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title: MTEAPY's homepage | ||
description: Python library for Metabolic Task Enrichment Analysis | ||
theme: jekyll-theme-cayman | ||
# remote_theme: zendesk/jekyll-theme-zendesk-garden@main | ||
show_downloads: true | ||
title: MTEApy | ||
description: A Python library for Metabolic Task Enrichment Analysis | ||
theme: just-the-docs | ||
repository: bsc-life/mteapy | ||
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github: | ||
zip_url: https://github.com/bsc-life/mteapy/zipball/main | ||
tar_url: https://github.com/bsc-life/mteapy/tarball/main | ||
aux_links_new_tab: true | ||
aux_links: | ||
View MTEApy on GitHub: | ||
- https://github.com/bsc-life/mteapy | ||
nav_links_new_tab: true | ||
nav_external_links: | ||
- title: Download .tar.gz | ||
url: https://github.com/bsc-life/mteapy/tarball/main | ||
- title: View MTEApy on GitHub | ||
url: https://github.com/bsc-life/mteapy | ||
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footer_content: true | ||
color_scheme: light | ||
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callouts_level: quiet # or loud | ||
callouts: | ||
highlight: | ||
color: yellow | ||
important: | ||
title: Important | ||
color: blue | ||
new: | ||
title: New | ||
color: green | ||
note: | ||
title: Note | ||
color: purple | ||
warning: | ||
title: Warning | ||
color: red |
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<footer class="site-footer"> | ||
<span class="site-footer-owner"></span> | ||
</footer> |
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<link rel="stylesheet" href="{{ '/assets/css/custom_style.css' | relative_url }}"> |
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<footer class="site-footer"> | ||
{% if site.github.is_project_page %} | ||
<span class="site-footer-owner">{{ site.description }}<p></p></span> | ||
<span class="site-footer-owner"><a href="{{ site.github.repository_url }}">{{ site.github.repository_name }}</a> is maintained by <a href="{{ site.github.owner_url }}">{{ site.github.owner_name }}</a>.</span> | ||
{% endif %} | ||
</footer> |
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body { padding: 0; margin: 0; font-family: "Ubuntu"; font-size: 16px; line-height: 1.5; color: #606c71; } | ||
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.page-header { color: #fff; text-align: center; background-color:orange; background-image: linear-gradient(120deg, orange, purple); } | ||
@media screen and (min-width: 64em) { .page-header { padding: 5rem 6rem; } } | ||
@media screen and (min-width: 42em) and (max-width: 64em) { .page-header { padding: 3rem 4rem; } } | ||
@media screen and (max-width: 42em) { .page-header { padding: 2rem 1rem; } } | ||
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.project-name { margin-top: 0.1rem; margin-bottom: 0.1rem; } | ||
@media screen and (min-width: 64em) { .project-name { font-size: 3.25rem; } } | ||
@media screen and (min-width: 42em) and (max-width: 64em) { .project-name { font-size: 2.25rem; } } | ||
@media screen and (max-width: 42em) { .project-name { font-size: 1.75rem; } } | ||
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.main-content h1, .main-content h2, .main-content h3, .main-content h4, .main-content h5, .main-content h6 { margin-top: 2rem; margin-bottom: 1rem; font-weight: bold; color: orange; } | ||
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.main-content code { padding: 2px 4px; font-family: Consolas, "Liberation Mono", Menlo, Courier, monospace; font-size: 0.9rem; color: #567482; background-color: #f3f6fa; border-radius: 0.5rem; } | ||
a { color: rgb(255, 102, 0); text-decoration: none; } | ||
h1, h2, h3, h4, h5, h6, #toctitle { margin-top: 0; margin-bottom: 1em; font-weight: 500; line-height: 1.25; color: rgb(255, 102, 0); } | ||
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--- | ||
title: CellFie | ||
layout: default | ||
nav_order: 3 | ||
--- | ||
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# **The CellFie framework** | ||
{: .no_toc } | ||
*** | ||
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## Table of contents | ||
{: .no_toc .text-delta } | ||
1. TOC | ||
{:toc} | ||
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## Description | ||
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The **CellFie** framework is a contraint-based metabolic modeling framework that was originally published by [Richelle _et al._, 2021](https://doi.org/10.1016/j.crmeth.2021.100040). It leverages the use of mathematical descriptions of metabolic functions (metabolic tasks) and transcriptomics data to quantify metabolic functions. As opposed to TIDE, the CellFie framework allows for the processing of multiple samples at a time, making it suitable for large datasets and single-cell RNA sequencing. | ||
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## Command options | ||
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| Argument | Shortcut | Description | Default | | ||
|:-------- |:-------- |:----------- |:------- | | ||
| `expr_file` | | Filename for a normalized gene expression file (e.g., TPM). It should contain at least one column with gene names/symbols. | | | ||
| `--delim` | `-d` | Field delimiter for inputed file. | `\t` | | ||
| `-out` | `-o` | Directory to store the analysis' results. The result file(s) will be stored in the specified directory in a tab-sepparated format (`.tsv`). | `CellFie_results/` | | ||
| `--gene_col` | | Name of the column in the inputed file containing gene names/symbols. | `geneID` | | ||
| `--threshold_type` | | Determines the threshold approach to be used. A `global` approach used the same threshold for all genes whereas a `local` approach uses a different threshold for each gene when computing the gene activity levels. | `local` | | ||
| `--global_threshold_type` | | Whether to use a `value` or a `percentile` of the distribution of all genes as global treshold for all genes. | `percentile` | | ||
| `--global_value` | | Value to use as global threshold according to the `global_threshold_type` option selected. Note that percentile values must be between 0 and 1. | `0.75` | | ||
| `--local_threshold_type` | | Determines the threshold type to be used in a local approach. `minmaxmean`: the threshold for each gene is determined by the mean of expression values across all conditions/samples but must be higher or equal than a lower bound and lower or equal to an upper bound. `mean`: the threshold of a gene is determined as its mean expression across all conditions/samples. | `minmaxmean` | | ||
| `--minmaxmean_threshold_type` | | Whether to use `value` or `percentile` of the distribution of all genes as upper and lower bounds. | `percentile` | | ||
| `--upper_bound` | | Upper bound value to be used according to the `minmaxmean_threshold_type`. Note that percentile values must be between 0 and 1. | `0.75` | | ||
| `--lower_bound` | | Lower bound value to be used according to the `minmaxmean_threshold_type`. Note that percentile values must be between 0 and 1. | `0.25` | | ||
| `--binary_scores` | | Flag to indicate whether to also return the binary metabolic score matrix as a second result file. See the original publication for more details | `False` | | ||
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## Usage Example | ||
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### Transcriptomics Data | ||
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One of the first things that the CellFie framework requires is a normalized gene expression matrix (usually stored as TPMs). Normaly, this type of data contains gene names/symbols as rows, and samples as columns. For the command to run, one of the columns of the matrix must store the information regarding gene names/symbols. | ||
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A typical normalized gene expression matrix will look like the following: | ||
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``` | ||
geneID S1 S2 S3 S4 | ||
0 ENSG00000000419 6.721972 7.768211 0.111999 0.561086 | ||
1 ENSG00000001036 5.880123 10.804611 4.273897 3.703098 | ||
2 ENSG00000001084 13.568022 11.912389 21.792070 4.126645 | ||
3 ENSG00000001630 9.830659 10.973878 16.052115 3.264040 | ||
4 ENSG00000002549 10.312642 10.373970 6.246490 0.597024 | ||
... ... ... ... ... ... | ||
``` | ||
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### Running CellFie | ||
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To run the CellFie framework using the command-line, the command `run-mtea CellFie` should be used with the desired arguments. A typical CellFie analysis is run using the `minmaxmean` local thresholding strategy, which will be used by default by the command, with a percentile upper and lower bounds of `0.75` and `0.25`. | ||
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{: .note} | ||
Only the **Human-GEM** and its metabolic tasks are implemented, so the framework will only take in **EnsemblIDs** as valid genic nomenclature. We are working to allow for any metabolic model and metabolic tasks to be used for more customisable analyses! | ||
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```sh | ||
run-mtea CellFie expression_file.tsv \ | ||
-o results/ \ | ||
--gene_col geneID \ | ||
--threshold_type local \ | ||
--local_threshold_type minmaxmean \ | ||
--minmaxmean_threshold_type percentile \ | ||
--upper_bound 0.75 \ | ||
--lower_bound 0.25 \ | ||
--binary_scores | ||
``` | ||
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### Understanding the CellFie results | ||
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Once the analysis is run, one or two results files will be stored in the specified directory. | ||
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| Result File | Description | | ||
|:----------- |:----------- | | ||
| `cellfie_scores.tsv` | Main result file containing the metabolic activity score values. Columns represent the samples in the original gene expression file, and rows represent all the different metabolic tasks (stored in the `task_id` column). | | ||
| `cellfie_binary_scores.tsv` | Secondary result file that will only be generated if the flag `--binary_scores` is specified. It has the same structure as the main result file, but contains the binary interpretation of the activity of a metabolic task (`0` if the task is considered inactive, `1` if the task is considered active). | | ||
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A standard run of the CellFie framework should produce a `cellfie_scores.tsv` file similar to the following: | ||
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``` | ||
task_id S1 S2 S3 S4 | ||
0 X1 0.076515 0.671443 0.050100 0.733470 | ||
1 X2 0.863416 0.561653 1.204112 1.253820 | ||
2 X3 1.354543 0.889970 1.586738 2.489626 | ||
3 X4 1.195976 1.961420 1.423547 1.644596 | ||
4 X5 1.554831 1.785477 1.541452 1.704194 | ||
.. ... ... ... ... ... | ||
``` | ||
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Metabolic tasks are stored using their internal IDs, and their metadata can easily retrieved at the [task_info/](https://github.com/bsc-life/mteapy/tree/main/task_info) folder at the MTEApy repository. |
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--- | ||
title: The TIDE Framework | ||
title: TIDE | ||
layout: default | ||
id: tide | ||
nav_order: 2 | ||
--- | ||
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# **The TIDE Framework** | ||
# **The TIDE framework** | ||
{: .no_toc } | ||
*** | ||
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The **TIDE framework** was originally published by Dougherty _et al._ in 2021 (see [https://doi.org/10.1016/j.celrep.2021.108836](https://doi.org/10.1016/j.celrep.2021.108836)). | ||
## Table of contents | ||
{: .no_toc .text-delta } | ||
1. TOC | ||
{:toc} | ||
*** | ||
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## **Command-line arguments** | ||
## Description | ||
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| Argument | Shorcut | Description | | ||
| -------- | ------- | ----------- | | ||
| | ||
The **T**ask **I**nferred from **D**ifferential **E**xpression (TIDE) framework is a contraint-based metabolic modeling framework that was originally published by [Dougherty _et al._, 2021](https://doi.org/10.1016/j.celrep.2021.108836). It leverages the use of mathematical descriptions of metabolic functions (metabolic tasks) and the results of a Differential Expression Analysis to study metabolic perturbations in a case control assay. | ||
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## Command options | ||
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| Argument | Shortcut | Description | Default | | ||
|:-------- |:-------- |:----------- |:------- | | ||
| `dea_file` | | Filename for a differential expression analysis results file. It should contain at least three columns: genic (string), log-FC (numeric) and significance (numeric, e.g.: p-value, adjusted p-value, FDR). | | | ||
| `--delim` | `-d` | Field delimiter for inputed file. | `\t` | | ||
| `-out` | `-o` | Name (and location) to store the analysis' results. They will be stored in a tab-sepparated file, so filenames should contain the `.tsv` or `.txt` extensions. | `tide_results.tsv` | | ||
| `--gene_col` | | Name of the column in the inputed file containing gene names/symbols. | `geneID` | | ||
| `--lfc_col` | | Name of the column in the inputed file containing log-FC values. | `log2FoldChange` | | ||
| `--pvalue_col` | | Name of the column in the inputed file containing significance values. Only required if the flag `--mask_lfc_values` is `True`. | `padj` | | ||
| `--alpha` | `-a` | Significance threshold to mask log-FC. Only required if the flag `--mask_lfc_values` is `True`. | `0.05` | | ||
| `--n_permutations` | `-n` | Number of permutations to infer p-values for the metabolic scores. The resolution of the computed p-values will depend on this number. | `1000` | | ||
| `--n_cpus` | | Number of CPUs for parallel execution. | `1` | | ||
| `--or_func` | | Name of the function that will be used to resolve OR relationships in gene-protein-reaction (GPR) rules. Possible values are `absmax`, which will return the absolute maximum value, and `max`, which will return the maximum value. | `absmax` | | ||
| `--mask_lfc_values` | | Flag to indicate whether to mask log-FC values to 0 according to their significance. That is, if a log-FC value is non-significant (determined by the user), they will be masked to 0. | `False` | | ||
| `--random_scores` | | Flag to indicate whether to return the null distribution of random scores used to inferr significance with the results file. | `False` | | ||
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## Usage example | ||
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### Differential expression analysis | ||
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The first thing that the TIDE framework requires is a Differential Expression Analysis (DEA) result. Usually, this kind of data is stored in a tabular format and contains at least three columns: gene names/symbols, expression change values (log-FC) and significancy (p-value). | ||
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A typical DEA result will look like the following: | ||
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``` | ||
geneID geneSymbol log2FoldChange padj | ||
0 ENSG00000000003 TSPAN6 3.710229 0.259406 | ||
1 ENSG00000000005 TNMD -2.437056 0.485180 | ||
2 ENSG00000000419 DPM1 8.749658 0.802934 | ||
3 ENSG00000000457 SCYL3 -10.409959 0.051220 | ||
4 ENSG00000000460 FIRRM -0.977916 0.926198 | ||
... ... ... ... ... | ||
``` | ||
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### Running TIDE | ||
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To run the TIDE framework using the command-line, the command `run-mtea TIDE` should be used with the desired arguments. A typical TIDE analysis is run using a range of `1,000` to `10,000` permutations, the `absmax` function to evaluate OR GPR rules, and selecting the `--mask_lfc_values` flag, which will mask non-significant log-FC values to 0. | ||
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{: .note} | ||
Only the **Human-GEM** and its metabolic tasks are implemented, so the framework will only take in **EnsemblIDs** as valid genic nomenclature. We are working to allow for any metabolic model and metabolic tasks to be used for more customisable analyses! | ||
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```sh | ||
run-mtea TIDE dea_file.tsv \ | ||
-o results/tide_results.tsv \ | ||
-n 1000 \ | ||
--n_cpus 4 \ | ||
--or_func absmax \ | ||
--gene_col geneID \ | ||
--lfc_col log2FoldChange \ | ||
--pvalue_col padj \ | ||
-a 0.05 \ | ||
--mask_lfc_values | ||
``` | ||
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### Understanding the TIDE results | ||
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Once the analysis is run, a tabular file containing the analysis results will be saved into the inputed location. The results file will contain 7 columns: a task ID, the metabolic score, the mean random score obtained during the permutation test, its associated p-value, and three more columns detailing the metabolic task description, metabolic system and subsystem. | ||
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``` | ||
task_id score random_score pvalue task_description metabolic_system metabolic_subsystem | ||
0 X159 1.150921 -0.201177 0.000 Linolenate degradation Lipids Metabolism Fatty Acid Metabolism | ||
1 X164 1.216932 -0.185784 0.001 Arachidonate degradation Lipids Metabolism Fatty Acid Metabolism | ||
2 X160 1.026041 -0.184708 0.001 Linoleate degradation Lipids Metabolism Fatty Acid Metabolism | ||
3 X107 1.228656 -0.178454 0.001 Conversion of lysine to L-2-Aminoadipate Amino Acids Metabolism Lysine Metabolism | ||
4 X162 0.857837 -0.182584 0.001 gamma-Linolenate degradation Lipids Metabolism Fatty Acid Metabolism | ||
.. ... ... ... ... ... ... ... | ||
``` | ||
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The results can then be used to explore the metabolic changes of a case-control sample. | ||
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## The TIDE-essential framework | ||
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{: .highlight} | ||
Under construction! Please, come back soon. |