anndata-object-annotation
Summary
Embed computed quality control metrics into AnnData objects at per-spot and per-metabolite resolution for spatial metabolomics data. This skill enables systematic QC tracking and downstream filtering by storing derived metrics in standardized AnnData slots (adata.obs and adata.var).
When to use
After loading or merging spatial metabolomics data into an AnnData object (via read_sm_csv_as_anndata, read_sm_imzml_as_anndata, or merge_sm_pos_neg), and before filtering or normalization steps. Use this skill when you need to compute intensity-based QC metrics (total intensity per spot, detection frequency per metabolite) and embed them as columns in the AnnData object for downstream quality control decisions.
When NOT to use
- AnnData object is not derived from spatial metabolomics data (e.g., it is from spatial transcriptomics only or bulk metabolomics without spatial coordinates)
- QC metrics have already been computed and stored in adata.obs and adata.var by a prior workflow step
- Input data is not in AnnData format or lacks proper spot/metabolite dimensional structure
Inputs
- Spatial metabolomics AnnData object (adata) with .X matrix populated and spot/metabolite identifiers in .obs_names and .var_names
Outputs
- Annotated AnnData object with per-spot QC metrics in adata.obs columns (e.g., total_intensity, n_detected_metabolites)
- Annotated AnnData object with per-metabolite QC metrics in adata.var columns (e.g., detection_frequency, mean_intensity)
How to apply
Load a spatial metabolomics AnnData object prepared by SpatialMETA's input functions. Apply spatialmeta.pp.calculate_qc_metrics_sm to compute per-spot metrics (e.g., total intensity, number of detected metabolites) and per-metabolite metrics (e.g., detection frequency, average intensity). The function automatically stores per-spot metrics as new columns in adata.obs and per-metabolite metrics as new columns in adata.var. Inspect the resulting annotated AnnData object to verify that QC metric columns are present and populated with numeric values before proceeding to filter_cells_sm or filter_metabolites_sm steps.
Related tools
- spatialMETA (Provides the calculate_qc_metrics_sm function and integrated preprocessing workflow for spatial metabolomics quality control metric computation on AnnData objects) — https://github.com/WanluLiuLab/SpatialMETA
Examples
import spatialmeta as sm; adata = sm.pp.read_sm_csv_as_anndata('metabolomics.csv'); adata = sm.pp.calculate_qc_metrics_sm(adata); print(adata.obs.head())
Evaluation signals
- adata.obs contains new numeric columns (e.g., 'total_intensity', 'n_detected_metabolites') with non-null values for all spots
- adata.var contains new numeric columns (e.g., 'detection_frequency', 'mean_intensity') with non-null values for all metabolites
- Per-spot total intensity is non-negative and sums match the row-wise sum of adata.X
- Per-metabolite detection frequency is between 0 and 1 (or 0 and 100 if percentages), and correlates with sparsity patterns in adata.X
- Spot and metabolite metric columns are queryable and filterable for downstream QC workflows (e.g., filter_cells_sm or filter_metabolites_sm)
Limitations
- Metrics are computed in-place on the full AnnData object; if metrics must be recomputed with different parameters or on subsets, the function must be re-run
- The function assumes the input AnnData object is well-formed with proper dimensionality; malformed or incomplete objects may produce NaN or misleading metric values
- Metrics are additive (total intensity, detection counts) and do not account for metabolite mass, retention time, or other chemical properties that may be relevant for domain-specific QC decisions
Evidence
- [other] Apply spatialmeta.pp.calculate_qc_metrics_sm to compute QC metrics at the spot level (e.g., total intensity, number of detected metabolites per spot) and metabolite level (e.g., detection frequency, average intensity per metabolite).: "Apply spatialmeta.pp.calculate_qc_metrics_sm to compute QC metrics at the spot level (e.g., total intensity, number of detected metabolites per spot) and metabolite level (e.g., detection frequency,"
- [other] Store computed metrics in adata.obs (per-spot) and adata.var (per-metabolite) columns.: "Store computed metrics in adata.obs (per-spot) and adata.var (per-metabolite) columns."
- [other] SpatialMETA includes a calculate_qc_metrics_sm preprocessing function that computes quality control metrics at the per-spot and per-metabolite level on spatial metabolomics AnnData objects as part of the integrated workflow.: "SpatialMETA includes a calculate_qc_metrics_sm preprocessing function that computes quality control metrics at the per-spot and per-metabolite level on spatial metabolomics AnnData objects as part of"
1---2name: anndata-object-annotation3description: Use when after loading or merging spatial metabolomics data into an AnnData object (via read_sm_csv_as_anndata, read_sm_imzml_as_anndata, or merge_sm_pos_neg), and before filtering or normalization steps.4license: CC-BY-4.05---67# anndata-object-annotation89## Summary1011Embed computed quality control metrics into AnnData objects at per-spot and per-metabolite resolution for spatial metabolomics data. This skill enables systematic QC tracking and downstream filtering by storing derived metrics in standardized AnnData slots (adata.obs and adata.var).1213## When to use1415After loading or merging spatial metabolomics data into an AnnData object (via read_sm_csv_as_anndata, read_sm_imzml_as_anndata, or merge_sm_pos_neg), and before filtering or normalization steps. Use this skill when you need to compute intensity-based QC metrics (total intensity per spot, detection frequency per metabolite) and embed them as columns in the AnnData object for downstream quality control decisions.1617## When NOT to use1819- AnnData object is not derived from spatial metabolomics data (e.g., it is from spatial transcriptomics only or bulk metabolomics without spatial coordinates)20- QC metrics have already been computed and stored in adata.obs and adata.var by a prior workflow step21- Input data is not in AnnData format or lacks proper spot/metabolite dimensional structure2223## Inputs2425- Spatial metabolomics AnnData object (adata) with .X matrix populated and spot/metabolite identifiers in .obs_names and .var_names2627## Outputs2829- Annotated AnnData object with per-spot QC metrics in adata.obs columns (e.g., total_intensity, n_detected_metabolites)30- Annotated AnnData object with per-metabolite QC metrics in adata.var columns (e.g., detection_frequency, mean_intensity)3132## How to apply3334Load a spatial metabolomics AnnData object prepared by SpatialMETA's input functions. Apply spatialmeta.pp.calculate_qc_metrics_sm to compute per-spot metrics (e.g., total intensity, number of detected metabolites) and per-metabolite metrics (e.g., detection frequency, average intensity). The function automatically stores per-spot metrics as new columns in adata.obs and per-metabolite metrics as new columns in adata.var. Inspect the resulting annotated AnnData object to verify that QC metric columns are present and populated with numeric values before proceeding to filter_cells_sm or filter_metabolites_sm steps.3536## Related tools3738- **spatialMETA** (Provides the calculate_qc_metrics_sm function and integrated preprocessing workflow for spatial metabolomics quality control metric computation on AnnData objects) — https://github.com/WanluLiuLab/SpatialMETA3940## Examples4142```43import spatialmeta as sm; adata = sm.pp.read_sm_csv_as_anndata('metabolomics.csv'); adata = sm.pp.calculate_qc_metrics_sm(adata); print(adata.obs.head())44```4546## Evaluation signals4748- adata.obs contains new numeric columns (e.g., 'total_intensity', 'n_detected_metabolites') with non-null values for all spots49- adata.var contains new numeric columns (e.g., 'detection_frequency', 'mean_intensity') with non-null values for all metabolites50- Per-spot total intensity is non-negative and sums match the row-wise sum of adata.X51- Per-metabolite detection frequency is between 0 and 1 (or 0 and 100 if percentages), and correlates with sparsity patterns in adata.X52- Spot and metabolite metric columns are queryable and filterable for downstream QC workflows (e.g., filter_cells_sm or filter_metabolites_sm)5354## Limitations5556- Metrics are computed in-place on the full AnnData object; if metrics must be recomputed with different parameters or on subsets, the function must be re-run57- The function assumes the input AnnData object is well-formed with proper dimensionality; malformed or incomplete objects may produce NaN or misleading metric values58- Metrics are additive (total intensity, detection counts) and do not account for metabolite mass, retention time, or other chemical properties that may be relevant for domain-specific QC decisions5960## Evidence6162- [other] Apply spatialmeta.pp.calculate_qc_metrics_sm to compute QC metrics at the spot level (e.g., total intensity, number of detected metabolites per spot) and metabolite level (e.g., detection frequency, average intensity per metabolite).: "Apply spatialmeta.pp.calculate_qc_metrics_sm to compute QC metrics at the spot level (e.g., total intensity, number of detected metabolites per spot) and metabolite level (e.g., detection frequency,"63- [other] Store computed metrics in adata.obs (per-spot) and adata.var (per-metabolite) columns.: "Store computed metrics in adata.obs (per-spot) and adata.var (per-metabolite) columns."64- [other] SpatialMETA includes a calculate_qc_metrics_sm preprocessing function that computes quality control metrics at the per-spot and per-metabolite level on spatial metabolomics AnnData objects as part of the integrated workflow.: "SpatialMETA includes a calculate_qc_metrics_sm preprocessing function that computes quality control metrics at the per-spot and per-metabolite level on spatial metabolomics AnnData objects as part of"
HolobiomicsLab