nf-core/lsmquant

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Introduction

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyze before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Samplesheet header:

sample_id,img_directory,parameter_file

Single or Multiple samples

The pipeline always takes the samplesheet as input. For processing only one sample, you would only specify one sample in the samplesheet. The samplesheet below shows an example for processing multiple samples with the pipeline.

sample_id,img_directory,parameter_file
TEST1,/path/to/TEST1/,/path/to/params_TEST1.csv
TEST2,/path/to/TEST2/,/path/to/params_TEST2.csv
TEST3,/path/to/TEST3/,/path/to/params_TEST3.csv

If different samples should be processed with the same parameter set specified in the params.csv, you can use the same params.csv for different samples.

Parameter file

In the parameter.csv file you should specify processing parameters for your data and pipeline run. The CSV contains specific fields that are needed for the processes to run and only the value column should be modified. You can download a template parameter file here. An example row is displayed below:

Parameter,Value
z_window,5

The individual parameters are explained in the methods section.

Analysis specific parameters

This section describes every parameter that can be set in the parameter.csv. In order for the pipeline to run correctly all named parameters need to be present in the parameter file and its recommended to use the provided parameter file (link). Every parameter has a default value that will be set if not otherwise defined in the parameter.csv.

Sample specific information

Parameters for adjusting intensities

Manual tile shading correction (specific for LaVision Ultramicroscope II):

Shading correction using BaSiC:

Parameters for channel alignment

Z alignment parameters (for stitching and align by translation)

For align by translation

Specific for align by elastix

Stitching parameters

Specific for iterative 2D stitching

Postprocessing parameters

These are applied during the stitching process after the image has been merged.

Parameters for rescale intensities

Parameters for background subtraction

Difference-of-Gaussian filter

Smoothing filters

Update sample orientation

Resampling and annotations parameters

Registration parameters

Nuclei Detection

3-DUnet specific parameters

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/lsmquant --input ./samplesheet.csv --outdir ./results -profile docker -work-dir ./work

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

For this pipeline it is recommended to specify the location of the work directory as well with -work-dir. The directory will contain any nextflow working files which includes all in- and output files. The work directory will be larger than the input sample size. If you don’t specify a location, the work directory will be created in the location from where the pipeline got started.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run nf-core/lsmquant -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you’re running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull nf-core/lsmquant

Reproducibility

It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you’ll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the nf-core/lsmquant releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you’ll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow arguments

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to check if your system is supported, please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • This profile is not available for nf-core/lsmquant

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customize the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organization are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Methods description

This section provides a detailed explanation for the individual processing steps, based on the original NuMorph toolbox publication.

Intensity adjustment

This step performs two types of intensity adjustments to the raw images before tile stitching:

• Shading correction (Option: BaSiC, manual)

• Normalizing intensities between tile stacks

Shading correction by using BaSiC

The Gaussian shape of the light-sheet causes uneven illumination and shading across the y-axis. To correct these effects the tool BaSiC (MATLAB tool for retrospective shading correction) is used. A fraction of all images is used to estimate the flatfiled for each channel. Every image is then divided by the estimated flatfield to normalize illumination. With the parameter sampling_frequency, the fraction of images to use for BaSiC, is specified as a decimal. For example setting the sampling_frequency to 0.1, 10% of all images will be used to estimate the flatfield.

Normalizing intensities between tile stacks

Photo-bleaching and light attenuation can cause differences in brightness between tile stacks. To account for that, the differences in intensities are measured in overlapping regions (vertical and horizontal) of adjacent tiles. Next the adjustment factor $t^{adj}$, based on the 95th percentile of pixel intensities in overlapping regions, is calculated. For this 5% of all images are used. The final adjustment is applied with the following formula:

$I^{adj}(x,y) = (I(x,y) - D)*t^{adj}(x,y) + D$

• $I(x,y)$: Original measured image intensities at tile position (x,y)
• $I^{adj}(x,y)$: Adjusted image intensities at tile position (x,y)
• $t^{adj}$: Adjustment factor based on the 95th percentile of intensity differences
• $D$: Darkfield intensity (constant value based on the 5th percentile of pixel intensities across all measured regions)

Channel alignment

Drifts in sample positions and stage can occur during imaging multiple channels causing spatial misalignment. To correct for these shifts between channels two methods can be chosen:

• Rigid 2D translation: align_method: translation
• Nonlinear 3D registration using Elastix : align_method: elastix

Both methods expect the nuclear channel as reference, to which all other immunolabeled channels will be aligned to.

Rigid 2D translation

This approach estimates first the relative z displacement between the nuclei reference channel and every other channel. Within each tile, a number evenly spaced z slices of the reference channel is chosen by the parameter z_position. For every z position, phase corelation is calculated between all images from another channel in a search window (set by z_window) and summed up. The z position with the highest image similarity based on intensity correlation defines the inter-channel z displacement

Next, multimodal 2D registration is performed on each slices in the image stack by using MATLAB’s Image Processing toolbox, to determine xy translations. Outlier translations are defined as translations that are greater than 3 scaled median absolute deviations within a local window of 10 slices. These outliers are corrected by linear interpolation of adjacent images in the stack.

Nonlinear 3D registration using Elastix

To correct for rotations and other drifts for which 2D rigid translation is not sufficient, a nonlinear 3D B-spline registration using Elastix can be applied on individual tiles.

1. Downsampling: A full tile stack is loaded and downsampled by a factor of 3 in the x/y dimensions to create a nearly isotropic volume and reduce the computation time.

2. Normalizing intensities: For comparable brightness and contrast, intensity histogram matching is performed across all channels of the stack.

3. Generation of foreground mask: A mask for the nuclei channel is generated by using a threshold that limits sampling from the background.

4. Initial global alignment: An initial 3D translational registration to the full stack is applied.

5. Local rigid alignment: The stack is subdivided into chunks of 300 slices and a rigid 3D registration on each chunk is performed. This step corrects rotational drift and improves the alignment within local regions.

6. Nonlinear refinement: A nonlinear B-spline registration is performed on each chunk by using an advanced Mattes mutual information metric. This accounts for xy drift along the z axis. The control point grid of the B-spline transformation are set to be sparse along xy compared to z to balance alignment accuracy and computational cost.

Iterative image stitching

The iterative 2D stitching procedure to assemble the whole image, consists of two main stages:

• Estimation of z correspondence between tile stacks
• Iterative xy alignment and stitching

Estimation of z correspondence between tile stacks

To determine optimal z correspondence for adjacent tiles, a sample of evenly spaced images (set with z_position) from within a stack are registered to every z position within a image window (set by z_window) of a adjacent stack (vertically and horizontally) by phase correlation. Z correspondences are ranked by the amount of peak correlations among the z positions, where the highest count represent the best correspondence. In addition, the difference between the best and the 2nd best z correlation is taken as a weight, indicating the strength of a correspondence (larger difference = stronger correspondence). Finally this results in 4 matrices for a stack representing pairwise horizontal and vertical z displacements and their corresponding weights. To calculate the final z displacement for each tile a minimum spanning tree is used, where displacements are used as vertices and their weights as edges.

Iterative xy alignment and stitching