{scdrake}
offers two pipelines - one for single-sample, and second one for integration of multiple samples
(which were processed by the single-sample pipeline before). As for now, each pipeline consists of
two subpipelines (referred to as stages), and two stages common to both single-sample and integration pipelines.
A more detailed diagram with target structure can be found here.
Each stage has its own config, plus there is a main config for each pipeline.
You can read more about configs in a separate vignette("scdrake_config")
.
Each stage also outputs a report in HTML format with rich graphics.
Advanced users might be interested in looking into source code of {scdrake}
's plans
(files named plans_*.R
).
Pipeline steps are mostly based on recommendations given in a great book Orchestrating Single-Cell Analysis with Bioconductor.
You can inspect output from the pipeline here.
The used datasets are:
All credits for these datasets go to 10x Genomics. Visit https://www.10xgenomics.com/resources/datasets for more information.
This is a pipeline for processing a single-sample.
01_input_qc
: reading in data, filtering, quality control -> vignette("stage_input_qc")
02_norm_clustering
: normalization, HVG selection, dimensionality reduction, clustering, cell type annotation
-> vignette("stage_norm_clustering")
This is a pipeline to integrate multiple samples processed by the single-sample pipeline. Just for clarification, an individual sample is also denoted as a batch.
More information can be found in OSCA
01_integration
: reading in data and integration -> vignette("stage_integration")
02_int_clustering
: post-integration clustering and cell annotation -> vignette("stage_int_clustering")
02_int_clustering
This stage basically reproduces the clustering and cell type annotation steps in the 02_norm_clustering
stage of
the single-sample pipeline. The only difference is the user selection of a final integration method which will be used downstream.
HVGs, reduced dimensions, and selected markers are already computed in the previous stage (01_integration
).
Some stages are common to both single-sample and integration pipelines.
cluster_markers
A stage for calculation, visualization and reporting of cell cluster markers ("global markers").
-> vignette("stage_cluster_markers")
contrasts
A stage for calculation, visualization and reporting of differentially expressed markers ("contrasts").
This stage is basically the same as the cluster_markers
stage, but all output is related to individual comparisons
of levels of cell groupings. Hence "contrasts", a term known from bulk RNA-seq where sample groups are compared
-> they are put in contrast.
-> vignette("stage_contrasts")
vignette("scdrake_docker")
)vignette("scdrake")
vignette("scdrake_integration")
vignette("scdrake_advanced")
vignette("scdrake_extend")
{drake}
basics: vignette("drake_basics")
{drake}
book: https://books.ropensci.org/drake/vignette("pipeline_overview")
vignette("scdrake_faq")
vignette("scdrake_cli")
vignette("scdrake_config")
vignette("scdrake_envvars")
vignette("config_pipeline")
vignette("config_main")
01_input_qc
: reading in data, filtering, quality control -> vignette("stage_input_qc")
02_norm_clustering
: normalization, HVG selection, dimensionality reduction, clustering, cell type annotation
-> vignette("stage_norm_clustering")
01_integration
: reading in data and integration -> vignette("stage_integration")
02_int_clustering
: post-integration clustering and cell annotation -> vignette("stage_int_clustering")
cluster_markers
-> vignette("stage_cluster_markers")
contrasts
(differential expression) -> vignette("stage_contrasts")
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