Configuration

This page explains how to fully set up the config files to run SpaceBlocks. Full documentation can be found at https://cbib.github.io/SpaceBlocks/configuration/. If you are visualizing the documentation from the Snakemake catalog we recommend visiting the SpaceBlocks GitHub, since the rendered tube map does not include the HeadBlocks and is not consistently colored.

Copy config/config.yaml.template to config/config.yaml before configuring a run. The template contains all parameters, while the committed config/config.yaml is only the runnable fixture used by the Snakemake Workflow Catalog.

The explanations on this page are divided by type (directory, parameter, color, etc.). In the config file, parameters are divided by Block and function.

Every key in config/config.yaml is validated against workflow/schemas/config.schema.yaml before the run starts, so a typo or a missing required field fails immediately with a clear message.

Complete list of SpaceBlocks parameters

The full, always-updated table of every parameter (type, default, required) is generated automatically from the schema and shown on the workflow's Snakemake-catalog page. This page covers the how and the why; the schema is the exhaustive reference.

1. Choose a mode

The config mode selects which Headblock builds the standardized contract h5ad. In decoupled mode, SpaceBlocks consumes contract h5ads that already exist.

Space Ranger installation

The Visium HD HeadBlock additionally needs an external Space Ranger installation.

config["mode"]	Head that runs	Input
visiumhd	spaceranger_count_vhd → generate_qupath_vhd → prepare_input_vhd	10x Visium HD fastqs + Space Ranger
xenium5k	convert_zarr_x5k → generate_qupath_x5k → prepare_input_x5k	Xenium output bundle
atera	convert_zarr_ate → generate_qupath_ate → prepare_input_ate	Atera (alpha) output bundle; optional registered H&E inputs
merscope	generate_qupath_mer → prepare_input_mer	MERSCOPE region directory
decoupled	(none)	Pre-existing contract h5ad files you provide

The analysis CoreBlocks are identical in all five cases. Atera support is currently in alpha because the public preview format may change before commercial release.

2. Paths, files and sample sheets

The SpaceBlocks config/config.yaml sets the input and output directories, and gets the sample information and metadata (for integration and plotting) from one or two TSV files, depending on the config["mode"] set.

Setting a base directory

base_dir is the project-root prefix for generated output directories. In the shipped template, most output folders are written relative to it, but several required inputs are intentionally left as explicit filesystem paths because they point to external software or reference data.

Paths in SpaceBlocks fall into three categories:

• Project output paths: typically relative to base_dir or kept under the config directory for metadata files.
• Project input paths: configuration or pre-computed data used by the pipeline. These can be defined relative to config/ or as absolute paths.
• External absolute paths: installed tools, references, and other files that must be supplied explicitly.

Project output paths

These are the paths that are normally defined relative to base_dir.

Path / File	Key	Condition	Meaning
Path	post_processing_outdir	Mandatory	Output directory root; it will contain the result folders for the run.
Path	logdir	Mandatory	Directory for per-rule logs and benchmarks.
Path	spaceranger_processing_outdir	Head modes	Directory where the head writes heavy intermediates such as Space Ranger outputs or SpatialData Zarr stores.

Project input paths

These paths contain metadata, configuration or pre-processed results that are ingested by the pipeline. We provide some defaults under the config/config.yaml.template. Others are defined as placeholders /path/to/dir/ and /path/to/file.ext, so make sure to configure them if needed.

Path / File	Key	Condition	Meaning
File	core_samples	Mandatory	Sample metadata sheet. The shipped template keeps this in config/ as project metadata.
Path	contract_dir	mode: decoupled	Directory holding the pre-existing contract h5ads to analyse.
Path	xenium5k.xenium_dir	mode: xenium5k	Directory pattern for each Xenium bundle.
Path	atera.atera_dir	mode: atera	{sample} path pattern to each Atera outs/ bundle.
Path	merscope.merscope_dir	mode: merscope	{sample} path pattern to each MERSCOPE region directory.
Path	geojson_path	Optional, recommended	Directory containing the GeoJSONs that annotate spatial regions for each sample (see the QuPath tutorial). In decoupled mode this directory is not searched; region labels must already be in the contract h5ad.
Path	precomputed_metadata_dir	Optional (reproducibility)	Directory of precomputed per-sample metadata TSVs. The metadata can contain precomputed clusters and/or external annotations.
Path	spatial_niches.niche_dir	Optional (reproducibility)	Directory for precomputed niche TSVs when use_precomputed is enabled.
File	per_sample_qc	Optional (per-sample filters)	TSV of sample-specific QC filters. If empty, the same analysis.* thresholds apply to every sample (default).
File	snakemake_cell_markers	Optional (pre-annotation)	TSV of canonical marker genes drawn on the Leiden diagnostic plots (leiden_analysis) to guide manual annotation. If not set, default markers are used (config/snakemake_cell_markers.tsv)
File	gene_exploration.queries	Exploration Coreblock	Genes / gene sets to score (AUCell) and plot in the exploration block. If not set, test gene queries are used (config/gene_queries.tsv).
File	cluster_annotations	Optional	TSV of cluster-to-cell-type labels used during annotation. Template ships with a template of placeholder annotations (config/cluster_annotations.tsv). If not set, cell will be labeled Unannotated

GeoJSON filenames

Region annotation is optional per sample, but a sample-prefixed GeoJSON with an unsupported suffix is treated as an error. Expected suffixes are _tissue_hires_image.geojson for Visium HD, _morphology.geojson for Xenium and MERSCOPE, and either _he_background.geojson or _morphology.geojson for Atera.

Explicit external inputs

These are not derived from base_dir and must be set explicitly to the correct filesystem location.

Path / File	Key	Condition	Meaning
File	spaceranger	mode: visiumhd	Path to the externally installed Space Ranger executable.
File	probe_set	mode: visiumhd	Probe set CSV needed to run Space Ranger.
File	transcriptome	mode: visiumhd	Reference transcriptome needed to run Space Ranger.
File	ingest_ref	Optional (auto-annotation)	Annotated h5ad scRNA-seq reference for ingest_ref.
File	samples	mode: visiumhd	Sample sheet with fastq directories, slide, and capture area per sample.

Sample sheets

core_samples.tsv is the technology-agnostic sample sheet used by the CoreBlocks in every use case. This file must contain a column named sample. Every other column is stamped into each sample's obs, so it can be used by integration plots and pseudobulk group_by, covariates, or paired_by settings

Visium HD additionally uses visiumhd_samples.csv for fastq, slide, and capture-area information.

For Xenium, Atera, and MERSCOPE, the sample list comes from core_samples, and each input bundle is located with the corresponding {sample} path pattern.

Color customization for result visualization

You can customize the color scale for any sample metadata in core_samples.tsv as additional columns. See section 3 and section 4 for details.

Sample sheet	Condition	Role
config/core_samples.tsv	Mandatory	The technology-agnostic anchor: one row per sample. The first column is the sample name; any further columns are design metadata (e.g. patient, condition, type) that are stamped into obs and can be surfaced downstream.
config/visiumhd_samples.csv	mode: visiumhd	The Visium HD head sheet: fastq directories (plus any re-sequencing runs), slide, and area per sample.

3. Common parameters

Parameters are settings that live exclusively in config/config.yaml and determine the details of the run.

Parameter	Condition	Role
mode	Mandatory	Which headblock builds the contract (see section 1).
random_seed	Reproducibility	Seed for stochastic steps, including PCA, UMAP, Leiden, subclustering, neighbours, and sketching. Results may still differ across software stacks or systems (see section 6).
use_precomputed_clusters	Optional (reproducibility)	If true, reuse Leiden clusters/metadata from precomputed_metadata_dir instead of recomputing them.
ingest_ref_label_key	Annotated reference	Column in the ingest_ref reference that holds the reference cell-type labels.
integration.integrate_key	Mandatory	Variable Harmony corrects over during integration (e.g. sample).
annotation_types	[external_annotation] when external annotation is enabled; otherwise [tsv_annotation]	Annotation variants expanded by pseudobulk and neighbourhood analyses. Allowed values are tsv_annotation, external_annotation, and ingest_annotation. Add ingest_annotation explicitly if those analyses should use transferred labels.
extra_annotations.columns	Optional	core_samples.tsv columns to be used in downstream plots (e.g. [patient, batch]). All sample-sheet columns are carried into obs and can be used by pseudobulk models whether or not they are plotted.
analysis.run_leiden_analysis	Optional, default to true	Controls the per-resolution diagnostic plot rule. Leiden clusters are still computed by preprocess_umap.
analysis.run_pseudobulk_de	Optional false in runtime/schema; true in the template	Enables DESeq2 differential expression. Pseudobulk aggregation may still run when DE is disabled.
analysis.pseudobulk.analyses	Pseudobulk	Named aggregation/model specifications containing the comparison variables, explicit contrasts, exclusions, optional pairing/covariates, one-vs-rest biomarkers, and optional LRT.
analysis.umap_point_size	Optional	Marker size for ordinary UMAP embedding plots (default: 2). Highlight and split UMAPs remain at least size 10.
analysis.spatial_point_size	Optional	Marker size for spatial maps (default: 20). This is independent of the UMAP marker size. Coordinate-only spatial-niche maps are scaled proportionally.

4. Color scale customization

In SpaceBlocks, color scales are fully customizable and consistent across analysis plots.

Undefined variable levels are colored in grey

When customizing color palettes, levels not explicitly listed fall back to grey #cccccc, so a value that renders grey usually means a missing key. If you wish to customize visualization, list all levels in a variable and assign a color for each of them.

To set a given color scale, you just need to specify the HEX color code for each level in config/config.yaml. There are three palette families, each keyed by column → level → hex:

# core_samples.tsv columns: column -> level -> colour
sample_colors:
  patient:
    "Patient 1": "#000000"
    "Patient 2": "#E69F00"
  condition:
    Control: "#0072B2"
    Treatment: "#D55E00"
  batch:
    "Batch 1": "#8E44AD"

# annotation obs columns: column -> level -> colour
annotation_colors:
  cell_type_tsv:
    Tcells: "#1f77b4"
    Fibroblasts: "#2ca02c"
  cell_type_external:       # not inherited from cell_type_tsv
    Tcells: "#1f77b4"
  cell_type_ingest:
    Tcells: "#1f77b4"
  spatial_niche:            # optional override of the deterministic niche palette
    "0": "#4C78A8"

analysis:
  # Preferred display order. For a single region group in pseudobulk,
  # this is also the fallback when condition_order is absent.
  region_levels: ["Tumor area", "Healthy area"]
  # Region level -> colour
  region_colors:
    "Tumor area": "#46337EFF"
    "Healthy area": "#FDE725FF"

Spatial niches are coloured automatically from a deterministic palette unless you add a spatial_niche block under annotation_colors.

region_levels controls ordering, but it does not filter observations. For pseudobulk analyses, you may use exclude_levels to remove values.

5. Key configuration sections

The rest of the configuration lives in comment-separated config chunks. Files and single parameters are covered in section 2 and section 3; colour palettes in section 4.

Section	Condition	What it configures
visiumhd	mode: visiumhd	Visium HD head settings: spaceranger, probe_set, and transcriptome.
xenium5k	mode: xenium5k	Xenium head settings: xenium_dir, pyramid levels, and pixel_size_um.
atera	mode: atera	Atera head settings: atera_dir, Zarr and pyramid options, plus optional registered H&E image, alignment, and keypoint patterns.
merscope	mode: merscope	MERSCOPE head settings: merscope_dir, selected z-plane, embedded-image resolution, and image channels.
contract	Optional, defaults are implemented	Contract keys such as sample_key, spatial_key, require_region, require_raw_counts, and mito_prefix.
qc_sweep	Optional	Candidate-threshold QC diagnostics (never filters).
external_annotation	Optional	Overlay labels from an external tool: enabled, column, keep_unannotated (see section 6).
analysis	Required	The bulk of the run: QC filters (min_counts / min_genes / min_cells / max_counts / max_pct_mt), the Leiden resolution_scan_*, named pseudobulk analyses + thresholds, and the run_* toggles. Per-sample QC overrides come from per_sample_qc.
spatial_niches	Optional	BANKSY niche detection across concatenated samples.
subcompartments	Optional	Named cell-type subsets to re-cluster in subcluster.
gene_exploration	Exploration CoreBlock	Genes / gene sets to score (AUCell) and plot in the exploration block, plus its niche_column and rank fraction.
annotation_colors	Color customization	Annotations for samples, conditions, regions and other variables.
resources	Required	Per-rule mem_mb / runtime / threads (with a default). Memory scales with the retry attempt, so an OOM-killed job is resubmitted with more RAM.

Note

A few one-key blocks are documented elsewhere for readability: integration (integrate_key) and extra_annotations (columns) are parameters in section 3; cluster_annotations is a file in section 2; and the colour blocks (sample_colors, annotation_colors, analysis.region_colors) are in section 4.

Spatial niches

spatial_niches.enabled: true runs BANKSY jointly across the preprocessed samples. The physical-neighbour and clustering-neighbour parameters are independent:

Key	Default	Meaning
lambda	0.8	Weight of spatial-neighbour features in the BANKSY matrix.
num_neighbours	18	Physical neighbours used to construct BANKSY features.
max_m	1	Highest spatial-neighbourhood harmonic used by BANKSY.
nbr_weight_decay	scaled_gaussian	BANKSY neighbour-weight decay.
use_hvg	false	Restrict BANKSY to highly variable genes. The shipped configs set this to true.
n_top_genes	analysis.n_top_genes, otherwise 2000	Strongly recommended due to heavy RAM usage. Number of HVGs when use_hvg is enabled.
cluster_n_neighbours	50	Neighbours in the separate Harmony-corrected BANKSY PCA graph used for clustering.
niche_resolution	0.1	Leiden resolution controlling niche granularity.
n_iterations	-1	Leiden iterations; -1 runs until convergence and may take longer on very large datasets.
compute_umap	false	Whether to compute the optional integrated BANKSY UMAP.
refine	false	Apply spatial majority-vote refinement to computed or reused labels.

The BANKSY matrix is dense and has approximately (2 + max_m) × n_genes features. For large datasets, use use_hvg: true and tune n_top_genes to control memory.

With use_precomputed: true, SpaceBlocks skips BANKSY/Leiden and reloads a complete niche_<sample>.tsv for every sample. Missing files or cells will prevent the rule from running.

Pseudobulk experimental designs

Pseudobulk analyses are configured under analysis.pseudobulk.analyses. Each analysis has a stable name, one aggregation, one or more group_by columns, optional covariates and paired_by, exclusions, explicit pairwise contrasts, and optional one-vs-rest and LRT tests.

aggregation	One pseudobulk observation per	Separate result set per
all_cells	sample	—
by_celltype	sample and cell type	cell type
by_region	sample and region	—
by_celltype_region	sample, cell type and region	cell type

cell_type is a portable alias: SpaceBlocks resolves it to the cell-type column for the active annotation_types entry, so manual TSV, external, and ingest-transferred cell-type annotations can use the same pseudobulk configuration.

condition_order optionally sets the displayed order of comparison-group labels. With multiple group_by columns, use the combined labels written to pseudobulk metadata, for example condition=Control | treatment=Drug. Unlisted observed levels are appended.

Independent phenotype comparison

Add the experimental variables to core_samples.tsv:

sample  phenotype   batch
Cancer_P1   Cancer  Batch1
Cancer_P2   Cancer  Batch2
Cancer_P3   Cancer  Batch3
Normal_P4   Normal  Batch1
Normal_P5   Normal  Batch2
Normal_P6   Normal  Batch3

Then compare phenotypes for each cell type while adjusting for batch:

analysis:
  run_pseudobulk_de: true
  min_replicates: 3
  pseudobulk:
    analyses:
      - name: phenotype_by_celltype
        aggregation: by_celltype
        group_by: [phenotype]
        covariates: [batch]
        exclude_levels:
          cell_type: [Unannotated]
        contrasts:
          - name: cancer_vs_normal
            numerator: {phenotype: Cancer}
            denominator: {phenotype: Normal}
        lrt:
          enabled: false

The numerator determines the positive log2-fold-change direction. Levels not named in a pairwise contrast can remain in the fitted model and help dispersion estimation. Use exclude_levels only for values that should be removed from the analysis entirely. The exclusions are also applied to one-vs-rest tests and the LRT.

Here P1–P6 are independent samples, so paired_by is omitted. Use covariates only when the design supports estimating them without confounding. Use aggregation: all_cells for one whole-sample result rather than a result per cell type.

Phenotype and treatment

Multiple group_by columns form a combined categorical group. In the example below, Cancer and Normal phenotype levels are combined with Drug and Vehicle treatment levels. This supports clear comparisons between observed combinations without exposing interaction formulas:

- name: treatment_by_celltype
  aggregation: by_celltype
  group_by: [phenotype, treatment]
  exclude_levels:
    cell_type: [Unannotated]
  contrasts:
    - name: drug_vs_vehicle_in_cancer
      numerator: {phenotype: Cancer, treatment: Drug}
      denominator: {phenotype: Cancer, treatment: Vehicle}

This tests one combination against another. It is not a formal interaction or difference-of-differences test.

Paired regions or matched samples

Use paired_by: sample when the same sample contributes both levels of a contrast, as in tumour and healthy regions from the same section:

- name: regions_by_celltype
  aggregation: by_celltype_region
  group_by: [region_annotation]
  paired_by: sample
  exclude_levels:
    region_annotation: [Unlabeled, Bubble]
    cell_type: [Unannotated]
  contrasts:
    - name: tumor_vs_healthy
      numerator: {region_annotation: Tumor area}
      denominator: {region_annotation: Healthy area}
  one_vs_rest:
    enabled: true
  lrt:
    enabled: true

For a paired Wald contrast, SpaceBlocks retains only matched units containing both requested levels and interprets min_replicates as the minimum number of complete pairs.

For separate samples matched by patient, add the shared patient identifier to core_samples.tsv and set paired_by: patient, as in this example:

sample  phenotype   patient
Cancer_P1   Cancer  P1
Normal_P1   Normal  P1
Cancer_P2   Cancer  P2
Normal_P2   Normal  P2
Cancer_P3   Cancer  P3
Normal_P3   Normal  P3

- name: matched_phenotype_by_celltype
  aggregation: by_celltype
  group_by: [phenotype]
  paired_by: patient
  contrasts:
    - name: cancer_vs_normal
      numerator: {phenotype: Cancer}
      denominator: {phenotype: Normal}

Thus, use paired_by: sample for multiple region levels from the same spatial sample, paired_by: patient for separate samples matched by patient, and omit paired_by for independent Cancer-versus-Normal samples.

Repeated observations without pairing

It is generally a good idea to used paired contrasts when observations can be considered repeated measurements from the same experimental unit. Thus, if no paired_by is configured and a sample contributes pseudobulks to more than one comparison level, DE for that pooled or cell-type subgroup is aborted by default because those observations are not independent. The script records the reason in that subgroup's ERROR.txt, so it does not silently omit the repeated samples.

For an explicitly exploratory analysis, set allow_repeated_unpaired: true and leave paired_by unset. The model then treats the repeated pseudobulks as independent, ignores within-sample correlation, and may underestimate standard errors. When repeated observations are detected, the analysis writes WARNING_repeated_unpaired.txt. paired_by and allow_repeated_unpaired: true cannot be combined.

Multiple level designs

Multiple level designs unlock the possibility of (1) running likelihood ratio tests (LRT) for holistic comparisons across experimental levels and (2) one-vs-rest Wald tests to identify potential gene markers for each of the levels. SpaceBlocks provides native implementation of these statistical tests, explained below.

When lrt.enabled is true and at least three eligible comparison-group levels exist, SpaceBlocks runs an omnibus LRT test (using DESeq2 R implementation) followed by gene clustering using the DEGpatterns R package. This is carried out only when sufficient level and observations remain after exclusions, replicate filtering, and pairing. This is performed following the tutorial from Harvard Chan Bioinformatics Core workshop. With paired_by, the full model includes the paired unit term and the reduced model removes only the comparison group. With fewer than three eligible levels, LRT and DEGpatterns are skipped for that subgroup or cell type, and the reason is written to the pseudobulk rule log and LRT/SKIPPED_insufficient.txt. When the LRT does run and finds enough significant genes, DEGpatterns groups their expression profiles. Note that no order or trend direction is imposed by the configuration. Results are written under pseudobulk/.../LRT/.

When one_vs_rest.enabled is true, SpaceBlocks also tests each eligible level against all other eligible levels combined. It uses the same exclusions explained for the LRT test. Results are written under pseudobulk/.../one_vs_rest/, including a TSV, volcano plot, and heatmaps for each level. unique_markers.tsv contains genes significantly upregulated for one level but not for any other one-vs-rest comparison; the accompanying barplot and heatmap summarize these level-specific biomarkers. This is independent of the omnibus LRT and configured pairwise contrasts.

6. Advanced: Reproducibility and reusability

Even though we have ensured the highest reproducibility standards when creating SpaceBlocks, some steps are never 100% reproducible between systems (e.g. UMAP calculation, Leiden clustering).

We therefore provide features for minimal file sharing/storage that tighten the reproducibility gap.

SpaceBlocks allows you to input:

• Externally assembled h5ad AnnData objects — run mode: decoupled and point contract_dir at the directory of pre-built contract h5ads. The heads are skipped; the core validates and analyses them directly.
• Pre-computed clusters / annotations — set use_precomputed_clusters: true and provide metadata_<sample>.tsv files in precomputed_metadata_dir.
• Precomputed niches: set spatial_niches.enabled: true, spatial_niches.use_precomputed: true, and optionally spatial_niches.niche_dir.
• External cell labels: enable external_annotation as described below.

External annotation

external_annotation:
  enabled: true
  column: manual_celltype
  keep_unannotated: true

For every sample, provide column in precomputed_metadata_dir/metadata_<sample>.tsv. In decoupled mode only, the source column may instead be stored directly in each contract h5ad's obs when no external metadata directory is configured. SpaceBlocks normalizes the source to cell_type_external downstream.

• With keep_unannotated: true, normal QC is applied and unmatched cells remain as Unannotated. A mixed real/Unannotated column remains active; a placeholder-only column is omitted from annotation plots.
• With keep_unannotated: false, preprocessing retains only cells with real external labels and skips the configured cell-QC thresholds. Genes expressed in no retained cell are still removed.

When external annotation is enabled, it becomes the primary annotation used by reports, integration summaries, and gene exploration. If ingest_ref is also configured, both cell_type_external and cell_type_ingest are retained. Pseudobulk and neighbourhood analyses use only the variants listed in annotation_types; they do not automatically add ingest labels.

annotate_cells can operate without a manual cluster mapping and will set cell_type_tsv to Unannotated. However, the current default-target assembly still uses a readable, non-empty cluster_annotations file as the gate for annotation-dependent downstream targets. Keep such a file configured when running rule all, even for an external-primary analysis.

7. Mode-specific examples

The commented config/config.yaml.template is the full template. Here, these snippets show only the keys that distinguish each mode, and you may combine them with the common settings from the template.

Visium HD

mode: visiumhd
samples: config/visiumhd_samples.csv
spaceranger: /path/to/spaceranger
probe_set: /path/to/probe_set.csv
transcriptome: /path/to/refdata-gex
spaceranger_processing_outdir: "{base_dir}/spaceranger_results"

Xenium 5K

mode: xenium5k
xenium5k:
  xenium_dir: "/path/to/xenium/{sample}"
  qupath_pyramid_level: 3
  hires_pyramid_level: 3
  pixel_size_um: 0.2125

Atera (alpha support)

mode: atera
atera:
  atera_dir: "/path/to/atera/{sample}/outs"
  qupath_pyramid_level: 3
  hires_pyramid_level: 3
  pixel_size_um: 0.2125
  he_image: ""       # optional {sample} pattern; set with he_alignment
  he_alignment: ""   # optional {sample} pattern; set with he_image
  he_keypoints: ""   # optional {sample} pattern

MERSCOPE

mode: merscope
merscope:
  merscope_dir: "/path/to/merscope/{sample}"
  z_index: 3
  hires_pixel_size_um: 1.0
  channels: [DAPI, PolyT, Cellbound1, Cellbound2, Cellbound3]

Pre-built contracts

mode: decoupled
contract_dir: /path/to/contract_h5ads     # one <sample>.h5ad file per sample

8. Minimal decoupled example

The following is a schema-valid structural skeleton. Replace the paths and resource values for your environment; each sample listed in core_samples must have <contract_dir>/<sample>.h5ad.

mode: "decoupled"
core_samples: "config/core_samples.tsv"
contract_dir: "/path/to/contract_h5ads"
geojson_path: "/path/to/geojson"
precomputed_metadata_dir: "/path/to/precomputed_metadata/"
post_processing_outdir: "{base_dir}/results"
# ... see config/config.yaml.template for the full, commented template.

For a normal run, start by copying config/config.yaml.template to config/config.yaml, then adjust the paths and sample sheets to your data.

You may next read the get started documentation and the public data end-to-end example runs.