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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Run the following command to take the quiz\n",
"IRdisplay::display_html('
')"
]
},
{
"cell_type": "markdown",
"id": "0f0c9233",
"metadata": {},
"source": [
"### Data preparation\n",
"The GSA method is freely available as standalone package in CRAN repository. We can use the following code to install the package."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "c3efb930",
"metadata": {},
"outputs": [],
"source": [
"# Install GSA from CRAN\n",
"suppressMessages({if (!require(\"GSA\"))\n",
" suppressWarnings(install.packages(\"GSA\"))\n",
"})\n",
"suppressMessages({\n",
" library(GSA)\n",
"})"
]
},
{
"cell_type": "markdown",
"id": "e4a2ed98",
"metadata": {},
"source": [
"The GSA method require an expression matrix, a numeric vector containing class of each sample and a vector of genes the inputs. We can easily get those inputs by load the data that we processed in the [**Module 01** ](./Module01-GEO_Data_Processing.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "3982c128",
"metadata": {},
"outputs": [],
"source": [
"# Loading expression data with groups\n",
"data <- readRDS(\"./data/GSE48350.rds\")\n",
"expression_data <- data$expression_data\n",
"norm_expression_data <- data$norm_expression_data\n",
"groups <- data$groups"
]
},
{
"cell_type": "markdown",
"id": "ad173974",
"metadata": {},
"source": [
"We can also use the sample approach available in the [**Module 01** ](./Module01-GEO_Data_Processing.ipynb) to map the probe IDs to gene symbols. The step-by-step coding instruction is shown below:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "e0d1acf4",
"metadata": {},
"outputs": [],
"source": [
"# Get the probe IDs\n",
"expression_data$PROBEID <- rownames(expression_data)\n",
"probeIDs <- rownames(expression_data)\n",
"# Perform gene mapping\n",
"suppressMessages({\n",
" annotLookup <- AnnotationDbi::select(hgu133plus2.db, keys = probeIDs, columns = c('PROBEID', 'GENENAME', 'SYMBOL'))\n",
"})\n",
"# Merge DE result data frame with annotation table\n",
"new_expression_data = merge(annotLookup, expression_data, by=\"PROBEID\")\n",
"# Remove NA value\n",
"new_expression_data <- new_expression_data[!is.na(new_expression_data$SYMBOL),]\n",
"# Remove duplicated genes symbol\n",
"new_expression_data <- new_expression_data[!duplicated(new_expression_data$SYMBOL,fromLast=FALSE),]\n",
"rownames(new_expression_data) <- new_expression_data$SYMBOL\n",
"# Drop PROBEID, GENENAME, and SYMBOL columns\n",
"new_expression_data <- new_expression_data[,-c(1:3)]\n",
"genenames= rownames(new_expression_data)"
]
},
{
"cell_type": "markdown",
"id": "f8acf4f4",
"metadata": {},
"source": [
"### GSA Enrichment analysis using GO terms\n",
"Using data obtaiend from the previous step, we can run the GSA method by callinf the function `GSA`. We can reuse `GO_term_hallmark` and `KEGG_hallmark` loaded in FGSEA to perform analysis. The code details are shown below:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "e026aa03",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"perm= 10 / 200 \n",
"perm= 20 / 200 \n",
"perm= 30 / 200 \n",
"perm= 40 / 200 \n",
"perm= 50 / 200 \n",
"perm= 60 / 200 \n",
"perm= 70 / 200 \n",
"perm= 80 / 200 \n",
"perm= 90 / 200 \n",
"perm= 100 / 200 \n",
"perm= 110 / 200 \n",
"perm= 120 / 200 \n",
"perm= 130 / 200 \n",
"perm= 140 / 200 \n",
"perm= 150 / 200 \n",
"perm= 160 / 200 \n",
"perm= 170 / 200 \n",
"perm= 180 / 200 \n",
"perm= 190 / 200 \n",
"perm= 200 / 200 \n"
]
}
],
"source": [
"# Getting\n",
"genesets = GO_term_hallmark\n",
"GSA.obj<-GSA(as.matrix(new_expression_data),as.numeric(groups$groups), genenames=genenames, genesets=genesets, resp.type=\"Two class unpaired\",nperms=100,random.seed=1)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "ebba9b67",
"metadata": {},
"outputs": [],
"source": [
"# List the results from a GSA analysis\n",
"res <- GSA.listsets(GSA.obj, geneset.names=names(genesets),FDRcut=.5)"
]
},
{
"cell_type": "markdown",
"id": "df55e53c",
"metadata": {},
"source": [
"A table of the negative gene sets. “Negative” means that lower expression of most genes in the gene set correlates with higher values of the phenotype y. Eg for two classes coded 1,2, lower expression correlates with class 2.\n",
"\n"
]
},
{
"cell_type": "markdown",
"source": [
"
\n",
" Please note that the output generated from GSA would be varied depending on R environment and software version. Especially, when the users run the scripts using\n",
" User-managed Notebooks instances which have a preinstalled suite of packages.\n",
"
"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 66,
"id": "ae97649e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": "
\nA matrix: 6 × 5 of type chr\n\n\t| Gene_set | Gene_set_name | Score | p-value | FDR |
|---|
\n\n\n\t| 9 | GO:0000045 | -0.3745 | 0 | 0 |
\n\t| 55 | GO:0000422 | -0.5436 | 0 | 0 |
\n\t| 620 | GO:0006091 | -0.4743 | 0 | 0 |
\n\t| 631 | GO:0006120 | -1.1936 | 0 | 0 |
\n\t| 729 | GO:0006457 | -0.4546 | 0 | 0 |
\n\t| 785 | GO:0006605 | -0.4504 | 0 | 0 |
\n\n
\n",
"text/markdown": "\nA matrix: 6 × 5 of type chr\n\n| Gene_set | Gene_set_name | Score | p-value | FDR |\n|---|---|---|---|---|\n| 9 | GO:0000045 | -0.3745 | 0 | 0 |\n| 55 | GO:0000422 | -0.5436 | 0 | 0 |\n| 620 | GO:0006091 | -0.4743 | 0 | 0 |\n| 631 | GO:0006120 | -1.1936 | 0 | 0 |\n| 729 | GO:0006457 | -0.4546 | 0 | 0 |\n| 785 | GO:0006605 | -0.4504 | 0 | 0 |\n\n",
"text/latex": "A matrix: 6 × 5 of type chr\n\\begin{tabular}{lllll}\n Gene\\_set & Gene\\_set\\_name & Score & p-value & FDR\\\\\n\\hline\n\t 9 & GO:0000045 & -0.3745 & 0 & 0\\\\\n\t 55 & GO:0000422 & -0.5436 & 0 & 0\\\\\n\t 620 & GO:0006091 & -0.4743 & 0 & 0\\\\\n\t 631 & GO:0006120 & -1.1936 & 0 & 0\\\\\n\t 729 & GO:0006457 & -0.4546 & 0 & 0\\\\\n\t 785 & GO:0006605 & -0.4504 & 0 & 0\\\\\n\\end{tabular}\n",
"text/plain": " Gene_set Gene_set_name Score p-value FDR\n[1,] 9 GO:0000045 -0.3745 0 0 \n[2,] 55 GO:0000422 -0.5436 0 0 \n[3,] 620 GO:0006091 -0.4743 0 0 \n[4,] 631 GO:0006120 -1.1936 0 0 \n[5,] 729 GO:0006457 -0.4546 0 0 \n[6,] 785 GO:0006605 -0.4504 0 0 "
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"neg.table <-res$negative\n",
"head(neg.table)"
]
},
{
"cell_type": "markdown",
"id": "c237dea8",
"metadata": {},
"source": [
"A table of the positive gene sets. \"Positive\" means that higher expression of most genes in the gene set correlates with higher values of the phenotype y."
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "e640ff47",
"metadata": {},
"outputs": [
{
"data": {
"text/html": "
\nA matrix: 6 × 5 of type chr\n\n\t| Gene_set | Gene_set_name | Score | p-value | FDR |
|---|
\n\n\n\t| 1115 | GO:0007422 | 0.361 | 0 | 0 |
\n\t| 1215 | GO:0008360 | 0.3628 | 0 | 0 |
\n\t| 1688 | GO:0014037 | 0.6887 | 0 | 0 |
\n\t| 1689 | GO:0014044 | 0.95 | 0 | 0 |
\n\t| 1885 | GO:0017145 | 0.5955 | 0 | 0 |
\n\t| 2100 | GO:0022011 | 1.0103 | 0 | 0 |
\n\n
\n",
"text/markdown": "\nA matrix: 6 × 5 of type chr\n\n| Gene_set | Gene_set_name | Score | p-value | FDR |\n|---|---|---|---|---|\n| 1115 | GO:0007422 | 0.361 | 0 | 0 |\n| 1215 | GO:0008360 | 0.3628 | 0 | 0 |\n| 1688 | GO:0014037 | 0.6887 | 0 | 0 |\n| 1689 | GO:0014044 | 0.95 | 0 | 0 |\n| 1885 | GO:0017145 | 0.5955 | 0 | 0 |\n| 2100 | GO:0022011 | 1.0103 | 0 | 0 |\n\n",
"text/latex": "A matrix: 6 × 5 of type chr\n\\begin{tabular}{lllll}\n Gene\\_set & Gene\\_set\\_name & Score & p-value & FDR\\\\\n\\hline\n\t 1115 & GO:0007422 & 0.361 & 0 & 0\\\\\n\t 1215 & GO:0008360 & 0.3628 & 0 & 0\\\\\n\t 1688 & GO:0014037 & 0.6887 & 0 & 0\\\\\n\t 1689 & GO:0014044 & 0.95 & 0 & 0\\\\\n\t 1885 & GO:0017145 & 0.5955 & 0 & 0\\\\\n\t 2100 & GO:0022011 & 1.0103 & 0 & 0\\\\\n\\end{tabular}\n",
"text/plain": " Gene_set Gene_set_name Score p-value FDR\n[1,] 1115 GO:0007422 0.361 0 0 \n[2,] 1215 GO:0008360 0.3628 0 0 \n[3,] 1688 GO:0014037 0.6887 0 0 \n[4,] 1689 GO:0014044 0.95 0 0 \n[5,] 1885 GO:0017145 0.5955 0 0 \n[6,] 2100 GO:0022011 1.0103 0 0 "
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos.table <-res$positive\n",
"head(pos.table)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "79687f5d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": "
\nA matrix: 12 × 2 of type chr\n\n\t| Gene | Score |
|---|
\n\n\n\t| CEBPA | 1.553 |
\n\t| NAGS | 0.686 |
\n\t| OTC | 0.396 |
\n\t| ORC1 | 0.2 |
\n\t| CPS1 | -0.097 |
\n\t| ARG1 | -0.632 |
\n\t| SLC25A2 | -0.782 |
\n\t| ORC2 | -0.872 |
\n\t| ASL | -0.893 |
\n\t| ASS1 | -1.177 |
\n\t| SLC25A15 | -1.925 |
\n\t| ARG2 | -2.268 |
\n\n
\n",
"text/markdown": "\nA matrix: 12 × 2 of type chr\n\n| Gene | Score |\n|---|---|\n| CEBPA | 1.553 |\n| NAGS | 0.686 |\n| OTC | 0.396 |\n| ORC1 | 0.2 |\n| CPS1 | -0.097 |\n| ARG1 | -0.632 |\n| SLC25A2 | -0.782 |\n| ORC2 | -0.872 |\n| ASL | -0.893 |\n| ASS1 | -1.177 |\n| SLC25A15 | -1.925 |\n| ARG2 | -2.268 |\n\n",
"text/latex": "A matrix: 12 × 2 of type chr\n\\begin{tabular}{ll}\n Gene & Score\\\\\n\\hline\n\t CEBPA & 1.553 \\\\\n\t NAGS & 0.686 \\\\\n\t OTC & 0.396 \\\\\n\t ORC1 & 0.2 \\\\\n\t CPS1 & -0.097\\\\\n\t ARG1 & -0.632\\\\\n\t SLC25A2 & -0.782\\\\\n\t ORC2 & -0.872\\\\\n\t ASL & -0.893\\\\\n\t ASS1 & -1.177\\\\\n\t SLC25A15 & -1.925\\\\\n\t ARG2 & -2.268\\\\\n\\end{tabular}\n",
"text/plain": " Gene Score \n [1,] CEBPA 1.553 \n [2,] NAGS 0.686 \n [3,] OTC 0.396 \n [4,] ORC1 0.2 \n [5,] CPS1 -0.097\n [6,] ARG1 -0.632\n [7,] SLC25A2 -0.782\n [8,] ORC2 -0.872\n [9,] ASL -0.893\n[10,] ASS1 -1.177\n[11,] SLC25A15 -1.925\n[12,] ARG2 -2.268"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Individual gene scores from a gene set analysis\n",
"# look at 10th gene set\n",
"GSA.genescores(10, genesets, GSA.obj, genenames)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "1cfde1f8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "plot without title",
"image/png": 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},
"metadata": {
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"width": 420,
"height": 420
}
},
"output_type": "display_data"
}
],
"source": [
"# Plot the result, this function makes a plot of the significant gene sets, based on a call to the GSA (Gene set analysis) function.\n",
"suppressWarnings(GSA.plot(GSA.obj, fac=1, FDRcut = 0.5))"
]
},
{
"cell_type": "markdown",
"id": "7107ef92",
"metadata": {},
"source": [
"### GSA Enrichment analysis using KEGG pathways\n",
"We can use the same procedure to per enrichment analyis with KEGG pathway. All the codes are similar but `genesets` are assigned from `KEGG_hallmark`. The code is shown below."
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "361aff0b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"perm= 10 / 100 \n",
"perm= 20 / 100 \n",
"perm= 30 / 100 \n",
"perm= 40 / 100 \n",
"perm= 50 / 100 \n",
"perm= 60 / 100 \n",
"perm= 70 / 100 \n",
"perm= 80 / 100 \n",
"perm= 90 / 100 \n",
"perm= 100 / 100 \n"
]
}
],
"source": [
"genesets = KEGG_hallmark\n",
"GSA.obj<-GSA(as.matrix(new_expression_data),as.numeric(groups$groups), genenames=genenames, genesets=genesets, resp.type=\"Two class unpaired\", nperms=100, random.seed=1)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "00afb710",
"metadata": {},
"outputs": [],
"source": [
"# List the results from a GSA analysis\n",
"res <- GSA.listsets(GSA.obj, geneset.names=names(genesets),FDRcut=.5)"
]
},
{
"cell_type": "markdown",
"id": "e15cdae3",
"metadata": {},
"source": [
"A table of the negative gene sets. \"Negative\" means that lower expression of most genes in the gene set correlates with higher values of the phenotype y. Eg for two classes coded 1,2, lower expression correlates with class 2."
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "c9e7aad4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": "
\nA matrix: 6 × 5 of type chr\n\n\t| Gene_set | Gene_set_name | Score | p-value | FDR |
|---|
\n\n\n\t| 218 | hsa04260 | -0.6738 | 0 | 0 |
\n\t| 328 | hsa05022 | -0.5187 | 0 | 0 |
\n\t| 256 | hsa04714 | -0.5235 | 0.01 | 0.4543 |
\n\t| 323 | hsa05012 | -0.738 | 0.01 | 0.4543 |
\n\t| 325 | hsa05016 | -0.7177 | 0.01 | 0.4543 |
\n\t| 327 | hsa05020 | -0.6895 | 0.01 | 0.4543 |
\n\n
\n",
"text/markdown": "\nA matrix: 6 × 5 of type chr\n\n| Gene_set | Gene_set_name | Score | p-value | FDR |\n|---|---|---|---|---|\n| 218 | hsa04260 | -0.6738 | 0 | 0 |\n| 328 | hsa05022 | -0.5187 | 0 | 0 |\n| 256 | hsa04714 | -0.5235 | 0.01 | 0.4543 |\n| 323 | hsa05012 | -0.738 | 0.01 | 0.4543 |\n| 325 | hsa05016 | -0.7177 | 0.01 | 0.4543 |\n| 327 | hsa05020 | -0.6895 | 0.01 | 0.4543 |\n\n",
"text/latex": "A matrix: 6 × 5 of type chr\n\\begin{tabular}{lllll}\n Gene\\_set & Gene\\_set\\_name & Score & p-value & FDR\\\\\n\\hline\n\t 218 & hsa04260 & -0.6738 & 0 & 0 \\\\\n\t 328 & hsa05022 & -0.5187 & 0 & 0 \\\\\n\t 256 & hsa04714 & -0.5235 & 0.01 & 0.4543\\\\\n\t 323 & hsa05012 & -0.738 & 0.01 & 0.4543\\\\\n\t 325 & hsa05016 & -0.7177 & 0.01 & 0.4543\\\\\n\t 327 & hsa05020 & -0.6895 & 0.01 & 0.4543\\\\\n\\end{tabular}\n",
"text/plain": " Gene_set Gene_set_name Score p-value FDR \n[1,] 218 hsa04260 -0.6738 0 0 \n[2,] 328 hsa05022 -0.5187 0 0 \n[3,] 256 hsa04714 -0.5235 0.01 0.4543\n[4,] 323 hsa05012 -0.738 0.01 0.4543\n[5,] 325 hsa05016 -0.7177 0.01 0.4543\n[6,] 327 hsa05020 -0.6895 0.01 0.4543"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"neg.table <-res$negative\n",
"head(neg.table)"
]
},
{
"cell_type": "markdown",
"id": "e226ae92",
"metadata": {},
"source": [
"A table of the positive gene sets. \"Positive\" means that higher expression of most genes in the gene set correlates with higher values of the phenotype y. See \"negative\" above for more info."
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "bcd7ff3e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": "
\nA matrix: 2 × 5 of type chr\n\n\t| Gene_set | Gene_set_name | Score | p-value | FDR |
|---|
\n\n\n\t| 169 | hsa04520 | 0.5032 | 0 | 0 |
\n\t| 173 | hsa04810 | 0.2173 | 0 | 0 |
\n\n
\n",
"text/markdown": "\nA matrix: 2 × 5 of type chr\n\n| Gene_set | Gene_set_name | Score | p-value | FDR |\n|---|---|---|---|---|\n| 169 | hsa04520 | 0.5032 | 0 | 0 |\n| 173 | hsa04810 | 0.2173 | 0 | 0 |\n\n",
"text/latex": "A matrix: 2 × 5 of type chr\n\\begin{tabular}{lllll}\n Gene\\_set & Gene\\_set\\_name & Score & p-value & FDR\\\\\n\\hline\n\t 169 & hsa04520 & 0.5032 & 0 & 0\\\\\n\t 173 & hsa04810 & 0.2173 & 0 & 0\\\\\n\\end{tabular}\n",
"text/plain": " Gene_set Gene_set_name Score p-value FDR\n[1,] 169 hsa04520 0.5032 0 0 \n[2,] 173 hsa04810 0.2173 0 0 "
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pos.table <-res$positive\n",
"head(pos.table)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "0d676c13",
"metadata": {},
"outputs": [
{
"data": {
"text/html": "
\nA matrix: 30 × 2 of type chr\n\n\t| Gene | Score |
|---|
\n\n\n\t| OGDH | 1.545 |
\n\t| IDH2 | 1.427 |
\n\t| SUCLG2 | 0.717 |
\n\t| PCK1 | -0.022 |
\n\t| PCK2 | -0.16 |
\n\t| PC | -0.213 |
\n\t| DLST | -0.235 |
\n\t| PDHA2 | -0.239 |
\n\t| ACO1 | -0.6 |
\n\t| SDHC | -0.676 |
\n\t| IDH1 | -0.851 |
\n\t| IDH3B | -1.241 |
\n\t| PDHB | -1.449 |
\n\t| DLD | -1.453 |
\n\t| IDH3G | -1.621 |
\n\t| SDHB | -1.751 |
\n\t| SDHD | -1.842 |
\n\t| DLAT | -1.904 |
\n\t| MDH1 | -2.045 |
\n\t| SDHA | -2.068 |
\n\t| ACLY | -2.094 |
\n\t| SUCLG1 | -2.193 |
\n\t| IDH3A | -2.202 |
\n\t| CS | -2.314 |
\n\t| OGDHL | -2.378 |
\n\t| FH | -2.453 |
\n\t| MDH2 | -2.547 |
\n\t| SUCLA2 | -2.584 |
\n\t| PDHA1 | -3.078 |
\n\t| ACO2 | -3.559 |
\n\n
\n",
"text/markdown": "\nA matrix: 30 × 2 of type chr\n\n| Gene | Score |\n|---|---|\n| OGDH | 1.545 |\n| IDH2 | 1.427 |\n| SUCLG2 | 0.717 |\n| PCK1 | -0.022 |\n| PCK2 | -0.16 |\n| PC | -0.213 |\n| DLST | -0.235 |\n| PDHA2 | -0.239 |\n| ACO1 | -0.6 |\n| SDHC | -0.676 |\n| IDH1 | -0.851 |\n| IDH3B | -1.241 |\n| PDHB | -1.449 |\n| DLD | -1.453 |\n| IDH3G | -1.621 |\n| SDHB | -1.751 |\n| SDHD | -1.842 |\n| DLAT | -1.904 |\n| MDH1 | -2.045 |\n| SDHA | -2.068 |\n| ACLY | -2.094 |\n| SUCLG1 | -2.193 |\n| IDH3A | -2.202 |\n| CS | -2.314 |\n| OGDHL | -2.378 |\n| FH | -2.453 |\n| MDH2 | -2.547 |\n| SUCLA2 | -2.584 |\n| PDHA1 | -3.078 |\n| ACO2 | -3.559 |\n\n",
"text/latex": "A matrix: 30 × 2 of type chr\n\\begin{tabular}{ll}\n Gene & Score\\\\\n\\hline\n\t OGDH & 1.545 \\\\\n\t IDH2 & 1.427 \\\\\n\t SUCLG2 & 0.717 \\\\\n\t PCK1 & -0.022\\\\\n\t PCK2 & -0.16 \\\\\n\t PC & -0.213\\\\\n\t DLST & -0.235\\\\\n\t PDHA2 & -0.239\\\\\n\t ACO1 & -0.6 \\\\\n\t SDHC & -0.676\\\\\n\t IDH1 & -0.851\\\\\n\t IDH3B & -1.241\\\\\n\t PDHB & -1.449\\\\\n\t DLD & -1.453\\\\\n\t IDH3G & -1.621\\\\\n\t SDHB & -1.751\\\\\n\t SDHD & -1.842\\\\\n\t DLAT & -1.904\\\\\n\t MDH1 & -2.045\\\\\n\t SDHA & -2.068\\\\\n\t ACLY & -2.094\\\\\n\t SUCLG1 & -2.193\\\\\n\t IDH3A & -2.202\\\\\n\t CS & -2.314\\\\\n\t OGDHL & -2.378\\\\\n\t FH & -2.453\\\\\n\t MDH2 & -2.547\\\\\n\t SUCLA2 & -2.584\\\\\n\t PDHA1 & -3.078\\\\\n\t ACO2 & -3.559\\\\\n\\end{tabular}\n",
"text/plain": " Gene Score \n [1,] OGDH 1.545 \n [2,] IDH2 1.427 \n [3,] SUCLG2 0.717 \n [4,] PCK1 -0.022\n [5,] PCK2 -0.16 \n [6,] PC -0.213\n [7,] DLST -0.235\n [8,] PDHA2 -0.239\n [9,] ACO1 -0.6 \n[10,] SDHC -0.676\n[11,] IDH1 -0.851\n[12,] IDH3B -1.241\n[13,] PDHB -1.449\n[14,] DLD -1.453\n[15,] IDH3G -1.621\n[16,] SDHB -1.751\n[17,] SDHD -1.842\n[18,] DLAT -1.904\n[19,] MDH1 -2.045\n[20,] SDHA -2.068\n[21,] ACLY -2.094\n[22,] SUCLG1 -2.193\n[23,] IDH3A -2.202\n[24,] CS -2.314\n[25,] OGDHL -2.378\n[26,] FH -2.453\n[27,] MDH2 -2.547\n[28,] SUCLA2 -2.584\n[29,] PDHA1 -3.078\n[30,] ACO2 -3.559"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Individual gene scores from a gene set analysis\n",
"# look at 10th gene set\n",
"GSA.genescores(10, genesets, GSA.obj, genenames)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "2284bc1f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "plot without title",
"image/png": 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},
"metadata": {
"image/png": {
"width": 420,
"height": 420
}
},
"output_type": "display_data"
}
],
"source": [
"# Plot the result, this function makes a plot of the significant gene sets, based on a call to the GSA (Gene set analysis) function.\n",
"suppressWarnings(GSA.plot(GSA.obj, fac=1, FDRcut = 0.5))\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "67951b37",
"metadata": {
"tags": [
"remove-input"
]
},
"outputs": [
{
"data": {
"text/html": "\r\n\r\n
\r\n
\r\n\r\n
Quiz_Submodule4\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n\r\n
\r\n\r\n
\r\n
\r\n
\r\n\r\n
\r\n\r\n
\r\n\r\n\r\n
\r\n\r\n
\r\n\r\n\r\n\r\n
\r\n\r\n
\r\n\r\n
\r\n\r\n
\r\n\r\n
\r\n\r\n
\r\n\r\n
\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"IRdisplay::display_html('
')\n"
]
}
],
"metadata": {
"celltoolbar": "Tags",
"environment": {
"kernel": "ir",
"name": "r-cpu.4-1.m93",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/r-cpu.4-1:m93"
},
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "4.2.2"
},
"toc-showcode": true,
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}