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/$$$$$$ /$$$$$$ /$$$$$$$$ /$$$$$$$ /$$__ $$ /$$__ $$|__ $$__/| $$__ $$
| $$ \__/| $$ \ $$ | $$ | $$ \ $$
| $$ | $$$$$$$$ | $$ | $$ | $$
| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/

CATD: Critical Assessment of Transcriptomics Deconvolution

This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.

Citation

Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.

Installation

The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation. The installation instruction for the deconvolution programs can be found at install.md.

Input format

The input reference data (a single-cell dataset) is formatted as ExpressionSet.

ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation:

The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int. The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.

An example below:

A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python

Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.

An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').

Experimental design

In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.

1. self-reference

Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.

In this case, the self_reference function in CATD.R is used.

2. cross-refernce

When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.

In this case, the cross_reference function in CATD.R is used.

3. real bulk

When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset. We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.

The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.

Example command

  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866
  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866
  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About

CATD: Critical Assessment of Transcriptomics Deconvolution

Topics

benchmarkingbulk-rna-seqdeconvolutionscrna-seq

Resources

Readme
MIT license
Activity
Custom properties

Stars

2 stars

Watchers

4 watching

Forks

0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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drawing
/$$$$$$ /$$$$$$ /$$$$$$$$ /$$$$$$$ /$$__ $$ /$$__ $$|__ $$__/| $$__ $$
| $$ \__/| $$ \ $$ | $$ | $$ \ $$
| $$ | $$$$$$$$ | $$ | $$ | $$
| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/ 
CATD: Critical Assessment of Transcriptomics Deconvolution
This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.
Citation
Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.
Installation
The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation.
The installation instruction for the deconvolution programs can be found at install.md.
Input format
The input reference data (a single-cell dataset) is formatted as ExpressionSet.
ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: 
The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int.
The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.
An example below:
A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python
Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.
An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').
Experimental design
In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.
1. self-reference
Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.
In this case, the self_reference function in CATD.R is used.
2. cross-refernce
When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.
In this case, the cross_reference function in CATD.R is used.
3. real bulk
When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset.
We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.
The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.
Example command
  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About
CATD: Critical Assessment of Transcriptomics Deconvolution
Topics
benchmarkingbulk-rna-seqdeconvolutionscrna-seq
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
4 watching
Forks
0 forks
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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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
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var __m = "*";
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Skip to content
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drawing
/$$$$$$ /$$$$$$ /$$$$$$$$ /$$$$$$$ /$$__ $$ /$$__ $$|__ $$__/| $$__ $$
| $$ \__/| $$ \ $$ | $$ | $$ \ $$
| $$ | $$$$$$$$ | $$ | $$ | $$
| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/ 
CATD: Critical Assessment of Transcriptomics Deconvolution
This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.
Citation
Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.
Installation
The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation.
The installation instruction for the deconvolution programs can be found at install.md.
Input format
The input reference data (a single-cell dataset) is formatted as ExpressionSet.
ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: 
The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int.
The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.
An example below:
A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python
Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.
An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').
Experimental design
In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.
1. self-reference
Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.
In this case, the self_reference function in CATD.R is used.
2. cross-refernce
When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.
In this case, the cross_reference function in CATD.R is used.
3. real bulk
When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset.
We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.
The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.
Example command
  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About
CATD: Critical Assessment of Transcriptomics Deconvolution
Topics
benchmarkingbulk-rna-seqdeconvolutionscrna-seq
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
4 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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('^' + ".*" + '

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drawing
/$$$$$$ /$$$$$$ /$$$$$$$$ /$$$$$$$ /$$__ $$ /$$__ $$|__ $$__/| $$__ $$
| $$ \__/| $$ \ $$ | $$ | $$ \ $$
| $$ | $$$$$$$$ | $$ | $$ | $$
| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/ 
CATD: Critical Assessment of Transcriptomics Deconvolution
This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.
Citation
Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.
Installation
The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation.
The installation instruction for the deconvolution programs can be found at install.md.
Input format
The input reference data (a single-cell dataset) is formatted as ExpressionSet.
ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: 
The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int.
The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.
An example below:
A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python
Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.
An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').
Experimental design
In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.
1. self-reference
Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.
In this case, the self_reference function in CATD.R is used.
2. cross-refernce
When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.
In this case, the cross_reference function in CATD.R is used.
3. real bulk
When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset.
We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.
The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.
Example command
  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About
CATD: Critical Assessment of Transcriptomics Deconvolution
Topics
benchmarkingbulk-rna-seqdeconvolutionscrna-seq
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
4 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
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try {
var __m = "youtube.com";
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| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/ 
CATD: Critical Assessment of Transcriptomics Deconvolution
This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.
Citation
Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.
Installation
The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation.
The installation instruction for the deconvolution programs can be found at install.md.
Input format
The input reference data (a single-cell dataset) is formatted as ExpressionSet.
ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: 
The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int.
The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.
An example below:
A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python
Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.
An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').
Experimental design
In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.
1. self-reference
Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.
In this case, the self_reference function in CATD.R is used.
2. cross-refernce
When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.
In this case, the cross_reference function in CATD.R is used.
3. real bulk
When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset.
We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.
The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.
Example command
  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About
CATD: Critical Assessment of Transcriptomics Deconvolution
Topics
benchmarkingbulk-rna-seqdeconvolutionscrna-seq
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Readme
MIT license
Activity
Custom properties
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4 watching
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| $$ \__/| $$ \ $$ | $$ | $$ \ $$
| $$ | $$$$$$$$ | $$ | $$ | $$
| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/ 
CATD: Critical Assessment of Transcriptomics Deconvolution
This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.
Citation
Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.
Installation
The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation.
The installation instruction for the deconvolution programs can be found at install.md.
Input format
The input reference data (a single-cell dataset) is formatted as ExpressionSet.
ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: 
The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int.
The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.
An example below:
A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python
Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.
An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').
Experimental design
In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.
1. self-reference
Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.
In this case, the self_reference function in CATD.R is used.
2. cross-refernce
When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.
In this case, the cross_reference function in CATD.R is used.
3. real bulk
When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset.
We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.
The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.
Example command
  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About
CATD: Critical Assessment of Transcriptomics Deconvolution
Topics
benchmarkingbulk-rna-seqdeconvolutionscrna-seq
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
4 watching
Forks
0 forks
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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");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
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var __m = "*";
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drawing
/$$$$$$ /$$$$$$ /$$$$$$$$ /$$$$$$$ /$$__ $$ /$$__ $$|__ $$__/| $$__ $$
| $$ \__/| $$ \ $$ | $$ | $$ \ $$
| $$ | $$$$$$$$ | $$ | $$ | $$
| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/ 
CATD: Critical Assessment of Transcriptomics Deconvolution
This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.
Citation
Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.
Installation
The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation.
The installation instruction for the deconvolution programs can be found at install.md.
Input format
The input reference data (a single-cell dataset) is formatted as ExpressionSet.
ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: 
The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int.
The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.
An example below:
A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python
Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.
An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').
Experimental design
In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.
1. self-reference
Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.
In this case, the self_reference function in CATD.R is used.
2. cross-refernce
When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.
In this case, the cross_reference function in CATD.R is used.
3. real bulk
When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset.
We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.
The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.
Example command
  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About
CATD: Critical Assessment of Transcriptomics Deconvolution
Topics
benchmarkingbulk-rna-seqdeconvolutionscrna-seq
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
4 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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");
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drawing
/$$$$$$ /$$$$$$ /$$$$$$$$ /$$$$$$$ /$$__ $$ /$$__ $$|__ $$__/| $$__ $$
| $$ \__/| $$ \ $$ | $$ | $$ \ $$
| $$ | $$$$$$$$ | $$ | $$ | $$
| $$ | $$__ $$ | $$ | $$ | $$
| $$ $$| $$ | $$ | $$ | $$ | $$
| $$$$$$/| $$ | $$ | $$ | $$$$$$$/
\______/ |__/ |__/ |__/ |_______/ 
CATD: Critical Assessment of Transcriptomics Deconvolution
This package extends the functionalities in deconv_benchmark, which was published at: Nature Communications.
Citation
Zhichao Miao, Anna Vathrakokili Pournara, Irene Papatheodoru. Multiple-level benchmarking of cell type deconvolution.
Installation
The scripts in CATD include CATD.R and helper_functions.R, which do not require an installation.
The installation instruction for the deconvolution programs can be found at install.md.
Input format
The input reference data (a single-cell dataset) is formatted as ExpressionSet.
ExpressionSet (storageMode: lockedEnvironment)
assayData: 80 features, 1000 samples element names: exprs protocolData: none
phenoData
sampleNames: cell_1 cell_2 ... cell_1000 (1000 total)
varLabels: cellID cellType sampleID
varMetadata: labelDescription
featureData: none
experimentData: use 'experimentData(object)'
Annotation: 
The expression matrix is available at: x@assayData$exprs. Rows for genes, columns for cells. A matrix: M × N of type int.
The metadata is available at: x@phenoData@data, which includes THREE columns: cellID, cellType and sampleID.
An example below:
A data.frame: 1000 × 3
cellID	cellType	sampleID
<chr>	<chr>	<chr>
cell_1	cell_1	cell_type_1	indiv1
cell_2	cell_2	cell_type_1	indiv2
cell_3	cell_3	cell_type_1	indiv1
cell_4	cell_4	cell_type_1	indiv2
cell_5	cell_5	cell_type_1	indiv1
cell_6	cell_6	cell_type_1	indiv2
cell_7	cell_7	cell_type_1	indiv1
cell_8	cell_8	cell_type_1	indiv2
cell_9	cell_9	cell_type_1	indiv1
cell_10	cell_10	cell_type_1	indiv2
......

Format transformation from python
Python formatted AnnData can be transformed to required ExpressionSet format using the anndata2expressionset function in helper_functions.R.
An example here: anndata2expressionset('downsample200/Ximerakis2019.h5', 'downsample200R/Ximerakis2019.rds').
Experimental design
In this evaluation framework, we have THREE levels of experiments: 1. self-reference, 2. cross-reference and 3. real bulk.
1. self-reference
Both the pseudobulk and the cell type reference are generated from the same dataset, which is the same setting as the Nature Communications paper.
In this case, the self_reference function in CATD.R is used.
2. cross-refernce
When we have two reference (single-cell) datasets for the same type of tissue available, one of the dataset can be used to generate pseudobulk and the other one can be used as cell type reference.
In this case, the cross_reference function in CATD.R is used.
3. real bulk
When we have real bulk RNA-seq data available, we can deconvolve real bulk data with a single-cell reference dataset. However, there is no way to know the cell type proportion in the bulk dataset in priori. Therefore, we use TWO reference datasets to deconvolve the real bulk dataset.
We assume that a program can deconvolve the real bulk dataset with the reference dataset very well (suppose the accuracy is 100%). The resulted cell proportion can be used as a gold standard to compare with the deconvolution using the second reference dataset. There can be possibility that the deconvolution with the two reference datasets are accidentally equally wrong. However, such a situation can be almost excluded after applying the self-reference and cross-reference evaluations.
The bulk_reference function in CATD.R can deconvolve a real bulk dataset with a reference dataset, while the bulk_2references function compares the deconvolutions of a real bulk dataset using two reference datasets.
Example command
  1. self-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R s example.rds none bulk TMM all nnls 100 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. cross-reference
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R c example1.rds example2.rds none bulk TMM all nnls 10000 none 1 F T T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

  1. real bulk
# With the example we provided with this repository + no cell type removed:
Rscript CATD.R b bulk.rds example1.rds example2.rds none bulk TMM all nnls 10000 none 1 T
#Expected output:
# RMSE Pearson
#1 0.0351 0.9866

About
CATD: Critical Assessment of Transcriptomics Deconvolution
Topics
benchmarkingbulk-rna-seqdeconvolutionscrna-seq
Resources
Readme
MIT license
Activity
Custom properties
Stars
2 stars
Watchers
4 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages