nf-scautoqc

nf-scautoqc is the Nextflow implementation of scAutoQC pipeline used in Oliver et al, 2024.

How to run:

The recommended way to use nextflow is to run it in a screen session. These steps can be directly used in Sanger's FARM, but you can modify each step according to the environment you're working on or the job scheduler your HPC uses:

1. Start a screen session: screen -S nf_run1
2. Start a small interactive job for nextflow: bsub -G cellgeni -n1 -R"span[hosts=1]" -Is -q long -R"select[mem>2000] rusage[mem=2000]" -M2000 bash
3. Modify one of RESUME scripts (pre-made Nextflow run scripts)
4. Run the RESUME scripts you modified: ./RESUME-scautoqc-all
5. You can leave your screen session and let it run in the background: Ctrl+A, D

Files:

• main.nf - the Nextflow pipeline that executes scAutoQC pipeline.
• nextflow.config - the configuration script that allows the processes to be submitted to IBM LSF on Sanger's HPC and ensures correct environment is set via singularity container (this is an absolute path). Global default parameters are also set in this file and some contain absolute paths.
• RESUME-scautoqc-all - an example run script that executes the whole pipeline.
• RESUME-scautoqc-afterqc - an example run script that executes the pipeline after run_qc and find_doublets steps.
• RESUME-scautoqc-onlyqc - an example run script that executes the pipeline after until pooling step.
• bin/gather_matrices.py - a Python script that gathers matrices from STARsolo, Velocyto and Cellbender outputs (used in step 1).
• bin/qc.py - a Python script that runs automatic QC workflow (used in step 2).
• bin/flag_doublet.py - a Python script that runs scrublet to find doublets (used in step 3).
• bin/pool_all.py - a Python script that combines all of the output objects after QC step (used in step 4).
• bin/add_scrublet_meta.py - a Python script that adds scrublet scores (and metadata if available) (used in step 5).
• bin/integration.py - a Python script that runs scVI integration (used in step 6).
• genes_list/ - a folder that includes cell cycle, immunoglobulin and T cell receptor genes.
• Dockerfile - a dockerfile to reproduce the environment used to run the pipeline.

Workflow

This pipeline produces 10 outputs, each were detailed in their corresponding steps:

• [output 1]: h5ad object with different layers
• [output 2]: h5ad object with QC metrics
• [output 3]: QC plots for each sample
• [output 4]: CSV file with scrublet scores
• [output 5]: pooled h5ad object with all the samples
• [output 6]: pooled h5ad object with metadata
• [output 7]: QC plots
• [output 8]: CSV with QC metrics for each sample
• [output 9]: final integrated h5ad object
• [output 10]: ELBO plot from scVI training

The default version of the pipeline runs all the steps shown the diagram above. This pipeline has three run modes as shown on the right:

• all: runs all steps (1-2-3-4-5-6)
• only_qc: runs the steps until pooling including doublet finding (1-2-3)
• after_qc: runs the steps starting from pooling (4-5-6)

The parameters needed for all run modes are already specified in different RESUME scripts, and also can be found below:

Workflow: all

# to run all the steps
nextflow run main.nf \
  -entry all \            # to choose run mode
  --SAMPLEFILE /path/to/sample/file \
  --metadata /path/to/metadata/file \
  --ss_prefix /path/to/starsolo-results \
  --cb_prefix /path/to/cellbender-results \
  --ss_out Gene \         # to specify which STARsolo output folder to use (Gene or GeneFull)
  --project_tag test1 \   # to specify the run to add to the end of output folder (e.g. scautoqc-results-test1)
  --batch_key sampleID \  # batch key to use in scVI integration
  --ansi-log false \
  -resume

Workflow: only_qc

# to run all the steps before pooling
nextflow run main.nf \
  -entry only_qc \            # to choose run mode
  --SAMPLEFILE /path/to/sample/file \
  --metadata /path/to/metadata/file \
  --ss_prefix /path/to/starsolo-results \
  --cb_prefix /path/to/cellbender-results \
  --ss_out Gene \         # to specify which STARsolo output folder to use (Gene or GeneFull)
  --project_tag test1 \   # to specify the run to add to the end of output folder (e.g. scautoqc-results-test1)
  --batch_key sampleID \  # batch key to use in scVI integration
  --ansi-log false \
  -resume

Workflow: after_qc

# to run after qc steps 
nextflow run main.nf \
  -entry after_qc \            # to choose run mode
  --SAMPLEFILE /path/to/sample/file \
  --postqc_path /path/to/postqc/objects \
  --scrublet_path /path/to/scrublet/csvs \
  --metadata /path/to/metadata/file \
  --project_tag test1 \   # to specify the run to add to the end of output folder (e.g. scautoqc-results-test1)
  --batch_key sampleID \  # batch key to use in scVI integration
  --ansi-log false \
  -resume

1. gather_matrices

The inputs for the first step are determined according to STARsolo output which is specificed to use ss_out:

• By default, "Gene" folder is used (assuming the data is single-cell).
• "GeneFull" folder is used if the data is single-nuc.

This step requires three inputs:

• STARsolo output folder named "Gene" (or "GeneFull")
• STARsolo output folder named "Velocyto" (ignored if "GeneFull")
• Cellbender output in h5 format (if not provided, STARsolo outputs will be used only) (option to change mode will be added in the future)

gather_matrices step combines the matrices from three inputs into one h5ad object with multiple layers for each sample: raw, spliced, unspliced, ambiguous (only raw layer is used if "GeneFull" mode is specified). Main expression matrix, cell and gene metadata are retrieved from Cellbender output. Raw matrix is retrieved from the expression matrix of STARsolo output folder named Gene. Spliced, unspliced and ambiguous matrices are all retrieved from the expression matrices of STARsolo output folder named Velocyto.

This step produces:

• [output 1]: h5ad object with different layers

2. run_qc

This step requires the output of gather_matrices step which is the h5ad object with one or more layers.

run_qc step uses main automatic QC workflow which is summarised here. It applies the QC based on 8 QC metrics (log1p_n_counts, log1p_n_genes, percent_mito, percent_ribo, percent_hb, percent_top50, percent_soup, percent_spliced - last one is ignored if "GeneFull" mode is specified), and run CellTypist based on four models which are specified below and defined as default in this pipeline (Due to the nature of this study, only gut-related models were used):

• cecilia22_predH: CellTypist model from the immune populations combined from 20 tissues of 18 studies, includes 32 cell types (ref: Domínguez-Conde et al, 2022)
• cecilia22_predL: CellTypist model from the immune sub-populations combined from 20 tissues of 18 studies, includes 98 cell types (ref: Domínguez-Conde et al, 2022)
• elmentaite21_pred: CellTypist model from the intestinal cells from fetal, pediatric (healthy and Crohn's disease) and adult human gut, includes 134 cell types (ref: Elmentaite et al, 2021)
• suo22_pred: CellTypist model from the stromal and immune populations from the human fetus, includes 138 cell types (ref: Suo et al, 2022)
• megagut_pred: CellTypist model from the all cells in Pan-GI study, includes 89 cell types (ref: [Oliver et al, 2024 (in press)]).

This step produces [output 2] and [output 3]:

• [output 2]: an h5ad object with QC metrics, "good_qc_cluster" and "pass_auto_filter" columns tested with different mitochondrial thresholds,
• [output 3]: QC plots for each sample:
  • UMAP plots:
    • coloured by each metric and good_qc_cluster and qc_cluster
    • coloured by probability scores, uncertain and predicted labels using the first model (cecilia22_predH)
    • coloured by good_qc_cluster, pass_auto_filter, pass_default and qc_cluster
  • violin plot for each metric grouped by QC clusters
  • scatter plots for n_counts vs each metric:
    • coloured by good_qc_cluster
    • coloured by pass_default_filter
    • coloured by pass_auto_filter
    • coloured by the probability of a CellTypist prediction using the first model (cecilia22_predH)

3. find_doublets

This step requires the output of run_qc step which is the h5ad object with postqc columns.

find_doublets step runs scrublet on the h5ad object and annotates the doublet scores to cells. This step runs in parallel with step 4 for efficiency.

This step produces:

• [output 4]: CSV file with scrublet scores for each cell barcode for each sample.

4. pool_all

This step requires the outputs of run_qc step from all the samples.

pool_all step combines all of the objects produced in run_qc step in a single h5ad object.

This step produces:

• [output 5]: a concatenated h5ad object with all the samples

5. add_metadata

This step requires the h5ad output from pool_all and the scrublet csv outputs from find_doublets steps.

add_metadata step gives scores of different QC metrics of each sample, and adds scrublet scores (and the cell metadata according to samples if provided) to the h5ad object. This step also removes the samples with bad QC scores (passQC_frac80 lower than 0.1 and passQC_count80 lower than 100).

This step produces [output 6], [output 7], [output 8]:

• [output 6]: pooled h5ad object with metadata
• [output 7]: QC plots:
  • scatter plot of total_cell_count vs passQC_count80 coloured by each sample
  • histogram of log of samples that have passQC_count20, passQC_count50 and passQC_count80 columns True
• [output 8]: CSV with percentages of the cells passed QC for each test

6. integrate

This step requires the h5ad object from add_metadata step. integrate step removes stringent doublets applies scVI integration to all samples by using "sampleID" as a batch key (by default), and "log1p_n_counts" and "percent_mito" columns as categorical covariates. The final integrated object is given as the output of all of this pipeline. The steps below are applied before running integration:

• Stringent doublets (cells with doublet score higher than 0.3, and bh score lower than 0.05) are removed.
• 7500 highly variable genes are chosen.
• All cell cycle genes are removed.
• The dimensionality of the latent space is chosen as 20.
• The batch size is chosen as 256.

This step produces:

• [output 9]: final integrated h5ad object
• [output 10]: ELBO plot from scVI training

Future plans

Add run_cellbender process

• Current version of pipeline assumes that the Cellbender outputs exist.
• This addition will allow the pipeline to run Cellbender if the inputs do not exist.

Expand input options

• Current version of pipeline is able to take STARsolo outputs only.
• This expansion will allow user to specify the input type whether it is Cell Ranger or STARsolo input.
• For Cell Ranger inputs, the pipeline will be able to run Velocyto and Cellbender without specifying anything additionally.

Smart memory allocation

• This addition will estimate the average memory needed for pool_all step, so it won't need to try multiple times until it runs well.

Original workflow scheme

Changelog

For a version history/changelog, please see the CHANGELOG file.