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Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

About
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Snakefiles: Snakefile
CITESeq/Snakefile
SmartSeq/Snakefile
SmartSeq/preprocessing/Snakefile
Common_prep.py
Required inputs
1. Personal config file into your home directory, “.config/snakemake/CITESeq/config.yaml”
2. “project_config_file.yaml” in project directory
3. Sample sheet in project directory
a. “Sample_ID”
b. “CR_ID”, ID used by Cellranger (right now, the pipeline assumes the data is hashed) c. “flowcell_full”, fullname of the flowcell subdirectory where the cellranger outputs can be found
d. “HTO_index_name”
e. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
4. “QC_steps.csv”, location specified in project config file. It includes 1 line for each QC step, any of which can be missing and the step will be skipped. 5. For CITESeq, Cellranger outputs of feature, barcode, matrix information is required, both raw and filtered (for creating background for protein count normalization). It is assumed that they are in directories named “raw_feature_bc_matrix” and “filtered_feature_bc_matrix”, located in one of two places (where runs_dir is defined in the project config file, flowcell is the full name to the flowcell subdirectory, and id is the CR_ID of the cellranger sample named in the sample sheet): a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from a project directory which contains 2), and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/CITESeq/Snakefile --profile CITESeq results/QC_first_pass/App/Data.RData -np
Notes on calling snakemake:
1. On an HPC, load snakemake module before invocation.
2. –profile will refer to 1), the personal config file which specifies to snakemake how to submit job requests on the HPC.
3. -np requests a dry run, useful for troubleshooting and showing you which snakemake rules would be run.
4. The input “results/QC_first_pass/App/Data.RData” specifies which output of the pipeline you would like to create (or make sure is already created and up-to-date). This output is the final output, but you may also request an intermediate output, e.g. “results/QC_first_pass/Clustered.RDS” if you only want to do the QC and clustering and then evaluate before continuing.
“SC_R” pipeline (2021/04/06)
/hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile
Required inputs
6. Personal config file into your home directory,“.config/snakemake/SC_R/config.yaml”
7. “project_config_file.yaml”
8. Sample sheet
a. Required columns: “Sample_ID”, “flowcell_full”
b. Required if hashed: “CR_ID”, “HTO_index_name”
c. Also any batch features you entered in project_config_file.yaml, and anything else you want in the Seurat object
9. “QC_steps.csv”. There is 1 line for each QC step, any of which can be missing and the step will be skipped.
10. Cellranger data is assumed to be in one of either of these two places:
a. file.path(runs_dir, flowcell, 'multi_output', id, 'outs', 'multi', 'count','filtered_feature_bc_matrix')
b. file.path(runs_dir, flowcell, 'count_output', id, 'outs', 'filtered_feature_bc_matrix')
Invocation from project directory that contains 2) and 3):
snakemake --snakefile /hpcdata/vrc/vrc1_data/douek_lab/snakemakes/SC_R/Snakefile --profile SC_R results/QC_first_pass/App/Data.RData -np

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