404
+ +Page not found
+ + +'+ escapeHtml(title) + '
' + escapeHtml(summary) +'
+
+
+
+ Page not found
+ + +Cellranger is very resouce intensive. So resource allocation is critical for counts. By default,
+Cell Ranger runs locally by default (or when specified as --jobmode=local), using 90% of available memory and all of the available cores.
+ +multi pipelineThis pipeline is used in situations when analyzing cell multiplexing data or projects with samples that have both gene expression and VDJ captures. More information about this analysis and when to use it can be found at: +https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/multi +https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/using/multi
+The pipeline would be called using subcommand multi:
The libraries csv file would also need to be supplied.
+| Name | +Flowcell | +Sample | +Type | ++ |
|---|---|---|---|---|
| IL15_LNs | +H7CNNBGXG | +IL15_LNs | +Gene Expression | +Expression | +
| IL15_LNs | +H7CT7BGXG | +IL15_LNs_BC | +Antibody Capture | +Capture | +
| IL15_TUMOR_CD11 | +H7CNNBGXG | +IL15_TUMOR_CD11 | +Gene Expression | +Expression | +
| IL15_TUMOR_CD11 | +H7CT7BGXG | +IL15_TUMOR_CD11_BC | +Antibody Capture | +Capture | +
The pipeline will use the provided libraries file to make a library file for each individual sample. This will then be processed again to create the configuration file in the format expected by CellRanger. Due to the lack of CRISPR projects at the facility, the pipeline was never tested for this technology. If this pipeline is run on a sample with CRISPR capture, check the generated sample configuration file to ensure that it matches what is expected.
+For multi task with VDJ libraries, “donor” and “origin” are required in "libraries.csv" for aggregation. The link here shows how the donor and origin information will be used in the data analysis:
Donor: An individual from whom adaptive immune cells (T cells, B cells) are collected (e.g. a sister and a brother would each be considered unique donors for the purposes of V(D)J aggregation).
+Origin: The specific source from which a dataset of cells is derived.
+The chain information is specified in 'feature_types' column. Valid specifications include VDJ, VDJ-T, VDJ-B, or VDJ-T-GD, and the combinations:
+• VDJ-T & VDJ-B +• VDJ-T-GD & VDJ-B +• VDJ-T & VDJ-T-GD & VDJ-B
+For multi task with CRISPR Guide Capture libraries, "feature_reference" column is required to be put in the fifth column in "libraries.csv".
+Name,Flowcell,Sample,Type,Feature
+F1Test,AACCCHVM5,F1CRISPR_Library,Gene Expression,crispr_feature_reference1.csv
+F1Test,AACCCHVM5,F1GE_Library,CRISPR Guide Capture,crispr_feature_reference1.csv
+F2Test,AACCCHVM5,F2CRISPR_Library,Gene Expression,crispr_feature_reference2.csv
+F2Test,AACCCHVM5,F2GE_Library,CRISPR Guide Capture,crispr_feature_reference2.csv
+F3Test,AACCCHVM5,F3CRISPR_Library,Gene Expression,crispr_feature_reference3.csv
+F3Test,AACCCHVM5,F3GE_Library,CRISPR Guide Capture,crispr_feature_reference3.csv
+The descriptions of the feature reference CSV file can be found here. In case of varying feature references for different GEX/CRISPR pairs, the fifth column can be used to provide different references.
+If an antibody capture was used, then a feature reference file will need to be provided in the config.py file with a features entry. There is currently no pipeline flag to add this, and it will need to be provided manually. For example:
+features="features.csv"
+The feature reference file would contain (at minimum) a unique ID for the feature, human readable name, read, pattern, sequence, and feature type. For example:
+id,name,sequence,feature_type,read,pattern
+CITE_CD64,CD64,AGCAATTAACGGGAG,Antibody Capture,R2,5PNNNNNNNNNN(BC)
+CITE_F4_80,F4_80,TTAACTTCAGCCCGT,Antibody Capture,R2,5PNNNNNNNNNN(BC)
+CITE_CD8a,CD8a,TACCCGTAATAGCGT,Antibody Capture,R2,5PNNNNNNNNNN(BC)
+CITE_XCR1,XCR1,TCCATTACCCACGTT,Antibody Capture,R2,5PNNNNNNNNNN(BC)
+Unless the information is provided, it is probably easiest to determine the pattern by checking the FASTQ file for the sequence location. More information can be found at: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/feature-bc-analysis
+If a multiplexing capture was used, then the pipeline can be called with the cmo flag. This will add a cmo entry into the config.py file, which can then be edited to provide the cmo (cell multiplexing oligo) reference file. For example:
+cmo="cmo.csv"
+The cmo reference file has a very similar format to the feature reference file. The difference is that the feature type would be Multiplexing Capture. For example:
+id,name,sequence,feature_type,read,pattern
+HTO_1,HTO_1,GTCAACTCTTTAGCG,Multiplexing Capture,R2,5P(BC)
+HTO_2,HTO_2,TGATGGCCTATTGGG,Multiplexing Capture,R2,5P(BC)
+HTO_3,HTO_3,TTCCGCCTCTCTTTG,Multiplexing Capture,R2,5P(BC)
+HTO_4,HTO_4,AGTAAGTTCAGCGTA,Multiplexing Capture,R2,5P(BC)
+When the cmo information is filled into the sample configuration file, it will directly use the cmo ID as the multiplexing sample ID. This would need to be manually edited if a different entry would want to be included.
+ +rnaTo be added
+ +' + escapeHtml(summary) +'
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'); + } +} + +function doSearch () { + var query = document.getElementById('mkdocs-search-query').value; + if (query.length > min_search_length) { + if (!window.Worker) { + displayResults(search(query)); + } else { + searchWorker.postMessage({query: query}); + } + } else { + // Clear results for short queries + displayResults([]); + } +} + +function initSearch () { + var search_input = document.getElementById('mkdocs-search-query'); + if (search_input) { + search_input.addEventListener("keyup", doSearch); + } + var term = getSearchTermFromLocation(); + if (term) { + search_input.value = term; + doSearch(); + } +} + +function onWorkerMessage (e) { + if (e.data.allowSearch) { + initSearch(); + } else if (e.data.results) { + var results = e.data.results; + displayResults(results); + } else if (e.data.config) { + min_search_length = e.data.config.min_search_length-1; + } +} + +if (!window.Worker) { + console.log('Web Worker API not supported'); + // load index in main thread + $.getScript(joinUrl(base_url, "search/worker.js")).done(function () { + console.log('Loaded worker'); + init(); + window.postMessage = function (msg) { + onWorkerMessage({data: msg}); + }; + }).fail(function (jqxhr, settings, exception) { + console.error('Could not load worker.js'); + }); +} else { + // Wrap search in a web worker + var searchWorker = new Worker(joinUrl(base_url, "search/worker.js")); + searchWorker.postMessage({init: true}); + searchWorker.onmessage = onWorkerMessage; +} diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 0000000..25cf588 --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"custom_setting/","text":"Resource allocation Cellranger is very resouce intensive. So resource allocation is critical for counts. By default, Cell Ranger runs locally by default (or when specified as --jobmode=local), using 90% of available memory and all of the available cores.","title":"Custom setting"},{"location":"custom_setting/#_1","text":"","title":""},{"location":"custom_setting/#resource-allocation","text":"Cellranger is very resouce intensive. So resource allocation is critical for counts. By default, Cell Ranger runs locally by default (or when specified as --jobmode=local), using 90% of available memory and all of the available cores.","title":"Resource allocation"},{"location":"multi/","text":"multi pipeline This pipeline is used in situations when analyzing cell multiplexing data or projects with samples that have both gene expression and VDJ captures. More information about this analysis and when to use it can be found at: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/multi https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/using/multi The pipeline would be called using subcommand multi : The libraries csv file would also need to be supplied. Name Flowcell Sample Type IL15_LNs H7CNNBGXG IL15_LNs Gene Expression Expression IL15_LNs H7CT7BGXG IL15_LNs_BC Antibody Capture Capture IL15_TUMOR_CD11 H7CNNBGXG IL15_TUMOR_CD11 Gene Expression Expression IL15_TUMOR_CD11 H7CT7BGXG IL15_TUMOR_CD11_BC Antibody Capture Capture Final sample name will be the name that is given to CellRanger as the name of the sample. Flowcell is the flowcell that contains the FASTQ files for this set of data. This can be the full path or just a unique identifier since the script will pull the full path from the config.py file. Sample is the sample name that was used when demultiplexing, and so should match the FASTQ files. Type is the library type for each sample. Current supported options are: Gene Expression, VDJ, CRISPR Guide Capture, Antibody Capture, and Multiplexing Capture The pipeline will use the provided libraries file to make a library file for each individual sample. This will then be processed again to create the configuration file in the format expected by CellRanger. Due to the lack of CRISPR projects at the facility, the pipeline was never tested for this technology. If this pipeline is run on a sample with CRISPR capture, check the generated sample configuration file to ensure that it matches what is expected. For multi task with VDJ libraries, \u201cdonor\u201d and \u201corigin\u201d are required in \"libraries.csv\" for aggregation. The link here shows how the donor and origin information will be used in the data analysis: Donor: An individual from whom adaptive immune cells (T cells, B cells) are collected (e.g. a sister and a brother would each be considered unique donors for the purposes of V(D)J aggregation). Origin: The specific source from which a dataset of cells is derived. The chain information is specified in 'feature_types' column. Valid specifications include VDJ, VDJ-T, VDJ-B, or VDJ-T-GD, and the combinations: \u2022 VDJ-T & VDJ-B \u2022 VDJ-T-GD & VDJ-B \u2022 VDJ-T & VDJ-T-GD & VDJ-B For multi task with CRISPR Guide Capture libraries, \"feature_reference\" column is required to be put in the fifth column in \"libraries.csv\". Name,Flowcell,Sample,Type,Feature F1Test,AACCCHVM5,F1CRISPR_Library,Gene Expression,crispr_feature_reference1.csv F1Test,AACCCHVM5,F1GE_Library,CRISPR Guide Capture,crispr_feature_reference1.csv F2Test,AACCCHVM5,F2CRISPR_Library,Gene Expression,crispr_feature_reference2.csv F2Test,AACCCHVM5,F2GE_Library,CRISPR Guide Capture,crispr_feature_reference2.csv F3Test,AACCCHVM5,F3CRISPR_Library,Gene Expression,crispr_feature_reference3.csv F3Test,AACCCHVM5,F3GE_Library,CRISPR Guide Capture,crispr_feature_reference3.csv The descriptions of the feature reference CSV file can be found here. In case of varying feature references for different GEX/CRISPR pairs, the fifth column can be used to provide different references. If an antibody capture was used, then a feature reference file will need to be provided in the config.py file with a features entry. There is currently no pipeline flag to add this, and it will need to be provided manually. For example: features=\"features.csv\" The feature reference file would contain (at minimum) a unique ID for the feature, human readable name, read, pattern, sequence, and feature type. For example: id,name,sequence,feature_type,read,pattern CITE_CD64,CD64,AGCAATTAACGGGAG,Antibody Capture,R2,5PNNNNNNNNNN(BC) CITE_F4_80,F4_80,TTAACTTCAGCCCGT,Antibody Capture,R2,5PNNNNNNNNNN(BC) CITE_CD8a,CD8a,TACCCGTAATAGCGT,Antibody Capture,R2,5PNNNNNNNNNN(BC) CITE_XCR1,XCR1,TCCATTACCCACGTT,Antibody Capture,R2,5PNNNNNNNNNN(BC) Unless the information is provided, it is probably easiest to determine the pattern by checking the FASTQ file for the sequence location. More information can be found at: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/feature-bc-analysis If a multiplexing capture was used, then the pipeline can be called with the cmo flag. This will add a cmo entry into the config.py file, which can then be edited to provide the cmo (cell multiplexing oligo) reference file. For example: cmo=\"cmo.csv\" The cmo reference file has a very similar format to the feature reference file. The difference is that the feature type would be Multiplexing Capture. For example: id,name,sequence,feature_type,read,pattern HTO_1,HTO_1,GTCAACTCTTTAGCG,Multiplexing Capture,R2,5P(BC) HTO_2,HTO_2,TGATGGCCTATTGGG,Multiplexing Capture,R2,5P(BC) HTO_3,HTO_3,TTCCGCCTCTCTTTG,Multiplexing Capture,R2,5P(BC) HTO_4,HTO_4,AGTAAGTTCAGCGTA,Multiplexing Capture,R2,5P(BC) When the cmo information is filled into the sample configuration file, it will directly use the cmo ID as the multiplexing sample ID. This would need to be manually edited if a different entry would want to be included.","title":"Subcommand `multi`"},{"location":"multi/#multi-pipeline","text":"This pipeline is used in situations when analyzing cell multiplexing data or projects with samples that have both gene expression and VDJ captures. More information about this analysis and when to use it can be found at: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/multi https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/using/multi The pipeline would be called using subcommand multi : The libraries csv file would also need to be supplied. Name Flowcell Sample Type IL15_LNs H7CNNBGXG IL15_LNs Gene Expression Expression IL15_LNs H7CT7BGXG IL15_LNs_BC Antibody Capture Capture IL15_TUMOR_CD11 H7CNNBGXG IL15_TUMOR_CD11 Gene Expression Expression IL15_TUMOR_CD11 H7CT7BGXG IL15_TUMOR_CD11_BC Antibody Capture Capture Final sample name will be the name that is given to CellRanger as the name of the sample. Flowcell is the flowcell that contains the FASTQ files for this set of data. This can be the full path or just a unique identifier since the script will pull the full path from the config.py file. Sample is the sample name that was used when demultiplexing, and so should match the FASTQ files. Type is the library type for each sample. Current supported options are: Gene Expression, VDJ, CRISPR Guide Capture, Antibody Capture, and Multiplexing Capture The pipeline will use the provided libraries file to make a library file for each individual sample. This will then be processed again to create the configuration file in the format expected by CellRanger. Due to the lack of CRISPR projects at the facility, the pipeline was never tested for this technology. If this pipeline is run on a sample with CRISPR capture, check the generated sample configuration file to ensure that it matches what is expected. For multi task with VDJ libraries, \u201cdonor\u201d and \u201corigin\u201d are required in \"libraries.csv\" for aggregation. The link here shows how the donor and origin information will be used in the data analysis: Donor: An individual from whom adaptive immune cells (T cells, B cells) are collected (e.g. a sister and a brother would each be considered unique donors for the purposes of V(D)J aggregation). Origin: The specific source from which a dataset of cells is derived. The chain information is specified in 'feature_types' column. Valid specifications include VDJ, VDJ-T, VDJ-B, or VDJ-T-GD, and the combinations: \u2022 VDJ-T & VDJ-B \u2022 VDJ-T-GD & VDJ-B \u2022 VDJ-T & VDJ-T-GD & VDJ-B For multi task with CRISPR Guide Capture libraries, \"feature_reference\" column is required to be put in the fifth column in \"libraries.csv\". Name,Flowcell,Sample,Type,Feature F1Test,AACCCHVM5,F1CRISPR_Library,Gene Expression,crispr_feature_reference1.csv F1Test,AACCCHVM5,F1GE_Library,CRISPR Guide Capture,crispr_feature_reference1.csv F2Test,AACCCHVM5,F2CRISPR_Library,Gene Expression,crispr_feature_reference2.csv F2Test,AACCCHVM5,F2GE_Library,CRISPR Guide Capture,crispr_feature_reference2.csv F3Test,AACCCHVM5,F3CRISPR_Library,Gene Expression,crispr_feature_reference3.csv F3Test,AACCCHVM5,F3GE_Library,CRISPR Guide Capture,crispr_feature_reference3.csv The descriptions of the feature reference CSV file can be found here. In case of varying feature references for different GEX/CRISPR pairs, the fifth column can be used to provide different references. If an antibody capture was used, then a feature reference file will need to be provided in the config.py file with a features entry. There is currently no pipeline flag to add this, and it will need to be provided manually. For example: features=\"features.csv\" The feature reference file would contain (at minimum) a unique ID for the feature, human readable name, read, pattern, sequence, and feature type. For example: id,name,sequence,feature_type,read,pattern CITE_CD64,CD64,AGCAATTAACGGGAG,Antibody Capture,R2,5PNNNNNNNNNN(BC) CITE_F4_80,F4_80,TTAACTTCAGCCCGT,Antibody Capture,R2,5PNNNNNNNNNN(BC) CITE_CD8a,CD8a,TACCCGTAATAGCGT,Antibody Capture,R2,5PNNNNNNNNNN(BC) CITE_XCR1,XCR1,TCCATTACCCACGTT,Antibody Capture,R2,5PNNNNNNNNNN(BC) Unless the information is provided, it is probably easiest to determine the pattern by checking the FASTQ file for the sequence location. More information can be found at: https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/feature-bc-analysis If a multiplexing capture was used, then the pipeline can be called with the cmo flag. This will add a cmo entry into the config.py file, which can then be edited to provide the cmo (cell multiplexing oligo) reference file. For example: cmo=\"cmo.csv\" The cmo reference file has a very similar format to the feature reference file. The difference is that the feature type would be Multiplexing Capture. For example: id,name,sequence,feature_type,read,pattern HTO_1,HTO_1,GTCAACTCTTTAGCG,Multiplexing Capture,R2,5P(BC) HTO_2,HTO_2,TGATGGCCTATTGGG,Multiplexing Capture,R2,5P(BC) HTO_3,HTO_3,TTCCGCCTCTCTTTG,Multiplexing Capture,R2,5P(BC) HTO_4,HTO_4,AGTAAGTTCAGCGTA,Multiplexing Capture,R2,5P(BC) When the cmo information is filled into the sample configuration file, it will directly use the cmo ID as the multiplexing sample ID. This would need to be manually edited if a different entry would want to be included.","title":"multi pipeline"},{"location":"rna/","text":"Subcommand rna To be added","title":"Subcommand rna"},{"location":"rna/#subcommand-rna","text":"To be added","title":"Subcommand rna"},{"location":"vdj/","text":"vdj pipeline usage: scMaestro vdj [-h] -f [FASTQPATH ...] -r REFERENCE FOLDER -g GENOME [-n] [--rerun] [--unlock] [--chain {auto,TR,IG}] options: -h, --help show this help message and exit -f [FASTQPATH ...], --fastqs [FASTQPATH ...] Path(s) to fastq files, multiple paths can be provided together. eg. \"-f path1 path2\" -r REFERENCE FOLDER, --reference REFERENCE FOLDER Reference genome folder for alignment -g GENOME, --genome GENOME Genome build, e.g. \"hg38\", \"mm10\" -n, --dryrun dry run --rerun dry run then prompt for submitting jobs that need to be rerun --unlock unlock working directory --chain {auto,TR,IG} Force the analysis to be carried out for a particular chain type. By default, Cell Ranger will try to automatically determine the chain type from the data. When this fails, the complete error message will look something like the following. In order for Cell Ranger to automatically determine chain type, the sample library must meet the listed conditions. V(D)J Chain detection failed for Sample foo in \"/mnt/scratch/inputs/x/y\". Total Reads = 1000000 Reads mapped to TR = 49211 Reads mapped to IG = 3 In order to distinguish between the TR and the IG chain the following conditions need to be satisfied: - A minimum of 10000 total reads - A minimum of 5.0% of the total reads needs to map to TR or IG - The number of reads mapped to TR should be at least 3.0x compared to the number of reads mapped to IG or vice versa Please check the input data and/or specify the chain via the --chain argument. To overcome this error on the command line, for cellranger vdj explicitly set the --chain parameter to either IG for BCRs or TR for TCRs. Documentation is at https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/using/vdj#opt-arg-exp. Reference https://kb.10xgenomics.com/hc/en-us/articles/10840673911693-How-to-solve-V-D-J-Chain-detection-failed-error-on-10x-Cloud","title":"Subcommand `vdj`"},{"location":"vdj/#vdj-pipeline","text":"usage: scMaestro vdj [-h] -f [FASTQPATH ...] -r REFERENCE FOLDER -g GENOME [-n] [--rerun] [--unlock] [--chain {auto,TR,IG}] options: -h, --help show this help message and exit -f [FASTQPATH ...], --fastqs [FASTQPATH ...] Path(s) to fastq files, multiple paths can be provided together. eg. \"-f path1 path2\" -r REFERENCE FOLDER, --reference REFERENCE FOLDER Reference genome folder for alignment -g GENOME, --genome GENOME Genome build, e.g. \"hg38\", \"mm10\" -n, --dryrun dry run --rerun dry run then prompt for submitting jobs that need to be rerun --unlock unlock working directory --chain {auto,TR,IG} Force the analysis to be carried out for a particular chain type. By default, Cell Ranger will try to automatically determine the chain type from the data. When this fails, the complete error message will look something like the following. In order for Cell Ranger to automatically determine chain type, the sample library must meet the listed conditions. V(D)J Chain detection failed for Sample foo in \"/mnt/scratch/inputs/x/y\". Total Reads = 1000000 Reads mapped to TR = 49211 Reads mapped to IG = 3 In order to distinguish between the TR and the IG chain the following conditions need to be satisfied: - A minimum of 10000 total reads - A minimum of 5.0% of the total reads needs to map to TR or IG - The number of reads mapped to TR should be at least 3.0x compared to the number of reads mapped to IG or vice versa Please check the input data and/or specify the chain via the --chain argument. To overcome this error on the command line, for cellranger vdj explicitly set the --chain parameter to either IG for BCRs or TR for TCRs. Documentation is at https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/using/vdj#opt-arg-exp.","title":"vdj pipeline"},{"location":"vdj/#reference","text":"https://kb.10xgenomics.com/hc/en-us/articles/10840673911693-How-to-solve-V-D-J-Chain-detection-failed-error-on-10x-Cloud","title":"Reference"}]} \ No newline at end of file diff --git a/search/worker.js b/search/worker.js new file mode 100644 index 0000000..8628dbc --- /dev/null +++ b/search/worker.js @@ -0,0 +1,133 @@ +var base_path = 'function' === typeof importScripts ? '.' : '/search/'; +var allowSearch = false; +var index; +var documents = {}; +var lang = ['en']; +var data; + +function getScript(script, callback) { + console.log('Loading script: ' + script); + $.getScript(base_path + script).done(function () { + callback(); + }).fail(function (jqxhr, settings, exception) { + console.log('Error: ' + exception); + }); +} + +function getScriptsInOrder(scripts, callback) { + if (scripts.length === 0) { + callback(); + return; + } + getScript(scripts[0], function() { + getScriptsInOrder(scripts.slice(1), callback); + }); +} + +function loadScripts(urls, callback) { + if( 'function' === typeof importScripts ) { + importScripts.apply(null, urls); + callback(); + } else { + getScriptsInOrder(urls, callback); + } +} + +function onJSONLoaded () { + data = JSON.parse(this.responseText); + var scriptsToLoad = ['lunr.js']; + if (data.config && data.config.lang && data.config.lang.length) { + lang = data.config.lang; + } + if (lang.length > 1 || lang[0] !== "en") { + scriptsToLoad.push('lunr.stemmer.support.js'); + if (lang.length > 1) { + scriptsToLoad.push('lunr.multi.js'); + } + if (lang.includes("ja") || lang.includes("jp")) { + scriptsToLoad.push('tinyseg.js'); + } + for (var i=0; i < lang.length; i++) { + if (lang[i] != 'en') { + scriptsToLoad.push(['lunr', lang[i], 'js'].join('.')); + } + } + } + loadScripts(scriptsToLoad, onScriptsLoaded); +} + +function onScriptsLoaded () { + console.log('All search scripts loaded, building Lunr index...'); + if (data.config && data.config.separator && data.config.separator.length) { + lunr.tokenizer.separator = new RegExp(data.config.separator); + } + + if (data.index) { + index = lunr.Index.load(data.index); + data.docs.forEach(function (doc) { + documents[doc.location] = doc; + }); + console.log('Lunr pre-built index loaded, search ready'); + } else { + index = lunr(function () { + if (lang.length === 1 && lang[0] !== "en" && lunr[lang[0]]) { + this.use(lunr[lang[0]]); + } else if (lang.length > 1) { + this.use(lunr.multiLanguage.apply(null, lang)); // spread operator not supported in all browsers: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Spread_operator#Browser_compatibility + } + this.field('title'); + this.field('text'); + this.ref('location'); + + for (var i=0; i < data.docs.length; i++) { + var doc = data.docs[i]; + this.add(doc); + documents[doc.location] = doc; + } + }); + console.log('Lunr index built, search ready'); + } + allowSearch = true; + postMessage({config: data.config}); + postMessage({allowSearch: allowSearch}); +} + +function init () { + var oReq = new XMLHttpRequest(); + oReq.addEventListener("load", onJSONLoaded); + var index_path = base_path + '/search_index.json'; + if( 'function' === typeof importScripts ){ + index_path = 'search_index.json'; + } + oReq.open("GET", index_path); + oReq.send(); +} + +function search (query) { + if (!allowSearch) { + console.error('Assets for search still loading'); + return; + } + + var resultDocuments = []; + var results = index.search(query); + for (var i=0; i < results.length; i++){ + var result = results[i]; + doc = documents[result.ref]; + doc.summary = doc.text.substring(0, 200); + resultDocuments.push(doc); + } + return resultDocuments; +} + +if( 'function' === typeof importScripts ) { + onmessage = function (e) { + if (e.data.init) { + init(); + } else if (e.data.query) { + postMessage({ results: search(e.data.query) }); + } else { + console.error("Worker - Unrecognized message: " + e); + } + }; +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..0f8724e --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,3 @@ + +vdj pipelineusage: scMaestro vdj [-h] -f [FASTQPATH ...] -r REFERENCE FOLDER -g GENOME [-n] [--rerun] [--unlock]
+ [--chain {auto,TR,IG}]
+
+options:
+ -h, --help show this help message and exit
+ -f [FASTQPATH ...], --fastqs [FASTQPATH ...]
+ Path(s) to fastq files, multiple paths can be provided together. eg. "-f path1
+ path2"
+ -r REFERENCE FOLDER, --reference REFERENCE FOLDER
+ Reference genome folder for alignment
+ -g GENOME, --genome GENOME
+ Genome build, e.g. "hg38", "mm10"
+ -n, --dryrun dry run
+ --rerun dry run then prompt for submitting jobs that need to be rerun
+ --unlock unlock working directory
+ --chain {auto,TR,IG} Force the analysis to be carried out for a particular chain type.
+By default, Cell Ranger will try to automatically determine the chain type from the data. When this fails, the complete error message will look something like the following. In order for Cell Ranger to automatically determine chain type, the sample library must meet the listed conditions.
+V(D)J Chain detection failed for Sample foo in "/mnt/scratch/inputs/x/y".
+
+Total Reads = 1000000
+Reads mapped to TR = 49211
+Reads mapped to IG = 3
+
+In order to distinguish between the TR and the IG chain the following conditions need to be satisfied:
+- A minimum of 10000 total reads
+- A minimum of 5.0% of the total reads needs to map to TR or IG
+- The number of reads mapped to TR should be at least 3.0x compared to the number of reads mapped to IG or vice versa
+Please check the input data and/or specify the chain via the --chain argument.
+To overcome this error on the command line, for cellranger vdj explicitly set the --chain parameter to either IG for BCRs or TR for TCRs. Documentation is at https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/using/vdj#opt-arg-exp.
+https://kb.10xgenomics.com/hc/en-us/articles/10840673911693-How-to-solve-V-D-J-Chain-detection-failed-error-on-10x-Cloud
+ +