BranchesTags

Repository files navigation

differential_expression_analysis

This project is based on differential expression analysis with the following characteristics:

  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.

Requerimientos

  • R version 4.4.2

Descargar datasets

Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:

Rscriptdownload_rnaseq_datasets.r

Run DEA

Use the following command to perform differential expression analysis

Rscriptaed_limma_cbioportal_TMP.r

The script downloads all necessary libraries before running the analysis.

Operating details

Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.

Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.

Processing of clinical and sample data

  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'

RNASeq dataset processing

  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.

DEA with limma

  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)

About

tests of differential expression analysis

Resources

Readme
Activity
Custom properties

Stars

0 stars

Watchers

3 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
differential_expression_analysis
This project is based on differential expression analysis with the following characteristics:
  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.
Requerimientos
  • R version 4.4.2
Descargar datasets
Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:
Rscriptdownload_rnaseq_datasets.r
Run DEA
Use the following command to perform differential expression analysis
Rscriptaed_limma_cbioportal_TMP.r
The script downloads all necessary libraries before running the analysis.
Operating details
Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.
Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.
Processing of clinical and sample data
  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'
RNASeq dataset processing
  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.
DEA with limma
  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)
About
tests of differential expression analysis
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
differential_expression_analysis
This project is based on differential expression analysis with the following characteristics:
  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.
Requerimientos
  • R version 4.4.2
Descargar datasets
Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:
Rscriptdownload_rnaseq_datasets.r
Run DEA
Use the following command to perform differential expression analysis
Rscriptaed_limma_cbioportal_TMP.r
The script downloads all necessary libraries before running the analysis.
Operating details
Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.
Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.
Processing of clinical and sample data
  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'
RNASeq dataset processing
  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.
DEA with limma
  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)
About
tests of differential expression analysis
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
3 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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
differential_expression_analysis
This project is based on differential expression analysis with the following characteristics:
  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.
Requerimientos
  • R version 4.4.2
Descargar datasets
Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:
Rscriptdownload_rnaseq_datasets.r
Run DEA
Use the following command to perform differential expression analysis
Rscriptaed_limma_cbioportal_TMP.r
The script downloads all necessary libraries before running the analysis.
Operating details
Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.
Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.
Processing of clinical and sample data
  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'
RNASeq dataset processing
  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.
DEA with limma
  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)
About
tests of differential expression analysis
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
3 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); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
differential_expression_analysis
This project is based on differential expression analysis with the following characteristics:
  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.
Requerimientos
  • R version 4.4.2
Descargar datasets
Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:
Rscriptdownload_rnaseq_datasets.r
Run DEA
Use the following command to perform differential expression analysis
Rscriptaed_limma_cbioportal_TMP.r
The script downloads all necessary libraries before running the analysis.
Operating details
Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.
Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.
Processing of clinical and sample data
  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'
RNASeq dataset processing
  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.
DEA with limma
  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)
About
tests of differential expression analysis
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
differential_expression_analysis
This project is based on differential expression analysis with the following characteristics:
  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.
Requerimientos
  • R version 4.4.2
Descargar datasets
Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:
Rscriptdownload_rnaseq_datasets.r
Run DEA
Use the following command to perform differential expression analysis
Rscriptaed_limma_cbioportal_TMP.r
The script downloads all necessary libraries before running the analysis.
Operating details
Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.
Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.
Processing of clinical and sample data
  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'
RNASeq dataset processing
  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.
DEA with limma
  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)
About
tests of differential expression analysis
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, '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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
differential_expression_analysis
This project is based on differential expression analysis with the following characteristics:
  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.
Requerimientos
  • R version 4.4.2
Descargar datasets
Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:
Rscriptdownload_rnaseq_datasets.r
Run DEA
Use the following command to perform differential expression analysis
Rscriptaed_limma_cbioportal_TMP.r
The script downloads all necessary libraries before running the analysis.
Operating details
Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.
Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.
Processing of clinical and sample data
  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'
RNASeq dataset processing
  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.
DEA with limma
  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)
About
tests of differential expression analysis
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
3 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");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
differential_expression_analysis
This project is based on differential expression analysis with the following characteristics:
  • It uses the lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas Program (TCGA), obtained from cBioportal.
  • It uses the SEX variable to compare genes and obtain the differentially expressed ones.
  • Use the Limma library of R to perform the DEA.
Requerimientos
  • R version 4.4.2
Descargar datasets
Ejecutar el siguiente comando para obtener todos los datasets de cbioportal que tienen datos de RNA-Seq:
Rscriptdownload_rnaseq_datasets.r
Run DEA
Use the following command to perform differential expression analysis
Rscriptaed_limma_cbioportal_TMP.r
The script downloads all necessary libraries before running the analysis.
Operating details
Of the dataset downloaded from cBioportal, only the files "datasets/luad_tcga/data_clinical_patient.txt", "datasets/luad_tcga/data_clinical_sample.txt", and "datasets/luad_tcga/data_mrna_seq_v2_rsem.txt" are used for the analysis. These correspond to the patient clinical data, the sample data, and the gene expression data, respectively.
Expression data: The dataset has RNASeq data expressed in Transcripts per million (TPM) values ​​using RSEM.
Processing of clinical and sample data
  • Filter only the PATIENT_ID and SEX columns in clinical data.
  • Convert SEX values ​​to uppercase in clinical data.
  • Remove duplicate records in clinical data.
  • Filter only the SAMPLE_ID and PATIENT_ID columns in sample data.
  • Perform a merge between sample_data and clinical_data using PATIENT_ID as the key.
  • Keep only the SAMPLE_ID and SEX columns.
  • Replace hyphens (-) with periods (.) in SAMPLE_ID.
  • Ensure there are no duplicates by removing them from the dataset (there should be no duplicates at this point).
  • Count the number of samples per sex, and calculate the percentage of each sex in the sample.
  • Store the entire processed dataset in a dataframe called 'metadata'
RNASeq dataset processing
  • Genes with duplicate names (Hugo_Symbol) are identified.
  • Duplicate genes are removed, keeping only the first occurrence of each Hugo_Symbol.
  • The 'Entrez_Gene_Id' column is removed.
  • Rows where Hugo_Symbol is NA are removed.
  • Hugo_Symbol is assigned as row names.
  • Samples in metadata that are not present in rnaseq_tpm_data are removed.
  • The result is returned in a data frame named 'rnaseq_tpm_data'.
DEA with limma
  • Create boxplots to compare expression distributions between samples.
  • Convert TPM values ​​to a log2 scale (TPM + 1) to stabilize variance.
  • Generate histograms of the expression distribution before and after the log2 transformation.
  • Save the plots to a plots.pdf file.
  • Create a design matrix with the variable SEX as a factor.
  • Calculate the variance of each gene in the log-transformed data.
  • Eliminate genes with a variance less than the threshold (1e-4 by default).
  • Calculate the mean expression of each gene, set a percentile-based threshold (15% by default), and then eliminate genes with mean expression below this threshold.
  • Use the limma package to identify genes differentially expressed by sex (SEX), using the dataset modified with the two points above.
  • Fit a linear model (lmFit) and apply the eBayes adjustment.
  • Extracts the results with corrected p-values ​​(Benjamini-Hochberg correction).
  • Returns the table of differentially expressed genes.
  • Sorts the results of the differential analysis by the adjusted p-value (adj.P.Val), in ascending order (lowest to highest).
  • Sorts the results by the fold change (logFC), in descending order (highest to lowest).
  • Sorts the results in two steps: first by the adjusted p-value in ascending order and then by logFC in descending order.
  • Extracts the 50 genes with the lowest adjusted p-values ​​(the most significant).
  • Generates a "Volcano Plot" to visualize the relationship between logFC and -log10 (adjusted p-value). Distinguishes significant genes (in red) from non-significant ones (in black).
  • Saves the results in two CSV files (one with all results and one with the top 50)
About
tests of differential expression analysis
Resources
Readme
Activity
Custom properties
Stars
0 stars
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages