nf-core/lsmquant
A pipeline for processing and analysis of light-sheet microscopy images.
Introduction
Samplesheet input
You will need to create a samplesheet with information about the samples you would like to analyse 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
Column | Description |
---|---|
sample_id | Custom sample name. |
img_directory | Full path to the image directory for the sample. |
parameter_file | Full path to the corresponding parameter_file for the analysis. |
Single or Multiple samples
The pipline 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 modifyed. You can download a template parameter file here.
An example row is displayed below:
Parameter,Value
z_window,5
The individual parameters are explained here
Analysis specific parameters
This section descripbes every parameter that can be set in the parameter.csv
. In order that the pipeline runs 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
.
Parameter | Description |
---|---|
darkfield_intensity | 1xn_channels; Constant darkfield intensity value (i.e. average intensity of image with nothing present). Default: 101 |
img_directory | |
single_sheet | true, false; Whether a single sheet was used for acquisition |
ls_width | 1xn_channels interger. Light sheet width setting for UltraMicroscope II as percentage. Default: 50 |
laser_y_displacement | [-0.5,0.5]; Displacement of light-sheet along y axis. Value of 0.5 means light-sheet center is positioned at the top of the image. Default: 0 |
sampling_frequency | [0,1]; Fraction of images to read and sample from. Setting to 1 means use all images. Default: 0.2 |
shading_correction_tiles | Integer vector. Subset tile positions for calculating shading correction (row major order). It’s recommended that bright regions are avoided |
shading_smoothness | numeric >= 1; Factor for adjusting smoothness of shading correction. Greater values lead to a smoother flatfield image. Default: 2 |
shading_intensity | numeric >= 1; Factor for adjusting the total effect of shading correction. Greater values lead to a smaller overall adjustment. Default: 1 |
update_z_adjustment | true, false; Update z adjusment steps with new parameters. Otherwise pipeline will search for previously calculated parameters. Default: false |
z_positions | integer or numeric; Sampling positions along adjacent image stacks to determine z displacement. If <1, uses fraction of all images. Set to 0 for no adjustment, only if you’re confident tiles are aligned along z dimension. Default: 0.01 |
z_window | integer; Search window for finding corresponding tiles (i.e. +/-n z positions). Default: 5 |
z_initial | 1xn_channels-1 interger; Predicted initial z displacement between reference channel and secondary channel. Default: 0 |
align_method | elastix, translation; Channel alignment by rigid, 2D translation or non-rigid B-splines using elastix. Default: translation |
align_tiles | Option to align only certain stacks and not all stacks. Row-major order. Default: ” |
align_channels | Option to align only certain channels (set to >1). Default: ” |
align_slices | Option to align only certain slice ranges. Set as cell array for non-continuous ranges (i.e. {1:100,200:300}). Default: ” |
align_stepsize | interger; Only for alignment by translation. Number of images sampled for determining translations. Images in between are interpolated. Default: 5 |
only_pc | true, false; Use only phase correlation for registration. This gives only a quick estimate for channel alignment. Default: false |
align_chunks | Only for alignment by elastix. Option to align only certain chunks. Default: ” |
elastix_params | 1xn_channels-1 string; Name of folders containing elastix registration parameters. Place in /supplementary_data/elastix_parameter_files/channel_alignment. Default: 32_bins |
pre_align | true, false; (Experimental) Option to pre-align using translation method prior to non-linear registration. Default: false |
max_chunk_size | integer; Chunk size for elastix alignment. Decreasing may improve precision but can give spurious results. Default: 300 |
chunk_pad | integer; Padding around chunks. Should be set to value greater than the maximum expected translation in z. Default: 30 |
mask_int_threshold | numeric; Mask intensity threshold for choosing signal pixels in elastix channel alignment. Leave empty to calculate automatically. Default: ” |
resample_s | 1x3 integer. Amount of downsampling along each axis. Some downsampling, ideally close to isotropic resolution, is recommended. Default: 3;3;1 |
hist_match | 1xn_channels-1 interger; Match histogram bins to reference channel? If so, specify number of bins. Otherwise leave empty or set to 0. This can be useful for low contrast images. Default: 64 |
sift_refinement | true, false; Refine stitching using SIFT algorithm (requires vl_fleat toolbox). Default: true |
load_alignment_params | true, false; Apply channel alignment translations during stitching. Default: true |
overlap | 0:1; overlap between tiles as fraction. Default: 0.20 |
stitch_sub_stack | z positions; If only stitching a cetrain z range from all the images. Default: ” |
stitch_sub_channel | channel index; If only stitching certain channels. Default: ” |
stitch_start_slice | z index; Start stitching from specific position. Otherwise this will be optimized. Default: ” |
blending_method | sigmoid, linear, max. Default: sigmoid |
sd | 0:1; Recommended: ~0.05. Steepness of sigmoid-based blending. Larger values give more block-like blending. Default: 0.05 |
border_pad | integer >= 0; Crops borders during stitching. Increase if images shift significantly between channels to prevent zeros values from entering stitched image. Default: 25 |
rescale_intensities | true, false; Rescaling intensities and applying gamma. Default: false |
lowerThresh | 1xn_channels numeric; Lower intensity for rescaling. Default: ” |
signalThresh | 1xn_channels numeric; Rough estimate for minimal intensity for features of interest. Default: ” |
upperThresh | 1xn_channels numeric; Upper intensity for rescaling. Default: ” |
Gamma | 1xn_channels numeric; Gamma intensity adjustment. Default: ” |
subtract_background | true, false. Subtrat background (similar to Fiji’s rolling ball background subtraction).Default: false |
nuc_radius | numeric >= 1; Max radius of cell nuclei along x/y in pixels. Required also for DoG filtering.Default: 13 |
DoG_img | true,false; Apply difference of gaussian enhancement of blobs.Default: false |
DoG_minmax | 1x2 numeric; Min/max sigma values to take differene from.Default: 0.8;2 |
DoG_factor | [0,1]; Factor controlling amount of adjustment to apply. Set to 1 for absolute DoG.Default: 1 |
smooth_img | 1xn_channels, “gaussian”, “median”, “guided”. Apply a smoothing filter.Default: false |
smooth_sigma | 1xn_channels numeric; Size of smoothing kernel. For median and guided filters, it is the dimension of the kernel size. Default: ” |
flip_axis | ”none”, “horizontal”, “vertical”, “both”; Flip image along horizontal or vertical axis.Default: none |
rotate_axis | 0, 90 or -90; Rotate image.Default: 0 |
group | Group name/id.Default: TEST;WT;R1 |
channel_num | Channel id.Default: C01;C00 |
markers | Name of markers present.Default: topro;ctip2 |
position_exp | 1x3 string of regular expression specifying image row(y), column(x), slice(z).Default: [\d;\d];Z\d** |
resolution | Image reolution in um/voxel.Default: ” |
orientation | 1x3 string specifying sample orientation. Default: ail |
hemisphere | ”left”,“right”,“both”,“none”. Default: left |
channel_alignment | true, update, false; Channel alignment. Default: true |
adjust_intensity | true, update, false; Whether to calculate and apply any of the following intensity adjustments. Intensity adjustment measurements should typically be performed on raw images. Default: update |
stitch_images | true, update, false; 2D iterative stitching. Default: true |
use_processed_images | false or name of sub-directory in output directory (i.e. aligned, stitched…); Load previously processed images in output directory as input images. Default: false |
ignore_markers | completely ignore marker from processing steps. Default: Auto |
save_images | true or false; Save images during processing. Otherwise only parameters will be calculated and saved. Default: true |
save_samples | true, false; Save sample results for each major step. Default: true |
adjust_tile_shading | basic, manual, false; Can be 1xn_channels. Perform shading correction using BaSIC algorithm or using manual measurements from UMII microscope. Default: basic |
adjust_tile_position | true, false; Can be 1xn_channels. Normalize tile intensities by position using overlapping regions. Default: true |
resample_images | true, update, false; Perform image resampling. Default: true |
register_images | true, update, false; Register image to reference atlas. Default: true |
count_nuclei | true, update, false; Count cell nuclei or other blob objects.Default: true |
classify_cells | true, update, false; Classify cell-types for detected nuclei centroids. Default: false |
resample_resolution | Isotropic resample resolution. This is also the resolution at which registration is performed. Default: 25 |
resample_channels | Resample specific channels. If empty, only registration channels will be resampled. Default: ” |
use_annotation_mask | true, false; Use annotation mask for cell counting. Default: false |
annotation_mapping | atlas, image; Specify whether annotation file is mapped to the atlas or light-sheet image. Default: atlas |
annotation_file | File for storing structure annotation data. Default: ” |
annotation_resolution | Isotropic resolution of the annotation file. Only needed when mapping is to the image. Default: 25 |
registration_direction | atlas_to_image, image_to_atlas; Direction to perform registration. Default: atlas_to_image |
registration_parameters | default, points, or name of folder containing elastix registration parameters in /data/elastix_parameter_files/atlas_registration. Default: default |
registration_channels | integer; Which light-sheet channels to register. Can select more than 1. Default: 1 |
registration_prealignment | image. Pre-align multiple light-sheet images by rigid transformation prior to registration. Default: image |
atlas_file | ara_nissl_25.nii and/or average_template_25.nii and/or a specific atlas .nii file in /data/atlas. Default: 3Drecon-ADMBA-P4_atlasVolume.nii |
use_points | Use points during registration. Default: false |
prealign_annotation_index | Not used. Default: ” |
points_file | Name of points file to guide registration. Default: ” |
save_registered_images | Whether to save registered images. Default: true |
mask_cerebellum_olfactory | Remove olfactory bulbs and cerebellum from atlas ROI. Default: true |
count_method | Default: 3dunet |
int_threshold | Minimum intensity of positive cells. Default: 200 |
model_file | Model file name. Default: ” |
gpu | Cuda visible device index. Default: 0 |
chunk_size | ’Chunk size in voxels. Default: [112, 112, 32] |
chunk_overlap | Overlap between chunks in voxels. Default: [16, 16, 8] |
pred_threshold | Prediction threshold. Default: 0.5 |
normalize_intensity | Whether to normalize intensities using min/max. Default: true |
resample_chunks | Whether to resample image to match trained image resolution. Note: increases computation time. Default: false |
tree_radius | Pixel radius for removing centroids near each other. Default: 2 |
acquired_img_resolution | Resolution of acquired images. Default: [0.75, 0.75, 4] |
trained_img_resolution | Resolution of images the model was trained on. Default: [0.75, 0.75, 2.5] |
measure_coloc | Measure intensity of co-localizaed channels. Default: false |
n_channels | Number of channels. Default: ” |
use_mask | Use mask. Default: false |
mask_file | Mask file. Default: ” |
resample_resolution | Resolution of resampled images. Default: 25 |
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
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.
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>
.
Do not use -c <file>
to specify parameters as this will result in errors. Custom config files specified with -c
must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).
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.
If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.
Core Nextflow arguments
These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen)
-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.
We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.
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).
- A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow
conda
- A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.
-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 customise 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 organisation 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'