each node. We recommend using multiple OS threads here to speed up the process using +RTS -N${NUMOSTHREADS} (no number to use all cores). The following example will compare all nodes to all other cells using 8 OS threads:

too-many-cells differential \
    --matrix-path input \
    --filter-thresholds "(250, 1)" \
    -n "([], [])" \
    --normalization "UQNorm" \
    +RTS -N8


Diversity is the measure of the “effective number of entities within a system”, originating from ecology (See Jost: Entropy and Diversity). Here, each cell is an organism and each cell label or cluster is a species, depending on the question. In ecology, the diversity index measures the effective number of species within a population such that the minimum is a diversity of 1 for a single dominant species up to maximum of the total number of species (evenly abundant). If our species is a cluster, then here the diversity is the effective number of cell states within a population (for labels, make-tree generates these results automatically in “diversity” columns). Say we have two populations and we generated the trees using make-tree into two different output folders, out1 and out2. We can find the diversity of each population using the diversity entry point.

too-many-cells diversity\
    --priors out1 \
    --priors out2 \
    -o out_diversity_stats

We can then find a simple plot of diversity in diversity_output. In addition, we also provide rarefaction curves for comparing the number of different cell states at each subsampling useful for comparing the number of cell states where the population sizes differ.


“Pseudotime” refers to the one dimensional relationship between cells, useful for looking at the ordering of cell states or labels. The implementation of pseudotime in a too-many-cells point-of-view is by finding the distance between all cells and the cells found in the longest path from the root in the tree. Then each cell has a distance from the “start” and thus we plot those distances.

too-many-cells paths\
    --prior out \
    --labels-file labels.csv \
    --bandwidth 3 \
    -o out_paths

Working with scATAC-seq data using too-many-peaks

For more information, check out the too-many-peaks walkthrough.

scATAC-seq is a powerful technology for quantifying chromatin accessibility for individual cells. too-many-cells now supports scATAC-seq to generate cell clade relationships from chromatin state information through too-many-peaks. All of the previous analyses used with gene-product features now work with genomic regions in the form chrN:START-END, where N is the chromosome number, START is the start of the region and END is the end base of the region.

Matrices in this format can be read from either CSV or matrix-market as above but with the correctly formatted features, or you can load in directly from a fragments.tsv.gz file in Cellranger format (tab delimited with each row being chrN\tSTART\tEND\tBARCODE\tCOUNT) making sure that the filename contains the fragments ending, such as t-all_fragments.tsv.gz. For example:

too-many-cells make-tree\
    -m ./t-all_fragments.tsv.gz \
    -Z "T-ALL" \
    -m ./control_fragments.tsv.gz
    -Z "Control" \
    --filter-thresholds "(1000, 1)" \
    --bindwidth 5000 \
    --lsa 50 \
    --normalization NoneNorm \
    --blacklist-regions-file Anshul_Hg19UltraHighSignalArtifactRegions.bed.gz \
    --draw-node-number \
    --draw-mark "MarkModularity" \
    --fragments-output \
    --labels-output \
    -o out \
    > out_leaves.csv

Note: We use --lsa and --normalization NoneNorm for latent semantic analysis dimensionality reduction as there are many features in scATAC-seq, so we try to overcome a potential issue where all cells are considered outliers. To blacklist known biased regions in the genome, we can call --blacklist-regions-file. The --fragments-output and --labels-output go hand-in-hand with -Z in order to keep the renamed barcodes and labels (found in the output folder). too-many-cells will binarize the data by default unless --no-binarize is specified. Lastly, we choose a bindwidth using --binwidth to conform to a set of standard features across cells and samples.


With scATAC-seq, we want to identify enriched locations in the genome for each newly found subpopulation of cells. The peaks entrypoint can collect the appropriate fragments for quantification and visualization of peaks.

too-many-cells peaks \
    -f ./out/fragments.tsv.gz \
    --prior ./out \
    --genome human.hg19.genome \
    --bedgraph \
    --labels-file ./out/labels.csv \
    --all-nodes \
    --peak-node "1" \
    --peak-node "5" \
    --peak-node-labels "(1, [\"Control\"])" \
    --peak-node-labels "(5, [\"T-ALL\"])" \
    -o out_peaks \
    +RTS -N6

Here, we will have our peaks in the specified output folder, along with many other files and folder:

out_peaks/cluster_fragmentsfragments.tsv.gz files for each node.
out_peaks/cluster_bedgraphsBedgraphs and bigwigs if specified using --bedgraph for track visualization uses.
out_peaks/cluster_peaks/union.bdgMerged peaks across all requested nodes in bedgraph format.
out_peaks/cluster_peaks/union_fragments.tsv.gzMerged peaks across all requested nodes in fragments.tsv.gz format.
out_peaks/cluster_peaks/Folder containing merged peaks across nodes and peaks for each individual node in each folder.

--bedgraph enabled the cluster_bedgraphs folder, while --all-nodes specified to find peaks for all nodes, not just the leaves. However, when paired with --peak-node, we just look at the peaks for each node in the list (but --all-nodes is still required if looking at non-leaf nodes as well). Without --peak-node, this command would have found peaks for every node. Furthermore, --peak-node-labels allows the filtration based on the label of cells in of the requested node. --genome tells the peak finding program where the genome file is (containing the effective genome sizes of chromosomes in tab-delimited format of chrN\tSIZE used in the MACS2 program). Here, the -f fragments.tsv.gz and labels.csv was from the previous scATAC-seq section, where we automatically generated the correctly renamed barcodes and labels. Lastly, +RTS -N6 tells too-many-cells to use six cores for the calculation. These output files, especially the merged peak files, can be used for differential accessibility analysis as in scRNA-seq. This entrypoint is highly customizable, down to the exact command used for peak calling, so check out too-many-cells peaks -h for more information.


After differential accessibility using peaks, the result can be used to find motifs enriched in each node.

too-many-cells motifs \
    --diff-file ./diff_out.csv \
    --motif-genome hg19.fa \
    --top-n 1000 \
    --motif-command "homer/homer-4.9/bin/ %s fasta %s" \
    -o motifs

In this example, we use the output from a differential expression analysis using too-many-cells differential from our merged peaks. Using a complete genome file used by our motif program of choice (here HOMER, but defaults to MEME) with --motif-genome, we want to provide the motif program with the top 1000 most differential peaks using --top-n. Lastly, while the default uses MEME, we find HOMER to be much faster. The prior command shows the use of another program to find the motifs, making sure the %s for input and output are in the right locations (check too-many-cells motifs -h).


To identify potential cell type candidates from sorted bulk data, too-many-cells classify uses cosine-similarity to provide scores for each bulk population. For example, we have a scATAC-seq experiment in ./mat. We also have known bulk ATAC-seq peak data of B cells in bedgraph files. We can score each cell with:

too-many-cells classify \
    --reference-file ./proB.bdg \
    --reference-file ./preB.bdg \
    --reference-file ./memoryB.bdg \
    --reference-file ./plasmaB.bdg \
    -m ./mat \
    --normalization "NoneNorm" \
    --blacklist-regions-file "mm9-blacklist.bed.gz" \
    > labels.csv

--reference-file is a list of bedgraphs here for each population. You can also specify a single reference file as an input matrix with each barcode being the label for the population, as bulk just has one sample. To use a single matrix, use --single-reference-matrix in addition to --reference-file to specify the file as a single reference matrix. The output will be identical to a normal too-many-cells labels.csv file, but with an additional column score which provides the value of the highest cosine-similarity label.


A simple entrypoint to output the transformed matrix too-many-cells uses before clustering. Saves to --mat-output.

Advanced documentation

Each entry point has its own documentation accessible with -h, such as too-many-cells make-tree -h:

too-many-cells -h
too-many-cells, Gregory W. Schwartz. Clusters and analyzes single cell data.

Usage: too-many-cells (make-tree | interactive | differential | diversity |
                      paths | classify | peaks | motifs | matrix-output)

Available options:
  -h,--help                Show this help text

Available commands:


Check out an instructional example of using too-many-cells here when finished looking at the brief feature overview.


Cluster single cells and analyze cell clade relationships with colorful visualizations.







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