hms-dbmi/dropEst

Name: dropEst

Owner: Harvard Medical School - Department of Biomedical Informatics

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

Created: 2017-07-17 20:28:48.0

Updated: 2017-12-08 02:14:28.0

Pushed: 2018-01-15 11:13:22.0

Homepage: null

Size: 40650

Language: C++

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README

dropEst - Pipeline

Pipeline for estimating molecular count matrices for droplet-based single-cell RNA-seq measurements. Implements methods, described in this paper.

Table of contents

• dropEst - Pipeline
  • Table of contents
  • General processing steps
  • Setup
    • System requirements
    • Installation
    • Troubleshooting
  • dropTag
    • Protocols
      • inDrop v1 & v2
      • inDrop v3
      • 10x
    • Command line arguments for dropTag
  • Alignment
    • Alignment with TopHat
    • Alignment with Kallisto
  • dropEst
    • Usage of tagged bam files (e.g. 10x, Drop-seq) as input
    • Usage of pseudoaligners
    • Count intronic / exonic reads only
    • Command line arguments for dropEst
    • Output
  • dropReport
    • Troubleshooting
  • dropEstR package
  • Additional notes

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.

Setup

System requirements

• Boost >= 1.54
• BamTools with headers
• Either install libbamtools-dev
• or build it locally and then specify the location of the build when running cmake (e.g. cmake -D BAMTOOLS_ROOT=/home/username/bamtools .)
• Zlib
• 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)

Installation

Install R packages:

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

Clone this repository:

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

Build:

ropEst
e . && make

Troubleshooting

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 try to build this library manually with the version of compiler, which you're going to use for dropEst build.

dropTag

droptag -- generate tagged fastq files for alignment

Example command:

optag -c config.xml [-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.

Protocols

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:

optag -c ./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.

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

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

10x

• 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:

optag -c dropEst/configs/10x.xml [-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:

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

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)
• -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.

Alignment

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:

   at2 -p number_of_threads --no-coverage-search -g 1 -G genes.gtf -o output_dir Bowtie2Index/genome reads.fastq.gz
   

4. 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 read length and std_length is standard deviataion of read length. You should specify values, according to the experiment design.

dropEst

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). Two 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.

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 significantly lower performance.

Example command:

est [options] [-m] -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:

est [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:

pest [options] -P -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”.

Example commands:

• Intronic reads only:

  est [-f] [-g ./genes.gtf] -L i -c ./config.xml ./alignment_*.bam
  

• Exonic reads only:

  est [-f] [-g ./genes.gtf] -L i -c ./config.xml ./alignment_*.bam
  

• Exon/intron spanning reads:

  est [-f] [-g ./genes.gtf] -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”.

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
  
• -R, –reads-output: print count matrix for reads and don't use UMI statistics
  
• -q, –quiet : disable logs
  
• -V, –velocyto : save separate count matrices for exons, introns and exon/intron spanning reads
• -w, –write-mtx : write out matrix in MatrixMarket format
  

Output

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.

dropReport

To run the report you have to install:

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

  all.packages(c("optparse","rmarkdown"))
  

To run report use:

opReport.Rsc cell.counts.rds

To see full list of options use:

opReport.Rsc -h

Troubleshooting

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.

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:

ools::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)

Additional notes

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