Pipeline for initial analysis of droplet-based single-cell RNA-seq data
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dropEst - Pipeline

Pipeline for estimating molecular count matrices for droplet-based single-cell RNA-seq measurements. Implements methods, described in this paper. To reproduce results from the paper see this repository.


[0.8.3] - 2018-05-17

  • Fixed multiple bugs
  • Barcodes for 10x chromium v2

See CHANGELOG.md for the full list.

Table of contents

General processing steps

  1. dropTag: extraction of cell barcodes and UMIs from the library. Result: demultiplexed .fastq.gz files, which should be aligned to the reference.
  2. Alignment of the demultiplexed files to reference genome. Result: .bam files with the alignment.
  3. dropEst: building count matrix and estimation of some statistics, necessary for quality control. Result: .rds file with the count matrix and statistics. Optionally: count matrix in MatrixMarket format.
  4. dropReport - Generating report on library quality.
  5. dropEstR - UMI count corrections, cell quality classification


System requirements

  • Boost >= 1.54
  • BamTools library >= 2.5.0
    • Note that some linux distributions have separate packages for the library and the executable, i.e. on Ubuntu you need libbamtools-dev, but not bamtools.
    • or you can build it locally and then specify the location of the build when running cmake (e.g. cmake -D BAMTOOLS_ROOT=/home/username/bamtools .)
  • Zlib (was tested on 1.2.11 version)
  • Bzip2 (was tested on 1.0.5 version)
  • R >= 3.2.2 with packages:
    • Rcpp
    • RcppEigen
    • RInside
    • Matrix
  • Compiler with c++11 support (was tested with gcc >= 4.8.5 and CLang 3.9.1)
  • CMake >= 3.0


Install R packages:

install.packages(c("Rcpp","RcppEigen", "RInside", "Matrix"))

Clone this repository:

git clone https://github.com/hms-dbmi/dropEst.git


mkdir dropEst/build
cd dropEst/build
cmake .. && make

Manual installation of dependencies

Here is the instruction on how to install specific versions of libraries to the local folder (i.e. ~/local/). Let's store this directory in LOCAL_LIBS variable:

export LOCAL_LIBS=$HOME/local/

To create this directory use:


To add installed libraries to PATH use:

export PATH=$LOCAL_LIBS/bin:$LOCAL_LIBS/usr/local/bin/:$PATH


Download version 3.12:

wget https://cmake.org/files/v3.12/cmake-3.12.0-rc1.tar.gz
tar xvf cmake-3.12.0-rc1.tar.gz
cd cmake-3.12.0-rc1

Build and install:

./bootstrap --prefix=$LOCAL_LIBS
make install

For the detailed instruction see instruction page.


Download version 1.2.11:

wget https://zlib.net/zlib-1.2.11.tar.gz
tar xvf zlib-1.2.11.tar.gz
cd zlib-1.2.11

Build and install:

./configure --prefix=$LOCAL_LIBS
make install


Clone repository, version 2.5.0:

git clone https://github.com/pezmaster31/bamtools.git
cd bamtools
git reset --hard 94f072

Build and install:

mkdir build && cd build
cmake ../
make install DESTDIR=$LOCAL_LIBS

For the detailed instruction see instruction page.


Download version 1.0.6:

wget http://www.bzip.org/1.0.6/bzip2-1.0.6.tar.gz
tar xvf bzip2-1.0.6.tar.gz
cd bzip2-1.0.6

Build and install:

make -f Makefile-libbz2_so
make install PREFIX=$LOCAL_LIBS
cp -a libbz2.so* $LOCAL_LIBS/lib/
ln -s $LOCAL_LIBS/lib/libbz2.so.1.0 $LOCAL_LIBS/lib/libbz2.so

For the detailed instruction see this page.


Download version 1.60:

wget http://sourceforge.net/projects/boost/files/boost/1.60.0/boost_1_60_0.tar.gz
tar xzf boost_1_60_0.tar.gz
cd boost_1_60_0

Build and install:

./bootstrap.sh --with-libraries=filesystem,iostreams,log,system,thread,test
./b2 cxxflags="-std=c++11" include="$LOCAL_LIBS/include/" search="$LOCAL_LIBS/lib/" link=shared threading=multi install --prefix=$LOCAL_LIBS

For the detailed instruction see tutorial page.


CMake can't find installed libraries

If cmake can't find one of the libraries, or you want to use some specific versions, which are currently not in the default path, use corresponding cmake variables:

  • Boost: BOOST_ROOT.
  • BamTools: BAMTOOLS_ROOT.
  • R: R_ROOT. Can be found by running the cat(R.home()) in R.

These variables should be set to the path to the installed library. It can be done either by using command line options: cmake -D R_ROOT="path_to_r" or by adding the variable declaration to the beginning of CMakeLists.txt: set(R_ROOT path_to_r).

In case you have some issues with the linker for specific library, please build this library manually with the version of compiler, which you're going to use for dropEst build.

Problems with std::__cxx11::string

If you have messages like "(path to some library): undefined reference to (some name) for std::__cxx11::basic_ostringstream<char, std::char_traits, std::allocator >", it means that you're trying to link a library, built with gcc < 5.0, while dropEst is built with gcc >= 5.0. It's a compiler issue, and you have to guarantee consistency of compiler versions by rebuilding either the library or dropEst. For more details see question on stackoverflow.

If you have several compilers in your system, please use cmake flags -DCMAKE_CXX_COMPILER=(c++ compiler) and -DCMAKE_C_COMPILER=(c compiler) to choose a compiler. Here, (c++ compiler) and (c compiler) denotes path to the prefered compiler version.

Boost 1.65

CMake < 3.10 has known issues with boost 1.65. If you have such combination, please try either to upgrade cmake or to downgrade boost.


In case you still can't build the project, dockerfiles for the most popular linux distributions are provided (see dropEst/dockers/). You can either build and run these dockers or just read dockerfiles for the further instructions on dropEst installation for specific distribution.

To install docker on your system see installation instruction. After installing the docker, use the following commands to start it:

cd dropEst/dockers/debian9
docker build -t dropest .
docker run --name dropest -it dropest

Or, for CentOS 6:

cd dropEst/dockers/centos6
docker build -t dropest .
docker run --name dropest -it dropest

You can find more info about dockers at Docker Cheat Sheet


droptag -- generate tagged fastq files for alignment

Example command:

./droptag -c config.xml [-S] [-s] reads1.fastq reads2.fastq [...]

Positional arguments of the dropTag phase contain paths to the read files, obtained with a sequencer. These files can be either in .fastq of .fastq.gz format. The file order depends on the type of used protocol. Below is the instruction on the usage for currently supported protocols.

Note. Algorithm of UMI correction requires information about base-call quality of UMIs. To save this information, use -s option. In this case, in addition to fastq files with reads, the pipeline saves separate gzipped file with read parameters. These files must be passed to dropEst with -r option.


inDrop v1 & v2

  • File 1: barcode reads. Structure:
    • Cell barcode, part 1
    • Spacer
    • Cell barcode, part 2
    • UMI
  • File 2: gene reads

Example config file is located at "dropEst/configs/indrop_v1_2.xml".
Example command:

./droptag -c [-S] [-s] ./configs/indrop_v1_2.xml barcode_reads.fastq gene_reads.fastq

inDrop v3

  • File 1: cell barcode, part 1 (default length: 8bp)
  • File 2: cell barcode + UMI, part 1 (default length: >= 14bp)
  • File 3: gene read
  • File 4 (optional): library tag

If a file with library tags provided, option "-t" is required. This option wasn't tested properly, so it's better to avoid using it.

Example config file is located at "dropEst/configs/indrop_v3.xml".
Example command:

./droptag -c dropEst/configs/indrop_v3.xml [-S] [-s] [-t library_tag] barcode1_reads.fastq barcode2_reads.fastq gene_reads.fastq [library_tags.fastq]


  • File 1: library tag (default length: 8bp)
  • File 2: cell barcode + UMI, part 1 (default length: 16+10=26bp)
  • File 3: gene read

Example config file is located at "dropEst/configs/10x.xml".
Example command:

./droptag -c dropEst/configs/10x.xml [-S] [-s] lib_tag_reads.fastq barcode_reads.fastq gene_reads.fastq

While dropTag provides way to demultiplex 10x data, Cell Ranger is still recommended tool for this. dropEst phase can be ran on the Cell Ranger demultiplexed .bam file to obtain data in the format, optimized for the subsequent analysis.

Note. Sometimes 10x CellRanger isn't able to determine gene, from which a read originated. In this cases it fills gene info with a list of possible genes, separated by semicolon (e.g. 'ENSG00000255508;ENSG00000254772'). These genes must be filtered out prior to further analysis:

holder <- readRDS('./cell.counts.rds')
cm <- holder$cm[grep("^[^;]+$", rownames(holder$cm)),]


  • File 1: Gene reads with barcodes at the beginning of the sequence

Example config file is located at "dropEst/configs/iclip.xml".
Example command:

./droptag -c dropEst/configs/iclip.xml [-S] [-s] data.fastq

Note. Implementation of iCLIP wasn't tested properly. Please, be careful using it. Anyone who used it is very welcome to comment it either in Issues or by e-mail.

Command line arguments for dropTag

  • -c, --config filename: xml file with droptag parameters
  • -l, --log-prefix prefix: logs prefix
  • -n, --name name: alternative output base name
  • -p, --parallel number: number of threads (usage of more than 6 threads should lead to significant speed up)
  • -r, --reads-per-out-file : maximum number of reads per output file; (0 - unlimited). Overrides corresponding xml parameter.
  • -S, --save-stats : save stats to rds file. This data is used on the dropReport phase.
  • -t, --lib-tag library tag : (for IndropV3 with library tag only)
  • -q, --quiet : disable logs

Please, use ./droptag -h for additional help.


dropTag writes the tagged reads into multiple files. All these files must be aligned to reference, and all bam files with the alignments must be provided as input for the dropEst stage. In the paper we used TopHat2 aligner, however any RNA-seq aligners (i.e. Kallisto or STAR) can be used.

Alignment with TopHat

  1. Install bowtie index for the sequenced organism.
  2. Download corresponding gene annotation in the gtf format.
  3. Run TopHat2 for each file:
tophat2 -p number_of_threads --no-coverage-search -g 1 -G genes.gtf -o output_dir Bowtie2Index/genome reads.fastq.gz
  1. The result needed for the count estimation is ./output_dir/accepted_hits.bam.

Alignment with Kallisto

Prior to v0.42.4, Kallisto didn't use BAM flags to mark primary/secondary alignments. Only versions $\geq$ 0.42.4 are supported.

  1. Download transcript sequences in .fasta format (i.e. Ensembl genes).
  2. Build Kallisto index: kallisto index -i genes.fa.gz.
  3. Run kallisto quant --pseudobam --single -i genes.index -o out -l mean_length -s std_length reads.fastq.gz. Here, mean_length is mean length of RNA fragment (not read length) and std_length is standard deviataion of RNA fragment length. You should specify values, according to the experiment design.


dropest: estimate molecular counts per cell

This phase requires aligned .bam files as input and uses them to estimate count matrix. These files must contain information about cell barcode and UMI for each read (reads, which don't contain such information are ignored). Three possible ways to encode this information are acceptable:

  1. Encode it in read names (as a result of dropTag phase).
  2. Use .bam tags (i.e. output of 10x Cell Ranger). Tag names can be configured in config.xml file.
  3. Save this information in a separate file using -s option on dropTag phase and pass it to dropEst with -r option.

Note. To run Bayesian algorithm of UMI error correction with dropestr, you need to pass information about UMI base call quality to dropEst. You can do it either using .bam tags or -s dropTag option (i.e. option 1 isn't available in this case). You still can run simpler algorithms (i.e. cluster or directional, see paper) without this information.

Count matrix estimation also requires information about the source gene for the reads. It can be provided in two ways:

  1. Use gene annotation in either .bad or .gtf format. To provide such file, "-g" option should be used.
  2. Use .bam tags. Tag name can be configured in config.xml file.

Another crucial moment in estimation of count matrix is correction of cell barcode errors. Most protocols provide the list of real barcodes, which simplifies the task of correction. If such file is available, path to the file should be specified in the config.xml file (Estimation/Merge/barcodes_file). This can dramatically increase quality of the result. Lists for inDrop protocols can be found at dropEst/data/barcodes/. Two algorithms of barcode correction are available:

  1. Simple, "-m" option. This algorithm is recommended in the case, where barcodes list is supplied.
  2. Precise, "-M" option. Doesn't requires list of real barcodes to obtain high-quality results, however has lower performance for large datasets.

Example command:

dropest [options] [-m] [-r pipeline_res.params.gz] -g ./hg38/genes.gtf -c ./config.xml ./alignment.*/accepted_hits.bam

Usage of tagged bam files (e.g. 10x, Drop-seq) as input

Some protocols provide pipelines, which create .bam files with information about CB, UMI and gene. To use these files as input, specify "-f" option. Example:

dropest [options] -f [-g ./genes.gtf] -c ./config.xml ./pipeline_res_*.bam

If "-g" option is provided, genes are parsed from the gtf, and information about genes from the bam file is ignored.

To specify corresponding .bam tag names, use "Estimation/BamTags" section in the config (see configs/config_desc.xml).

Usage of pseudoaligners

Pseudoaligners, such as Kallisto store gene / transcript names in the field of chromosome name. To parse such files, use "-P" option. Example:

dropest [options] -P [-r pipeline_res.params.gz] -c ./config.xml ./kallisto_res_*.bam

Count intronic / exonic reads only

One feature of the pipeline is the ability to count only UMIs, which reads touch only specific parts of a genome. Option "-L" allows to specify all acceptable types of regions:

  • e: UMIs with exonic reads only
  • i: UMIs with intronic reads only
  • E: UMIs, which have both exonic and not annotated reads
  • I: UMIs, which have both intronic and not annotated reads
  • B: UMIs, which have both exonic and intronic reads
  • A: UMIs, which have exonic, intronic and not annotated reads

Thus, to count all UMIs with exonic or not annotated reads, use "-L eE". Default value: "-L eEBA", i.e. to count all UMIs, which have at least one reads, touching exon.

Example commands:

  • Intronic reads only:
    dropest [-f] [-g ./genes.gtf] [-r pipeline_res.params.gz] -L i -c ./config.xml ./alignment_*.bam
  • Exonic reads only:
    dropest [-f] [-g ./genes.gtf] [-r pipeline_res.params.gz] -L e -c ./config.xml ./alignment_*.bam
  • Exon/intron spanning reads:
    dropest [-f] [-g ./genes.gtf] [-r pipeline_res.params.gz] -L BA -c ./config.xml ./alignment_*.bam

The pipeline can determine genome regions either using .gtf annotation file or using .bam tags, i.e. for CellRanger output (see Estimation/BamTags/Type in configs/config_desc.xml). If .gtf file isn't provided and .bam file doesn't containt annotation tags, all reads with not empty gene tag are considered as exonic.

Velocyto integration

For some purposes (i.e. velocyto) it can be useful to look separately at the fraction of intronic and exonic UMIs. Option "-V" allows to output three separate count matrices, each of which contains only UMIs of a specific type: intronic, exonic or exon/intron spanning. These matrices are stored in the separate file "cell.counts.matrices.rds".

Note. If you use bam file from other pipeline (i.e. 10x), please ensure that you provided gtf file with genes with -g option. Because CellRanger (and some other pipelines) doesn't include information about intronic reads to the bam file.

Command line arguments for dropEst

  • -b, --bam-output: print tagged bam files
  • -c, --config filename: xml file with estimation parameters
  • -C, --cells num: maximal number of output cells
  • -f, --filled-bam: bam file already contains genes/barcodes tags
  • -F, --filtered-bam: print tagged bam file after the merge and filtration
  • -g, --genes filename: file with genes annotations (.bed or .gtf)
  • -G, --genes-min num: minimal number of genes in output cells
  • -l, --log-prefix : logs prefix
  • -m, --merge-barcodes : merge linked cell tags
  • -M, --merge-barcodes-precise : use precise merge strategy (can be slow), recommended to use when the list of real barcodes is not available
  • -o, --output-file filename : output file name
  • -P, --pseudoaligner: use chromosome name as a source of gene id
  • -q, --quiet : disable logs
  • -r, --read-params filenames: file or files with serialized params from tags search step. If there are several files, they should be provided in quotes, separated by space: "file1.params.gz file2.params.gz file3.params.gz"
  • -R, --reads-output: print count matrix for reads and don't use UMI statistics
  • -u, --merge-umi: apply 'directional' correction of UMI errors. This option prevents output of reads_per_umi_per_cell. If you want to apply more advanced UMI correction, don’t use ‘-u’, but use follow up R analysis.
  • -V, --velocyto : save separate count matrices for exons, introns and exon/intron spanning reads
  • -w, --write-mtx : write out matrix in MatrixMarket format


Result of this phase is cell.counts.rds file with the next fields:

  • cm (sparse matrix): count matrix in sparse format
  • reads_per_chr_per_cell (list of data.frame): number of reads per cell (row) for each chromosome (column):
    • Exon (data.frame): exonic reads
    • Intron (data.frame): intronic reads
    • Intergenic (data.frame): intergenic reads
  • mean_reads_per_umi (vector): mean number of reads per UMI for each cell
  • some additional info

To additionaly print the file with count matrix in MatrixMarket format use "-w" option.


To run the report you have to install:

  • dropestr R package with all dependencies (dependencies = T).
  • pandoc
  • R packages:

To run the report use:

./dropReport.Rsc cell.counts.rds

To see full list of options use:

./dropReport.Rsc -h


If you get the error "pandoc version 1.12.3 or higher is required and was not found", try to set path in the corresponding environment variable: export RSTUDIO_PANDOC=/path/to/pandoc.


Complete examples of the pipeline can be found at EXAMPLES.md.

dropEstR package

This package implements UMI errors corrections and low-quality cells filtration, which are not performed during dropEst phase.

To install the package, use:

devtools::install_github('hms-dbmi/dropEst/dropestr', dependencies = T)

Package content:

  • filtration of low-quality cells (see vignette "low-quality-cells")
  • correction of UMI errors (see vignette "umi-correction")
  • quality control (see dropReport.Rsc)


dropestr depends on ks package, which suggests installed X Server. If you don't want to install it, use:

install.packages("ks", dependencies=c("Depends", "Imports", "LinkingTo"))

Additional notes

Description of the fields for the config file is provided in dropEst/configs/config_desc.xml.