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Add MT percentage filtering #97

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merged 9 commits into from
Sep 25, 2024
Merged

Add MT percentage filtering #97

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d59c27f
Add mito_fraction from sample input
tbrittoborges Sep 9, 2024
736ef28
Add support to celltypist model.convert via `celltypist_map_file` par…
tbrittoborges Sep 24, 2024
eb1f386
Merge branch 'dev' into mitochondrial_fraction
tbrittoborges Sep 24, 2024
c36b044
Max mitochondrial fraction. Fix #97
tbrittoborges Sep 24, 2024
ce28887
prettier usage.md
tbrittoborges Sep 24, 2024
8ed757b
rm celltypist_map_file changes
tbrittoborges Sep 24, 2024
93cca56
lint modules.json
tbrittoborges Sep 24, 2024
22b6935
prettier modules.json
tbrittoborges Sep 24, 2024
456d47e
Add celltypist model parameter link
nictru Sep 25, 2024
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8 changes: 8 additions & 0 deletions assets/schema_input.json
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Expand Up @@ -88,6 +88,14 @@
"errorMessage": "Minimum number of counts per gene must be an integer greater than 0.",
"meta": ["min_counts_gene"]
},
"max_mito_fraction": {
"type": "integer",
"minimum": 0,
"maximum": 100,
"default": 100,
"errorMessage": "Max mitochondrial fraction must be an integer between 0 and 100.",
"meta": ["max_mito_fraction"]
},
"expected_cells": {
"type": "integer",
"minimum": 1,
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33 changes: 19 additions & 14 deletions docs/usage.md
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Expand Up @@ -46,20 +46,21 @@ sample3,/absolute/path/to/sample3_filtered.csv,/absolute/path/to/sample3.csv,,,,

For CSV input files, specifying the `batch_col`, `label_col`, and `unknown_label` columns will not have any effect, as no additional metadata is available in the CSV file.

| Column | Description |
| ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `sample` | Unique sample identifier. Will be added to the pipeline output objects as `sample` column. |
| `filtered` | May contain paths to `h5ad`, `h5`, `rds`, or `csv` files. `rds` files may contain any object that can be converted to a `SingleCellExperiment` using the [Seurat `as.SingleCellExperiment`](https://satijalab.org/seurat/reference/as.singlecellexperiment) function. `csv` files should contain a matrix with genes as columns and cells as rows. |
| `unfiltered` | Same as `file`, but for the unfiltered cellranger or nf-core/scrnaseq output. If not provided, only `decontX` can be used for ambient RNA removal. |
| `batch_col` | Column in the input file containing batch information. Defaults to `batch`. If the column does not exist in the input object, the pipeline will create a new column and put the sample identifier in it. If the `batch_col` is something else than `batch`, it will be renamed to `batch` during pipeline execution. |
| `symbol_col` | Column in the input file containing gene symbol information. Defaults to `index`. There are two special values that can be used: `index` and `none`. `index` will use the row names of the matrix as gene symbols. `none` will trigger the pipeline to perform gene symbol conversion (this is not supported yet). The values from `symbol_col` will be copied to a column `gene_symbols` during pipeline execution. |
| `label_col` | Column in the input file containing cell type information. Defaults to `label`. If the column does not exist in the input object, the pipeline will create a new column and put `unknown` in it. If the `label_col` is something else than `label`, it will be renamed to `label` during pipeline execution. |
| `unknown_label` | Value in the `label_col` column that should be considered as unknown. Defaults to `unknown`. If the `unknown_label` is something else than `unknown`, it will be renamed to `unknown` during pipeline execution. If trying to perform integration with scANVI, more than one unique label other than `unknown` must exist in the input data. |
| `min_genes` | Minimum number of genes required for a cell to be considered. Defaults to `1`. |
| `min_cells` | Minimum number of cells required for a gene to be considered. Defaults to `1`. |
| `min_counts_cell` | Minimum number of counts required for a cell to be considered. Defaults to `1`. |
| `min_counts_gene` | Minimum number of counts required for a gene to be considered. Defaults to `1`. |
| `expected_cells` | Number of expected cells, used as input to Cellbender. |
| Column | Description |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `sample` | Unique sample identifier. Will be added to the pipeline output objects as `sample` column. |
| `filtered` | May contain paths to `h5ad`, `h5`, `rds`, or `csv` files. `rds` files may contain any object that can be converted to a `SingleCellExperiment` using the [Seurat `as.SingleCellExperiment`](https://satijalab.org/seurat/reference/as.singlecellexperiment) function. `csv` files should contain a matrix with genes as columns and cells as rows. |
| `unfiltered` | Same as `file`, but for the unfiltered cellranger or nf-core/scrnaseq output. If not provided, only `decontX` can be used for ambient RNA removal. |
| `batch_col` | Column in the input file containing batch information. Defaults to `batch`. If the column does not exist in the input object, the pipeline will create a new column and put the sample identifier in it. If the `batch_col` is something else than `batch`, it will be renamed to `batch` during pipeline execution. |
| `symbol_col` | Column in the input file containing gene symbol information. Defaults to `index`. There are two special values that can be used: `index` and `none`. `index` will use the row names of the matrix as gene symbols. `none` will trigger the pipeline to perform gene symbol conversion (this is not supported yet). The values from `symbol_col` will be copied to a column `gene_symbols` during pipeline execution. |
| `label_col` | Column in the input file containing cell type information. Defaults to `label`. If the column does not exist in the input object, the pipeline will create a new column and put `unknown` in it. If the `label_col` is something else than `label`, it will be renamed to `label` during pipeline execution. |
| `unknown_label` | Value in the `label_col` column that should be considered as unknown. Defaults to `unknown`. If the `unknown_label` is something else than `unknown`, it will be renamed to `unknown` during pipeline execution. If trying to perform integration with scANVI, more than one unique label other than `unknown` must exist in the input data. |
| `min_genes` | Minimum number of genes required for a cell to be considered. Defaults to `1`. |
| `min_cells` | Minimum number of cells required for a gene to be considered. Defaults to `1`. |
| `min_counts_cell` | Minimum number of counts required for a cell to be considered. Defaults to `1`. |
| `min_counts_gene` | Minimum number of counts required for a gene to be considered. Defaults to `1`. |
| `expected_cells` | Number of expected cells, used as input to Cellbender. |
| `max_mito_fraction` | Maximum fraction of mitochondrial reads for a cell to be considered. Defaults to `100`. |

An [example samplesheet](../assets/samplesheet.csv) has been provided with the pipeline.

Expand Down Expand Up @@ -107,6 +108,10 @@ genome: 'GRCh37'

You can also generate such `YAML`/`JSON` files via [nf-core/launch](https://nf-co.re/launch).

### Cell type annotation

Automated cell type annotation using [Celltypist](https://github.com/Teichlab/celltypist) is supported. You can specify the models to use with the `celltypist_model` parameter. If no models are specified, no cell type annotation will be performed.
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### Reference mapping

The pipeline supports mapping new samples into the latent space of an existing scVI/scANVI model.
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1 change: 1 addition & 0 deletions modules/local/scanpy/filter/main.nf
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Expand Up @@ -22,6 +22,7 @@ process SCANPY_FILTER {
min_cells = meta.min_cells ?: 1
min_counts_gene = meta.min_counts_gene ?: 1
min_counts_cell = meta.min_counts_cell ?: 1
max_mito_fraction = meta.max_mito_fraction ?: 100
prefix = task.ext.prefix ?: "${meta.id}"
template 'filter.py'
}
6 changes: 6 additions & 0 deletions modules/local/scanpy/filter/templates/filter.py
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Expand Up @@ -29,6 +29,12 @@ def format_yaml_like(data: dict, indent: int = 0) -> str:
adata = sc.read_h5ad("${h5ad}")
prefix = "${prefix}"

adata.var["mt"] = adata.var_names.str.startswith(("MT-", "mt-"))
sc.pp.calculate_qc_metrics(
adata, qc_vars=["mt"], percent_top=None, log1p=False, inplace=True
)
adata = adata[adata.obs.pct_counts_mt < int("${max_mito_fraction}"), :].copy()

sc.pp.filter_cells(adata, min_counts=int("${min_counts_cell}"))
sc.pp.filter_genes(adata, min_counts=int("${min_counts_gene}"))

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