`cell-seek run --pipeline multi`¶

1. About¶

The cell-seek executable is composed of several inter-related sub commands. Please see cell-seek -h for all available options.

This part of the documentation describes options and concepts for cell-seek run sub command for the MULTI pipeline which an be selected via the --pipeline flag in more detail. With minimal configuration, the run sub command enables you to start running cell-seek pipeline.

Setting up the cell-seek pipeline is fast and easy! In its most basic form, cell-seek run only has five required inputs.

1.1 Use Case¶

The MULTI pipeline uses the Cell Ranger multi function. There are multiple situation where this should be used. These include the following situations

Any combination that contains both gene expression and VDJ data
Any time sample multiplexing was performed and Cell Ranger will be used for demultiplexing
- This can include on-chip multiplexing (OCM), hashing with antibody capture (HTO), and CellPlex (CMO)
Flex (fixed RNA) capture

If the data does not include these modalities, then another pipeline should be considered.

A basic guideline of which pipeline should be used for different modalities can be found in the synopsis section of the run documentation.

2. Synopsis¶

$ cell-seek run [--help] \
      [--dry-run] [--job-name JOB_NAME] [--mode {{slurm,local}}] \
      [--sif-cache SIF_CACHE] [--singularity-cache SINGULARITY_CACHE] \
      [--silent] [--threads THREADS] [--tmp-dir TMP_DIR] \
      [--exclude-introns] \
      [--libraries LIBRARIES] [--features FEATURES] \
      [--cmo-sample CMOSAMPLE] [--cmo-reference CMOREFERENCE] \
      [--hto-sample HTOSAMPLE] \
      [--ocm-sample OCMSAMPLE] \
      [--probe-set PROBESET] [--probe-sample PROBESAMPLE] \
      [--filter FILTER] [--metadata METADATA] [--create-bam] \
      [--rename RENAME] [--forcecells FORCECELLS] \
      --input INPUT [INPUT ...] \
      --output OUTPUT \
      --pipeline multi \
      --genome {hg38, ...} \
      --cellranger {8.0.0, ...}

The synopsis for each command shows its arguments and their usage. Optional arguments are shown in square brackets.

A user must provide a list of FastQ (globbing is supported) to analyze via --input argument, an output directory to store results via --output argument, the multi pipeline via --pipeline argument, the reference genome to use via --genome argument, and the version of cellranger to use via --cellranger argument.

2.1 Required Arguments¶

Each of the following arguments are required. Failure to provide a required argument will result in a non-zero exit-code.

--input INPUT [INPUT ...]

Input FastQ file(s) or Cell Ranger folder(s).
type: file(s) or folder(s)

FastQ Input: One or more FastQ files can be provided. The pipeline does NOT support single-end data. From the command-line, each input file should separated by a space. Multiple input FastQ files per sample can be provided. Globbing is supported! This makes selecting FastQ files easy. Input FastQ files should always be gzipp-ed.

Example: --input .tests/*.R?.fastq.gz

Cell Ranger Input: Cell Ranger output folders can be provided. It is expected that the outs folder is contained within the Cell Ranger output folders, and keep the normal output folder structure. Globbing is supported!

Example: --input .tests/*/

--output OUTPUT

Path to an output directory.
type: path

This location is where the pipeline will create all of its output files, also known as the pipeline's working directory. If the provided output directory does not exist, it will be created automatically.

Example: --output /data/$USER/cell-seek_out

--pipeline multi

The pipeline to run.
type: string

This option selects the version of the pipeline to run. The documentation provided is based on choosing the option for multi analysis.

Example: --pipeline multi

--genome {hg38, mm10, hg2024, mm2024, custom.json}

Reference genome.
type: string

This option defines the reference genome of the samples. cell-seek does comes bundled with prebuilt reference files for human and mouse samples, The options hg38 or mm10 would select the 2020 release of the reference. The options hg2024 or mm2024 would select the 2024 release of the reference. More information about the officially released references can be found on the 10x Genomics website. Since there is no 2024 released VDJ reference, if hg2024 or mm2024 is selected in a run that includes VDJ data, the VDJ reference CR 7.1 release will be used for human, and CR 7.0 release will be used for mouse.

A custom reference genome can also be provided via a json file. Additional information for creating this json file can be found in cell-seek genome.

For prebuilt references please select one of the following options: hg38, mm10, hg2024, mm2024

Example: --genome hg38

--cellranger {7.1.0, 7.2.0, 8.0.0, 9.0.0, 10.0.0}

The version of Cell Ranger to run.
type: string

This option specifies which version of Cell Ranger to use when running GEX, VDJ, CITE, or MULTI pipelines. Please select one of the following options: 7.1.0, 7.2.0, 8.0.0, 9.0.0, 10.0.0

Example: --cellranger 7.1.0

2.2 Conditionally Required Arguments¶

The following arguments are only required when FastQ files are used as input. They are not required when Cell Ranger output file is used as input.

--libraries LIBRARIES

Libraries file.
type: file

A CSV file containing information about each library. It contains each sample's name, flowcell, demultiplexed name, and library type. More information about the libraries file and its requirements can be found on the 10x Genomics website.

Here is an example libraries.csv file:
Name,Flowcell,Sample,Type
IL15_LNs,H7CNNBGXG,IL15_LNs,Gene Expression
IL15_LNs,H7CT7BGXG,IL15_LNs_BC,Antibody Capture
Where:

Name: name of the sample passed to Cell Ranger.

Flowcell: The flowcell ID that contains the FASTQ files for this set of data.

Sample: Name that was used when demultiplexing, this should match the FASTQ files.

Type: library type for each sample. List of supported options:

Gene Expression

CRISPR Guide Capture

Antibody Capture

Multiplexing Capture

VDJ

Custom

Example: --libraries libraries.csv

2.3 Demultiplexing Options¶

This section contains the flags that are applicable when sample multiplexing was performed. Different flags should be used depending on the multiplexing method performed.

2.3.1 CellPlex (CMO) Options¶

--cmo-sample CMOSAMPLE

CMO sample file.
type: file

A CMO sample CSV file containing sample to CMO information used for multi analysis. CMO IDs should match the ones used in the CMO reference file. If no CMO reference is provided then CMO ID should match the ones used by 10x. The same CMO sample will be used on all multi libraries. This file should contain a unique sample ID for the sample, and the CMO ID(s) associated with that sample. If more than one CMO ID is associated with a sample then a | should be used to separate the tags. More information and examples about the samples section of the multi config file and its requirements can be found on the 10x Genomics website.

Here is an example cmo_sample.csv file:
sample_id,cmo_ids
sample1,CMO301
sample2,CMO302|CMO303
Where:

sample_id: Unique sample ID for this tagged sample. Must not contain whitespace, quote or comma characters. Each sample ID must be unique.

cmo_ids: Unique CMO ID(s) that the sample is tagged with. Must match either entries in the cmo_reference.csv file or 10x CMO IDs.

Example: --cmo-sample cmo_sample.csv

--cmo-reference CMOREFERENCE

CMO reference file.
type: file

A CMO reference CSV file containing information for processing CMO data, which is used in multi analysis if custom Cell Multiplexing oligos will be processed. This file should contain a unique ID for the CMO, a human readable name, sequence, feature type, read, and pattern. More information about the cmo reference file and its requirements can be found on the 10x Genomics website.

Here is an example cmo_reference.csv file:
id,name,sequence,feature_type,read,pattern
CMO301,CMO301,ATGAGGAATTCCTGC,Multiplexing Capture,R2,5P(BC)
CMO302,CMO302,CATGCCAATAGAGCG,Multiplexing Capture,R2,5P(BC)
Where:

id: Unique ID for this feature. Must not contain whitespace, quote or comma characters. Each ID must be unique and must not collide with a gene identifier from the transcriptome.

name: Human-readable name for this feature. Must not contain whitespace.

sequence: Nucleotide barcode sequence associated with this CMO

feature_type: Type of the feature. This should always be multiplexing capture.

read: Specifies which RNA sequencing read contains the Feature Barcode sequence. Must be R1 or R2, but in most cases R2 is the correct read.

pattern: Specifies how to extract the sequence of the feature barcode from the read.

Example: --cmo-reference cmo_reference.csv

2.3.2 Hashing with Antibody Capture (HTO) Arguments¶

--hto-sample HTOSAMPLE

HTO sample file.
type: file

A HTO sample CSV file containing sample to hashtag information used for multi analysis. This should always be used in conjunction with the feature file provided using the feature flag. More information about that flag can be found under Analysis Options.

HTO IDs should match the ones used in the feature reference file. The same HTO sample will be used on all multi libraries. This file should contain a unique sample ID for the sample, and the HTO ID(s) associated with that sample. If more than one HTO ID is associated with a sample then a | should be used to separate the tags. More information and examples about the samples section of the multi config file and its requirements can be found on the 10x Genomics website.

Here is an example hto_sample.csv file:
sample_id,hashtag_ids
sample1,HTO1
sample2,HTO2|HTO3
Where:

sample_id: Unique sample ID for this hashtagged sample. Must not contain whitespace, quote or comma characters. Each sample ID must be unique.

hashtag_ids: Unique HTO ID(s) that the sample is hashtagged with. Must match entries in the features.csv file.

Example: --hto-sample hto_sample.csv

2.3.3 On-Chip Multiplexing (OCM) Arguments¶

--ocm-sample OCMSAMPLE

OCM sample file.
type: file

An OCM sample CSV file containing sample to OCM barcode used for multi analysis. The same OCM sample will be used on all multi libraries. This file should contain a unique sample ID for the sample, and the OCM ID(s) associated with that sample. If more than one OCM ID is associated with a sample then a | should be used to separate the tags. More information and examples about the samples section of the multi config file and its requirements can be found on the 10x Genomics website.

Here is an example ocm_sample.csv file:
sample_id,ocm_barcode_ids
sample1,OB1
sample2,OB2
sample3,OB3|OB4
Where:

sample_id: Unique sample ID for this tagged sample. Must not contain whitespace, quote or comma characters. Each sample ID must be unique.

ocm_barcode_ids: Unique OCM ID(s) that the sample is tagged with.

Example: --ocm-sample ocm_sample.csv

2.4 Flex (Fixed RNA) Arguments¶

--probe-set PROBESET

Probe set file.
type: file

The probe set reference CSV file that matches the probes that were used in the capture. These are provided by 10x and can be found at their downloads page.

For those running the pipeline on the NIH's Biowulf, these references can also be found in the cell-seek references section in OpenOmics. The probe set reference files currently available in OpenOmics are:

Human

Chromium_Human_Transcriptome_Probe_Set_v1.0_GRCh38-2020-A.csv

Chromium_Human_Transcriptome_Probe_Set_v1.0.1_GRCh38-2020-A.csv

Chromium_Human_Transcriptome_Probe_Set_v1.1.0_GRCh38-2024-A.csv

Mouse

Chromium_Mouse_Transcriptome_Probe_Set_v1.0.1_mm10-2020-A.csv

Chromium_Mouse_Transcriptome_Probe_Set_v1.1.1_GRCm39-2024-A.csv

Example: --probe-set Chromium_Human_Transcriptome_Probe_Set_v1.1.0_GRCh38-2024-A.csv

--probe-sample PROBESAMPLE

Probe sample file.
type: file

A probe sample CSV file containing sample to probe barcode information used for multi analysis. This file should be used if multiplexing was performed. This file should contain a unique sample ID for the sample, and the probe barcode(s) associated with that sample. If more than one probe barcode is associated with a sample then a | should be used to separate the tags. More information and examples about the samples section of the multi config file and its requirements can be found on the 10x Genomics website.

Here is an example probe_sample.csv file:
sample_id,probe_barcode_ids
sample1,BC001
sample2,BC002|BC003
Where:

sample_id: Unique sample ID for this tagged sample. Must not contain whitespace, quote or comma characters. Each sample ID must be unique.

probe_barcode_ids: Unique probe barcode(s) that the sample is captured with.

Example: --probe-sample probe_sample.csv

2.5 Analysis Options¶

Each of the following arguments are optional, and do not need to be provided.

--features FEATURES

Features file.
type: file

A feature reference CSV file containing information for processing a feature barcode data. This file is used in feature barcode, and may be used in multi analysis. This file should contain a unique ID for the feature, a human readable name, sequence, feature type, read, and pattern. More information about the libraries file and its requirements can be found on the 10x Genomics website.

Here is an example features.csv file:
id,name,sequence,feature_type,read,pattern
CITE_CD64,CD64,AGTGGG,Antibody Capture,R2,5PNN(BC)
CITE_CD8,CD8,TCACCGT,Antibody Capture,R2,5PNNN(BC)
Where:

id: Unique ID for this feature. Must not contain whitespace, quote or comma characters. Each ID must be unique and must not collide with a gene identifier from the transcriptome.

name: Human-readable name for this feature. Must not contain whitespace.

sequence: Nucleotide barcode sequence associated with this feature, e.g. the antibody barcode or sgRNA protospacer sequence.

read: Specifies which RNA sequencing read contains the Feature Barcode sequence. Must be R1 or R2, but in most cases R2 is the correct read.

pattern: Specifies how to extract the sequence of the feature barcode from the read.

Type: Type of the feature. List of supported options:

CRISPR Guide Capture

Antibody Capture

Custom

Example: --features features.csv

--exclude-introns

Exclude introns from the count alignment.
type: boolean flag

Turns off the option of including introns when performing alignment. This flag is only applicable when dealing with gene expression related data.

Example: --exclude-introns

--create-bam

Create bam files.
type: boolean flag

By default the no-bam flag is used when running Cell Ranger. Use this flag to ensure that a bam file is created for each sample during analysis. This flag is only applicable when dealing with gene expression related data.

Example: --create-bam

--forcecells FORCECELLS

Force cells file.
type: file

Force cells file. A CSV file containing the name of the sample (the Cell Ranger outputted name) and the number of cells to force the sample to. It will generally be used if the first analysis run appears to do a poor job at estimating the number of cells, and a re-run is needed to adjust the number of cells in the sample.

This file can created in two different formats. The first one will contain the name of the sample and the number of cells to be forced to.

Here is an example forcecells.csv file:
Sample,Cells
Sample1,3000
Sample2,5000
Where:

Sample: The sample name used as the Cell Ranger output

Cells: The number of cells the sample should be forced to

In this example, Sample1 and Sample2 will be run while being forced to have 3000 and 5000 cells respectively. Any other samples that are processed will be run without using the force cells flag and will use the default cell calling algorithm.

The second format is only compatible when hashtag multiplexing is used and the number of cells needs to be forced for a specific hashtagged sample.

Here is an example forcecells.csv file:
Name,Sample,Cells
Library1,Sample1,3000
Library1,Sample2,5000
Where:

Library: The name of the library that is provided as to Cell Ranger when running multi analysis. This should match the name that is given in the libraries.csv file.

Sample: The sample ID used for the associated hashtag. This will have to match the value used in the CMO sample file or the CMO reference file that is provided as input. If only a CMO reference file is provided, the pipeline default assigns each hashtag with the IDs of HTO_1, HTO_2, etc.

Cells: The number of cells the sample should be forced to

In this example, the hashtags HTO_1 and HTO_2 in Library 1 will be run while being forced to 3000 and 5000 cells respectively. Any other libraries or samples that are processed will be run without using the force cells flag.

Example: --forcecells forcecells.csv

2.6 Orchestration Options¶

Each of the following arguments are optional, and do not need to be provided.

--dry-run

Dry run the pipeline.
type: boolean flag

Displays what steps in the pipeline remain or will be run. Does not execute anything!

Example: --dry-run

--silent

Silence standard output.
type: boolean flag

Reduces the amount of information directed to standard output when submitting master job to the job scheduler. Only the job id of the master job is returned.

Example: --silent

--mode {slurm,local}

Execution Method.
type: string
default: slurm

Execution Method. Defines the mode or method of execution. Vaild mode options include: slurm or local.

slurm
The slurm execution method will submit jobs to the SLURM workload manager. It is recommended running cell-seek in this mode as execution will be significantly faster in a distributed environment. This is the default mode of execution.

local
Local executions will run serially on compute instance. This is useful for testing, debugging, or when a users does not have access to a high performance computing environment. If this option is not provided, it will default to a local execution mode.

Example: --mode slurm

--job-name JOB_NAME

Set the name of the pipeline's master job.
type: string default: pl:cell-seek

When submitting the pipeline to a job scheduler, like SLURM, this option always you to set the name of the pipeline's master job. By default, the name of the pipeline's master job is set to "pl:cell-seek".

Example: --job-name pl_id-42

--singularity-cache SINGULARITY_CACHE

Overrides the $SINGULARITY_CACHEDIR environment variable.
type: path
default: --output OUTPUT/.singularity

Singularity will cache image layers pulled from remote registries. This ultimately speeds up the process of pull an image from DockerHub if an image layer already exists in the singularity cache directory. By default, the cache is set to the value provided to the --output argument. Please note that this cache cannot be shared across users. Singularity strictly enforces you own the cache directory and will return a non-zero exit code if you do not own the cache directory! See the --sif-cache option to create a shareable resource.

Example: --singularity-cache /data/$USER/.singularity

--sif-cache SIF_CACHE

Path where a local cache of SIFs are stored.
type: path

Uses a local cache of SIFs on the filesystem. This SIF cache can be shared across users if permissions are set correctly. If a SIF does not exist in the SIF cache, the image will be pulled from Dockerhub and a warning message will be displayed. The cell-seek cache subcommand can be used to create a local SIF cache. Please see cell-seek cache for more information. This command is extremely useful for avoiding DockerHub pull rate limits. It also remove any potential errors that could occur due to network issues or DockerHub being temporarily unavailable. We recommend running cell-seek with this option when ever possible.

Example: --singularity-cache /data/$USER/SIFs

--threads THREADS

Max number of threads for each process.
type: int
default: 2

Max number of threads for each process. This option is more applicable when running the pipeline with --mode local. It is recommended setting this vaule to the maximum number of CPUs available on the host machine.

Example: --threads 12

--tmp-dir TMP_DIR

Max number of threads for each process.
type: path
default: /lscratch/$SLURM_JOBID

Path on the file system for writing temporary output files. By default, the temporary directory is set to '/lscratch/$SLURM_JOBID' for backwards compatibility with the NIH's Biowulf cluster; however, if you are running the pipeline on another cluster, this option will need to be specified. Ideally, this path should point to a dedicated location on the filesystem for writing tmp files. On many systems, this location is set to somewhere in /scratch. If you need to inject a variable into this string that should NOT be expanded, please quote this options value in single quotes.

Example: --tmp-dir /scratch/$USER/

2.7 Miscellaneous Options¶

Each of the following arguments are optional, and do not need to be provided.

-h, --help

Display Help.
type: boolean flag

Shows command's synopsis, help message, and an example command

Example: --help

3. Example¶

3.1 GEX and VDJ¶

The following is an example of running multi when combining GEX and VDJ runs.

# Step 1.) Grab an interactive node,
# do not run on head node!
srun -N 1 -n 1 --time=1:00:00 --mem=8gb  --cpus-per-task=2 --pty bash
module purge
module load singularity snakemake

# Step 2A.) Dry-run the pipeline
./cell-seek run --input .tests/*.R?.fastq.gz \
                  --output /data/$USER/output \
                  --pipeline multi \
                  --genome hg38 \
                  --cellranger 8.0.0 \
                  --libraries libraries.csv \
                  --mode slurm \
                  --dry-run

# Step 2B.) Run the cell-seek pipeline
# The slurm mode will submit jobs to
# the cluster. It is recommended running
# the pipeline in this mode.
./cell-seek run --input .tests/*.R?.fastq.gz \
                  --output /data/$USER/output \
                  --pipeline multi \
                  --genome hg38 \
                  --cellranger 8.0.0 \
                  --libraries libraries.csv \
                  --mode slurm

3.2 Including HTO¶

3.2.1 Using the CMO flag¶

The following is an example of running multi while providing hashtag information.

# Step 1.) Grab an interactive node,
# do not run on head node!
srun -N 1 -n 1 --time=1:00:00 --mem=8gb  --cpus-per-task=2 --pty bash
module purge
module load singularity snakemake

# Step 2A.) Dry-run the pipeline
./cell-seek run --input .tests/*.R?.fastq.gz \
                  --output /data/$USER/output \
                  --pipeline multi \
                  --genome hg38 \
                  --cellranger 8.0.0 \
                  --libraries libraries.csv \
                  --cmo-reference cmo_reference.csv \
                  --mode slurm \
                  --dry-run

# Step 2B.) Run the cell-seek pipeline
# The slurm mode will submit jobs to
# the cluster. It is recommended running
# the pipeline in this mode.
./cell-seek run --input .tests/*.R?.fastq.gz \
                  --output /data/$USER/output \
                  --pipeline multi \
                  --genome hg38 \
                  --cellranger 8.0.0 \
                  --libraries libraries.csv \
                  --cmo-reference cmo_reference.csv \
                  --mode slurm

cell-seek run --pipeline multi¶

1. About¶

1.1 Use Case¶

2. Synopsis¶

2.1 Required Arguments¶

2.2 Conditionally Required Arguments¶

2.3 Demultiplexing Options¶

2.3.1 CellPlex (CMO) Options¶

2.3.2 Hashing with Antibody Capture (HTO) Arguments¶

2.3.3 On-Chip Multiplexing (OCM) Arguments¶

2.4 Flex (Fixed RNA) Arguments¶

2.5 Analysis Options¶

2.6 Orchestration Options¶

2.7 Miscellaneous Options¶

3. Example¶

3.1 GEX and VDJ¶

3.2 Including HTO¶

3.2.1 Using the CMO flag¶

`cell-seek run --pipeline multi`¶