From 6157b47b3bf1d1b03dd9b72c9da814c4f2279dea Mon Sep 17 00:00:00 2001 From: DriesSchaumont Date: Mon, 21 Aug 2023 12:32:52 +0000 Subject: [PATCH] =?UTF-8?q?Deploy=20preview=20for=20PR=2047=20=F0=9F=9B=AB?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pr-preview/pr-47/components/index.html | 176 ++++++------ .../pr-47/fundamentals/architecture.html | 6 +- pr-preview/pr-47/search.json | 4 +- pr-preview/pr-47/sitemap.xml | 254 +++++++++--------- 4 files changed, 220 insertions(+), 220 deletions(-) diff --git a/pr-preview/pr-47/components/index.html b/pr-preview/pr-47/components/index.html index 10b47cde..1aa9978b 100644 --- a/pr-preview/pr-47/components/index.html +++ b/pr-preview/pr-47/components/index.html @@ -1165,7 +1165,7 @@

Workflows

- + Workflows @@ -1176,7 +1176,7 @@

Workflows

- + BD Rhapsody @@ -1187,7 +1187,7 @@

Workflows

A generic pipeline for running BD Rhapsody WTA or Targeted mapping, with support for AbSeq, VDJ and/or SMK. - + Cell Ranger mapping @@ -1198,7 +1198,7 @@

Workflows

A pipeline for running Cell Ranger mapping. - + Cell Ranger multi @@ -1209,7 +1209,7 @@

Workflows

A pipeline for running Cell Ranger multi. - + Cell Ranger post-processing @@ -1220,7 +1220,7 @@

Workflows

Post-processing Cell Ranger datasets. - + Conversion @@ -1231,7 +1231,7 @@

Workflows

A pipeline to convert different file formats to .h5mu. - + Demux @@ -1242,7 +1242,7 @@

Workflows

A generic pipeline for running bcl2fastq, bcl-convert or Cell Ranger mkfastq. - + Full pipeline @@ -1253,7 +1253,7 @@

Workflows

A pipeline to analyse multiple multiomics samples. - + Harmony leiden @@ -1264,7 +1264,7 @@

Workflows

Run harmony integration followed by neighbour calculations, leiden clustering and run umap on the result. - + Initialize integration @@ -1275,7 +1275,7 @@

Workflows

Run calculations that output information required for most integration methods: PCA, nearest neighbour and UMAP. - + Integration @@ -1286,7 +1286,7 @@

Workflows

A pipeline for demultiplexing multimodal multi-sample RNA transcriptomics data. - + Make reference @@ -1297,7 +1297,7 @@

Workflows

Build a transcriptomics reference into one of many formats - + Prot multisample @@ -1308,7 +1308,7 @@

Workflows

Processing unimodal multi-sample ADT data. - + Prot singlesample @@ -1319,7 +1319,7 @@

Workflows

Processing unimodal single-sample CITE-seq data. - + Rna multisample @@ -1330,7 +1330,7 @@

Workflows

Processing unimodal multi-sample RNA transcriptomics data. - + Rna singlesample @@ -1341,7 +1341,7 @@

Workflows

Processing unimodal single-sample RNA transcriptomics data. - + Scanorama leiden @@ -1352,7 +1352,7 @@

Workflows

Run scanorama integration followed by neighbour calculations, leiden clustering and run umap on the result. - + Scvi @@ -1404,7 +1404,7 @@

Modules

- + Modules @@ -1415,7 +1415,7 @@

Modules

- + Add id @@ -1426,7 +1426,7 @@

Modules

Add id of .obs. - + BD Rhapsody @@ -1437,7 +1437,7 @@

Modules

A wrapper for the BD Rhapsody Analysis CWL v1.10.1 pipeline - + Bbknn @@ -1448,7 +1448,7 @@

Modules

BBKNN network generation - + Bcl convert @@ -1459,7 +1459,7 @@

Modules

Convert bcl files to fastq files using bcl-convert. - + Bcl2fastq @@ -1470,7 +1470,7 @@

Modules

Convert bcl files to fastq files using bcl2fastq - + Build bdrhap reference @@ -1481,7 +1481,7 @@

Modules

Compile a reference into a STAR index compatible with the BD Rhapsody pipeline. - + Build cellranger reference @@ -1492,7 +1492,7 @@

Modules

Build a Cell Ranger-compatible reference folder from user-supplied genome FASTA and gene GTF files. - + Calculate qc metrics @@ -1503,7 +1503,7 @@

Modules

Add basic quality control metrics to an .h5mu file. - + Cellbender remove background @@ -1514,7 +1514,7 @@

Modules

Eliminating technical artifacts from high-throughput single-cell RNA sequencing data. - + Cellranger count @@ -1525,7 +1525,7 @@

Modules

Align fastq files using Cell Ranger count. - + Cellranger count split @@ -1536,7 +1536,7 @@

Modules

Split 10x Cell Ranger output directory into separate output fields. - + Cellranger mkfastq @@ -1547,7 +1547,7 @@

Modules

Demultiplex raw sequencing data - + Cellranger multi @@ -1558,7 +1558,7 @@

Modules

Align fastq files using Cell Ranger multi. - + Cellxgene census @@ -1569,7 +1569,7 @@

Modules

Query CellxGene Census or user-specified TileDBSoma object, and eventually fetch cell and gene metadata or/and expression counts. - + Clr @@ -1580,7 +1580,7 @@

Modules

Perform CLR normalization on CITE-seq data (Stoeckius et al., 2017) - + Concat @@ -1591,7 +1591,7 @@

Modules

Concatenates several uni-modal samples in .h5mu files into a single file - + Delete layer @@ -1602,7 +1602,7 @@

Modules

Delete an anndata layer from one or more modalities - + Do filter @@ -1613,7 +1613,7 @@

Modules

Remove observations and variables based on specified .obs and .var columns - + Download file @@ -1624,7 +1624,7 @@

Modules

Download a file - + Fastqc @@ -1635,7 +1635,7 @@

Modules

Fastqc component, please see https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. - + Filter 10xh5 @@ -1646,7 +1646,7 @@

Modules

Filter a 10x h5 dataset - + Filter with counts @@ -1657,7 +1657,7 @@

Modules

Filter scRNA-seq data based on the primary QC metrics. - + Filter with hvg @@ -1668,7 +1668,7 @@

Modules

Annotate highly variable genes [Satija15] [Zheng17] [Stuart19]. - + Filter with scrublet @@ -1679,7 +1679,7 @@

Modules

Doublet detection using the Scrublet method (Wolock, Lopez and Klein, 2019). - + Find neighbors @@ -1690,7 +1690,7 @@

Modules

Compute a neighborhood graph of observations [McInnes18]. - + From 10xh5 to h5mu @@ -1701,7 +1701,7 @@

Modules

Converts a 10x h5 into an h5mu file - + From 10xmtx to h5mu @@ -1712,7 +1712,7 @@

Modules

Converts a 10x mtx into an h5mu file - + From bd to 10x molecular barcode tags @@ -1723,7 +1723,7 @@

Modules

Convert the molecular barcode sequence SAM tag from BD format (MA) to 10X format (UB) - + From bdrhap to h5mu @@ -1734,7 +1734,7 @@

Modules

Convert the output of a BD Rhapsody WTA pipeline to a MuData h5 file - + From cellranger multi to h5mu @@ -1745,7 +1745,7 @@

Modules

Converts the output from cellranger multi to a single .h5mu file. - + From h5ad to h5mu @@ -1756,7 +1756,7 @@

Modules

Converts a single layer h5ad file into a single MuData object - + From h5mu to h5ad @@ -1767,7 +1767,7 @@

Modules

Converts a h5mu file into a h5ad file - + Harmonypy @@ -1778,7 +1778,7 @@

Modules

Performs Harmony integration based as described in https://github.com/immunogenomics/harmony. - + Htseq count @@ -1789,7 +1789,7 @@

Modules

Quantify gene expression for subsequent testing for differential expression. - + Htseq count to h5mu @@ -1800,7 +1800,7 @@

Modules

Convert the htseq table to a h5mu - + Join csv @@ -1811,7 +1811,7 @@

Modules

Join a csv containing metadata to the .obs or .var field of a mudata file. - + Join uns to obs @@ -1822,7 +1822,7 @@

Modules

Join a data frame in .uns containing metadata to the .obs of a mudata file. - + Leiden @@ -1833,7 +1833,7 @@

Modules

Cluster cells using the Leiden algorithm [Traag18] implemented in the Scanpy framework [Wolf18]. - + Lianapy @@ -1844,7 +1844,7 @@

Modules

Performs LIANA integration based as described in https://github.com/saezlab/liana-py - + Log1p @@ -1855,7 +1855,7 @@

Modules

Logarithmize the data matrix. - + Make params @@ -1866,7 +1866,7 @@

Modules

Looks for files in a directory and turn it in a params file. - + Make reference @@ -1877,7 +1877,7 @@

Modules

Preprocess and build a transcriptome reference. - + Merge @@ -1888,7 +1888,7 @@

Modules

Combine one or more single-modality .h5mu files together into one .h5mu file - + Mermaid @@ -1899,7 +1899,7 @@

Modules

Generates a network from mermaid code - + Move obsm to obs @@ -1910,7 +1910,7 @@

Modules

Move a matrix from .obsm to .obs. - + Multi star @@ -1921,7 +1921,7 @@

Modules

Align fastq files using STAR. - + Multi star to h5mu @@ -1932,7 +1932,7 @@

Modules

Convert the output of multi_star to a h5mu - + Multiqc @@ -1943,7 +1943,7 @@

Modules

Component for multiqc (https://multiqc.info/) - + Normalize total @@ -1954,7 +1954,7 @@

Modules

Normalize counts per cell. - + Pca @@ -1965,7 +1965,7 @@

Modules

Computes PCA coordinates, loadings and variance decomposition. - + Popv @@ -1976,7 +1976,7 @@

Modules

Performs popular major vote cell typing on single cell sequence data using multiple algorithms. - + Publish @@ -1987,7 +1987,7 @@

Modules

Publish an artifact and optionally rename with parameters - + Regress out @@ -1998,7 +1998,7 @@

Modules

Regress out (mostly) unwanted sources of variation. - + Remove modality @@ -2009,7 +2009,7 @@

Modules

Remove a modality from a .h5mu file - + Samtools sort @@ -2020,7 +2020,7 @@

Modules

Sort and (optionally) index alignments. - + Scale @@ -2031,7 +2031,7 @@

Modules

Scale data to unit variance and zero mean - + Scanorama @@ -2042,7 +2042,7 @@

Modules

Use Scanorama to integrate different experiments - + Scarches @@ -2053,7 +2053,7 @@

Modules

Performs reference mapping with scArches - + Scvelo @@ -2065,7 +2065,7 @@

Modules

Namespace: velocity - + Scvi @@ -2076,7 +2076,7 @@

Modules

Performs scvi integration as done in the human lung cell atlas https://github.com/LungCellAtlas/HLCA - + Split modalities @@ -2087,7 +2087,7 @@

Modules

Split the modalities from a single .h5mu multimodal sample into seperate .h5mu files. - + Star align @@ -2098,7 +2098,7 @@

Modules

Align fastq files using STAR. - + Star align v273a @@ -2109,7 +2109,7 @@

Modules

Align fastq files using STAR. - + Star build reference @@ -2120,7 +2120,7 @@

Modules

Create a reference for STAR from a set of fasta files. - + Subset h5mu @@ -2131,7 +2131,7 @@

Modules

Create a subset of a mudata file by selecting the first number of observations - + Sync test resources @@ -2142,7 +2142,7 @@

Modules

Synchronise the test resources from s3://openpipelines-data to resources_test - + Umap @@ -2153,7 +2153,7 @@

Modules

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique suitable for visualizing high-dimensional data. - + Velocyto @@ -2164,7 +2164,7 @@

Modules

Runs the velocity analysis on a BAM file, outputting a loom file. - + Velocyto to h5mu diff --git a/pr-preview/pr-47/fundamentals/architecture.html b/pr-preview/pr-47/fundamentals/architecture.html index 5f529e95..b7091d54 100644 --- a/pr-preview/pr-47/fundamentals/architecture.html +++ b/pr-preview/pr-47/fundamentals/architecture.html @@ -260,7 +260,7 @@

Architecture

-

Openpipeline is a pipeline for the processing of multimodal single-cell data that scales to a great many of samples. Covering the architecture requires us to explain many angles: what the expected inputs and outputs are for each workflow are, how do the workflows relate to each other, what the state of the data is at each step of the pipeline, etc… Here is an overview of the general steps involved in processing sequencing data into a single integrated object. We will discuss each of the steps further below.

+

OpenPipeline is a pipeline for the processing of multimodal single-cell data that scales to a great many of samples. Covering the architecture requires us to explain many angles, including: what the expected inputs and outputs are for each workflow are, how do the workflows relate to each other, and what the state of the data is at each step of the pipeline. Here is an overview of the general steps involved in processing sequencing data into a single integrated object. We will discuss each of the steps further below.

@@ -301,11 +301,11 @@

Ingestion workflows

Multiomics workflows

-

There exists no singlesample workflow. However, the prot_singlesample and rna_singlesample pipelines do exist and they map identically to the functionality described in the single-sample antibody capture Processing.qmd and single-sample gene Expression processing sections respectively. If you would like to process your samples as described in the unimodal single sample processing section, you can execute both workflows in tandem for the two modalities.

+

There exists no singlesample workflow. However, the prot_singlesample and rna_singlesample pipelines do exist and they map identically to the functionality described in the single-sample antibody capture processing and single-sample gene expression processing sections respectively. If you would like to process your samples as described in the unimodal single sample processing section, you can execute both workflows in tandem for the two modalities.

Contrary to the workflows for single sample processing, there exists a multiomics/multisample workflow. However this workflow is not just the multiomics/prot_multisample and multiomics/rna_multisample workflows that have been combined. Instead, it combines the multiomics/prot_multisample, multiomics/rna_multisample and multiomics/integration/initialize_integration workflows. The purpose of this pipeline is to provide an extra ‘entrypoint’ into the full pipeline that skips the singlesample processing, allowing reprocessing samples that have already been processed before. A popular usecase is to manually select one or more celltypes which need to be processed again or the integration of observations from multiple experiments into a single dataset. Keep in mind that concatenation is not included in the multisample pipeline, so when multiple input files are specified they are processed in parallel. If you would like to integrate multiple experiments, you need to first concatenate them in a seperate step:

-

+

diff --git a/pr-preview/pr-47/search.json b/pr-preview/pr-47/search.json index 6ce38247..b3482d24 100644 --- a/pr-preview/pr-47/search.json +++ b/pr-preview/pr-47/search.json @@ -1376,7 +1376,7 @@ "href": "fundamentals/architecture.html", "title": "Architecture", "section": "", - "text": "Openpipeline is a pipeline for the processing of multimodal single-cell data that scales to a great many of samples. Covering the architecture requires us to explain many angles: what the expected inputs and outputs are for each workflow are, how do the workflows relate to each other, what the state of the data is at each step of the pipeline, etc… Here is an overview of the general steps involved in processing sequencing data into a single integrated object. We will discuss each of the steps further below.\nflowchart TD \n ingest[\"Ingestion\"] --> split --> unimodalsinglesample[\"Unimodal Single Sample Processing\"] --> concat --> unimodalmultisample[\"Unimodal Multi Sample Processing\"] --> merging --> integation_setup[\"Integration Setup\"] --> integration[\"Integration\"] --> downstreamprocessing[\"Downstream Processing\"]\n\n\nFigure 1: Overview of the steps included in OpenPipeline for the analysis of single cell multiomics data." + "text": "OpenPipeline is a pipeline for the processing of multimodal single-cell data that scales to a great many of samples. Covering the architecture requires us to explain many angles, including: what the expected inputs and outputs are for each workflow are, how do the workflows relate to each other, and what the state of the data is at each step of the pipeline. Here is an overview of the general steps involved in processing sequencing data into a single integrated object. We will discuss each of the steps further below.\nflowchart TD \n ingest[\"Ingestion\"] --> split --> unimodalsinglesample[\"Unimodal Single Sample Processing\"] --> concat --> unimodalmultisample[\"Unimodal Multi Sample Processing\"] --> merging --> integation_setup[\"Integration Setup\"] --> integration[\"Integration\"] --> downstreamprocessing[\"Downstream Processing\"]\n\n\nFigure 1: Overview of the steps included in OpenPipeline for the analysis of single cell multiomics data." }, { "objectID": "fundamentals/architecture.html#ingestion-workflows", @@ -1390,7 +1390,7 @@ "href": "fundamentals/architecture.html#multiomics-workflows", "title": "Architecture", "section": "Multiomics workflows", - "text": "Multiomics workflows\nThere exists no singlesample workflow. However, the prot_singlesample and rna_singlesample pipelines do exist and they map identically to the functionality described in the single-sample antibody capture Processing.qmd and single-sample gene Expression processing sections respectively. If you would like to process your samples as described in the unimodal single sample processing section, you can execute both workflows in tandem for the two modalities.\nContrary to the workflows for single sample processing, there exists a multiomics/multisample workflow. However this workflow is not just the multiomics/prot_multisample and multiomics/rna_multisample workflows that have been combined. Instead, it combines the multiomics/prot_multisample, multiomics/rna_multisample and multiomics/integration/initialize_integration workflows. The purpose of this pipeline is to provide an extra ‘entrypoint’ into the full pipeline that skips the singlesample processing, allowing reprocessing samples that have already been processed before. A popular usecase is to manually select one or more celltypes which need to be processed again or the integration of observations from multiple experiments into a single dataset. Keep in mind that concatenation is not included in the multisample pipeline, so when multiple input files are specified they are processed in parallel. If you would like to integrate multiple experiments, you need to first concatenate them in a seperate step:" + "text": "Multiomics workflows\nThere exists no singlesample workflow. However, the prot_singlesample and rna_singlesample pipelines do exist and they map identically to the functionality described in the single-sample antibody capture processing and single-sample gene expression processing sections respectively. If you would like to process your samples as described in the unimodal single sample processing section, you can execute both workflows in tandem for the two modalities.\nContrary to the workflows for single sample processing, there exists a multiomics/multisample workflow. However this workflow is not just the multiomics/prot_multisample and multiomics/rna_multisample workflows that have been combined. Instead, it combines the multiomics/prot_multisample, multiomics/rna_multisample and multiomics/integration/initialize_integration workflows. The purpose of this pipeline is to provide an extra ‘entrypoint’ into the full pipeline that skips the singlesample processing, allowing reprocessing samples that have already been processed before. A popular usecase is to manually select one or more celltypes which need to be processed again or the integration of observations from multiple experiments into a single dataset. Keep in mind that concatenation is not included in the multisample pipeline, so when multiple input files are specified they are processed in parallel. If you would like to integrate multiple experiments, you need to first concatenate them in a seperate step:" }, { "objectID": "fundamentals/architecture.html#the-full-pipeline", diff --git a/pr-preview/pr-47/sitemap.xml b/pr-preview/pr-47/sitemap.xml index a4c01580..f60e61d0 100644 --- a/pr-preview/pr-47/sitemap.xml +++ b/pr-preview/pr-47/sitemap.xml @@ -2,510 +2,510 @@ https://openpipelines.bio/team/dries_de_maeyer/index.html - 2023-08-21T12:06:53.146Z + 2023-08-21T12:29:47.550Z https://openpipelines.bio/team/toni_verbeiren/index.html - 2023-08-21T12:06:53.146Z + 2023-08-21T12:29:47.550Z https://openpipelines.bio/team/povilas_gibas/index.html - 2023-08-21T12:06:53.146Z + 2023-08-21T12:29:47.550Z https://openpipelines.bio/team/matthias_beyens/index.html - 2023-08-21T12:06:53.146Z + 2023-08-21T12:29:47.550Z https://openpipelines.bio/team/mauro_saporita/index.html - 2023-08-21T12:06:53.146Z + 2023-08-21T12:29:47.550Z https://openpipelines.bio/team/samuel_dsouza/index.html - 2023-08-21T12:06:53.146Z + 2023-08-21T12:29:47.550Z https://openpipelines.bio/team/marijke_van_moerbeke/index.html - 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