MarkersFinder

MarkersFinder¶

Find markers between different groups of cells

MarkersFinder is a process that wraps the Seurat::FindMarkers() function, and performs enrichment analysis for the markers found.

Input¶

srtobj: The seurat object loaded by SeuratPreparing If you have your Seurat object prepared by yourself, you can also use it here, but you should make sure that the object has been processed by PrepSCTFindMarkers if data is not normalized using SCTransform.

Output¶

outdir: Default: {{in.srtobj | stem0}}.markers.
The output directory for the markers and plots

Environment Variables¶

ncores (type=int): Default: 1.
Number of cores to use for parallel computing for some Seurat procedures.
- Used in future::plan(strategy = "multicore", workers = <ncores>) to parallelize some Seurat procedures.
- See also: https://satijalab.org/seurat/articles/future_vignette.html
mutaters (type=json): Default: {}.
The mutaters to mutate the metadata.
You can also use the clone selectors to select the TCR clones/clusters.
See https://pwwang.github.io/scplotter/reference/clone_selectors.html..
See also mutating the metadata.
group_by: The column name in metadata to group the cells.
If only group_by is specified, and ident-1 and ident-2 are not specified, markers will be found for all groups in this column in the manner of "group vs rest" comparison.
NA group will be ignored.
If None, Seurat::Idents(srtobj) will be used, which is usually "seurat_clusters" after unsupervised clustering.
ident_1: The first group of cells to compare When this is empty, the comparisons will be expanded to each group v.s. the rest of the cells in group_by.
ident_2: The second group of cells to compare If not provided, the rest of the cells are used for ident-2.
each: The column name in metadata to separate the cells into different cases.
When this is specified, the case will be expanded for each value of the column in metadata. For example, when you have envs.cases."Cluster Markers".each = "Sample", then the case will be expanded as envs.cases."Cluster Markers - Sample1", envs.cases."Cluster Markers - Sample2", etc.
You can specify allmarker_plots and overlaps to plot the markers for all cases in the same plot and plot the overlaps of the markers between different cases by values in this column.
dbs (list): Default: ['KEGG_2021_Human', 'MSigDB_Hallmark_2020'].
The dbs to do enrichment analysis for significant markers See below for all libraries.
https://maayanlab.cloud/Enrichr/#libraries
sigmarkers: Default: p_val_adj < 0.05.
An expression passed to dplyr::filter() to filter the significant markers for enrichment analysis.
Available variables are p_val, avg_log2FC, pct.1, pct.2 and p_val_adj. For example, "p_val_adj < 0.05 & abs(avg_log2FC) > 1" to select markers with adjusted p-value < 0.05 and absolute log2 fold change > 1.
enrich_style (choice): Default: enrichr.
The style of the enrichment analysis.
The enrichment analysis will be done by EnrichIt() from enrichit.
Two styles are available:
- enrichr: enrichr style enrichment analysis (fisher's exact test will be used).
- clusterprofiler: clusterProfiler style enrichment analysis (hypergeometric test will be used).
- clusterProfiler: alias for clusterprofiler
assay: The assay to use.
error (flag): Default: False.
Error out if no/not enough markers are found or no pathways are enriched.
If False, empty results will be returned.
subset: An expression to subset the cells for each case.
cache (type=auto): Default: /tmp.
Where to cache the results.
If True, cache to outdir of the job. If False, don't cache.
Otherwise, specify the directory to cache to.
rest (ns): Rest arguments for Seurat::FindMarkers().
Use - to replace . in the argument name. For example, use min-pct instead of min.pct.
- <more>: See https://satijalab.org/seurat/reference/findmarkers
allmarker_plots_defaults (ns): Default options for the plots for all markers when ident-1 is not specified.
- plot_type: The type of the plot.
  See https://pwwang.github.io/scplotter/reference/FeatureStatPlot.html.
  Available types are violin, box, bar, ridge, dim, heatmap and dot.
- more_formats (type=list): Default: [].
  The extra formats to save the plot in.
- save_code (flag): Default: False.
  Whether to save the code to generate the plot.
- devpars (ns): The device parameters for the plots.
  - res (type=int): Default: 100.
    The resolution of the plots.
  - height (type=int): The height of the plots.
  - width (type=int): The width of the plots.
- order_by: Default: desc(abs(avg_log2FC)).
  an expression to order the markers, passed by dplyr::arrange().
- genes: Default: 10.
  The number of top genes to show or an expression passed to dplyr::filter() to filter the genes.
- <more>: Other arguments passed to scplotter::FeatureStatPlot().
allmarker_plots (type=json): Default: {}.
All marker plot cases.
The keys are the names of the cases and the values are the dicts inherited from allmarker_plots_defaults.
allenrich_plots_defaults (ns): Default options for the plots to generate for the enrichment analysis.
- plot_type: Default: heatmap.
  The type of the plot.
- devpars (ns): The device parameters for the plots.
  - res (type=int): Default: 100.
    The resolution of the plots.
  - height (type=int): The height of the plots.
  - width (type=int): The width of the plots.
- <more>: See https://pwwang.github.io/scplotter/reference/EnrichmentPlot.html.
allenrich_plots (type=json): Default: {}.
Cases of the plots to generate for the enrichment analysis.
The keys are the names of the cases and the values are the dicts inherited from allenrich_plots_defaults.
The cases under envs.cases can inherit this options.
marker_plots_defaults (ns): Default options for the plots to generate for the markers.
- plot_type: The type of the plot.
  See https://pwwang.github.io/scplotter/reference/FeatureStatPlot.html.
  Available types are violin, box, bar, ridge, dim, heatmap and dot.
  There are two additional types available - volcano_pct and volcano_log2fc.
- more_formats (type=list): Default: [].
  The extra formats to save the plot in.
- save_code (flag): Default: False.
  Whether to save the code to generate the plot.
- devpars (ns): The device parameters for the plots.
  - res (type=int): Default: 100.
    The resolution of the plots.
  - height (type=int): The height of the plots.
  - width (type=int): The width of the plots.
- order_by: Default: desc(abs(avg_log2FC)).
  an expression to order the markers, passed by dplyr::arrange().
- genes: Default: 10.
  The number of top genes to show or an expression passed to dplyr::filter() to filter the genes.
- <more>: Other arguments passed to scplotter::FeatureStatPlot().
  If plot_type is volcano_pct or volcano_log2fc, they will be passed to scplotter::VolcanoPlot().
marker_plots (type=json): Default: {'Volcano Plot (diff_pct)': Diot({'plot_type': 'volcano_pct'}), 'Volcano Plot (log2FC)': Diot({'plot_type': 'volcano_log2fc'}), 'Dot Plot': Diot({'plot_type': 'dot'})}.
Cases of the plots to generate for the markers.
Plot cases. The keys are the names of the cases and the values are the dicts inherited from marker_plots_defaults.
The cases under envs.cases can inherit this options.
enrich_plots_defaults (ns): Default options for the plots to generate for the enrichment analysis.
- plot_type: The type of the plot.
  See https://pwwang.github.io/scplotter/reference/EnrichmentPlot.html.
  Available types are bar, dot, lollipop, network, enrichmap and wordcloud.
- more_formats (type=list): Default: [].
  The extra formats to save the plot in.
- save_code (flag): Default: False.
  Whether to save the code to generate the plot.
- devpars (ns): The device parameters for the plots.
  - res (type=int): Default: 100.
    The resolution of the plots.
  - height (type=int): The height of the plots.
  - width (type=int): The width of the plots.
- <more>: See https://pwwang.github.io/scplotter/reference/EnrichmentPlot.htmll.
enrich_plots (type=json): Default: {'Bar Plot': Diot({'plot_type': 'bar', 'ncol': 1, 'top_term': 10})}.
Cases of the plots to generate for the enrichment analysis.
The keys are the names of the cases and the values are the dicts inherited from enrich_plots_defaults.
The cases under envs.cases can inherit this options.
overlaps_defaults (ns): Default options for investigating the overlapping of significant markers between different cases or comparisons.
This means either ident-1 should be empty, so that they can be expanded to multiple comparisons.
- sigmarkers: The expression to filter the significant markers for each case.
  If not provided, envs.sigmarkers will be used.
- plot_type (choice): Default: venn.
  The type of the plot to generate for the overlaps.
  - venn: Use plotthis::VennDiagram().
  - upset: Use plotthis::UpsetPlot().
- more_formats (type=list): Default: [].
  The extra formats to save the plot in.
- save_code (flag): Default: False.
  Whether to save the code to generate the plot.
- devpars (ns): The device parameters for the plots.
  - res (type=int): Default: 100.
    The resolution of the plots.
  - height (type=int): The height of the plots.
  - width (type=int): The width of the plots.
- <more>: More arguments pased to plotthis::VennDiagram() (https://pwwang.github.io/plotthis/reference/venndiagram1.html) or plotthis::UpsetPlot() (https://pwwang.github.io/plotthis/reference/upsetplot1.html)
overlaps (type=json): Default: {}.
Cases for investigating the overlapping of significant markers between different cases or comparisons.
The keys are the names of the cases and the values are the dicts inherited from overlaps_defaults.
There are two situations that we can perform overlaps:
1. If ident-1 is not specified, the overlaps can be performed between different comparisons.
2. If each is specified, the overlaps can be performed between different cases, where in each case, ident-1 must be specified.
cases (type=json): Default: {}.
If you have multiple cases for marker discovery, you can specify them here. The keys are the names of the cases and the values are the above options. If some options are not specified, the default values specified above (under envs) will be used.
If no cases are specified, the default case will be added with the default values under envs with the name Marker Discovery.

Examples¶

The examples are for more general use of MarkersFinder, in order to demonstrate how the final cases are constructed.

Suppose we have a metadata like this:

id	seurat_clusters	Group
1	1	A
2	1	A
3	2	A
4	2	A
5	3	B
6	3	B
7	4	B
8	4	B

Default¶

By default, group_by is seurat_clusters, and ident_1 and ident_2 are not specified. So markers will be found for all clusters in the manner of "cluster vs rest" comparison.

Cluster
- 1 (vs 2, 3, 4)
- 2 (vs 1, 3, 4)
- 3 (vs 1, 2, 4)
- 4 (vs 1, 2, 3)

Each case will have the markers and the enrichment analysis for the markers as the results.

With `each` group¶

each is used to separate the cells into different cases. group_by is still seurat_clusters.

[<Proc>.envs]
group_by = "seurat_clusters"
each = "Group"

A:Cluster
- 1 (vs 2)
- 2 (vs 1)
B:Cluster
- 3 (vs 4)
- 4 (vs 3)

With `ident_1` only¶

ident_1 is used to specify the first group of cells to compare.
Then the rest of the cells in the case are used for ident_2.

[<Proc>.envs]
group_by = "seurat_clusters"
ident_1 = "1"

Cluster
- 1 (vs 2, 3, 4)

With both `ident_1` and `ident_2`¶

ident_1 and ident_2 are used to specify the two groups of cells to compare.

[<Proc>.envs]
group_by = "seurat_clusters"
ident_1 = "1"
ident_2 = "2"

Cluster
- 1 (vs 2)

Multiple cases¶

[<Proc>.envs.cases]
c1_vs_c2 = {ident_1 = "1", ident_2 = "2"}
c3_vs_c4 = {ident_1 = "3", ident_2 = "4"}

DEFAULT:c1_vs_c2
- 1 (vs 2)
DEFAULT:c3_vs_c4
- 3 (vs 4)

The DEFAULT section name will be ignored in the report. You can specify a section name other than DEFAULT for each case to group them in the report.