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Corrected file path in cobra
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@soelmicheletti
soelmicheletti committed Oct 12, 2023
1 parent 7763a55 commit e01b1df
Showing 1 changed file with 33 additions and 3 deletions.
36 changes: 33 additions & 3 deletions netbooks/netZooPy/cobra.ipynb
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"cells": [
{
"cell_type": "markdown",
"id": "7e26ed88",
"metadata": {},
"source": [
"# Decomposing gene co-expression networks with COBRA (Python version)\n",
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},
{
"cell_type": "markdown",
"id": "8520e757",
"metadata": {},
"source": [
"## 1. Introduction\n",
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{
"cell_type": "code",
"execution_count": null,
"id": "fe567bf2",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "c27aff27",
"metadata": {},
"source": [
"On the server, we need to change the working directory to the `data` folder of the current useer."
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{
"cell_type": "code",
"execution_count": null,
"id": "7e5dc5ad",
"metadata": {},
"outputs": [],
"source": [
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{
"cell_type": "code",
"execution_count": null,
"id": "402463e2",
"metadata": {},
"outputs": [],
"source": [
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{
"cell_type": "code",
"execution_count": null,
"id": "1f43ac8c",
"metadata": {},
"outputs": [],
"source": [
"gene_expression = pd.read_csv(ppath+\"gene_expression_thca.csv\", index_col = 0).to_numpy()\n",
"metadata = pd.read_csv(ppath+\"data/thca_metadata.csv\", index_col = 0)\n",
"metadata = pd.read_csv(ppath+\"thca_metadata.csv\", index_col = 0)\n",
"batch = metadata['batch'].to_numpy()\n",
"cancer = metadata['status'].to_numpy()\n",
"sex = metadata['sex'].to_numpy()"
]
},
{
"cell_type": "markdown",
"id": "f01301b9",
"metadata": {},
"source": [
"Here gene_expression is a gene expression matrix for 19711 genes and 572 samples. Batch, cancer, and sex are sample-specific metadata as vectors of length 572."
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{
"cell_type": "code",
"execution_count": null,
"id": "eefe741a",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "e23e09b2",
"metadata": {},
"source": [
"## 2. Applications of COBRA\n",
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{
"cell_type": "code",
"execution_count": null,
"id": "56c0ce1f",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "19e30957",
"metadata": {},
"source": [
"For batch correction, the design matrix must contain an intercept in the first column, and the batches (encoded usy dummy coding for identifiability) in the remaining columns. "
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{
"cell_type": "code",
"execution_count": null,
"id": "008a3832",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "db8d69f1",
"metadata": {},
"source": [
"We get a design matrix with 17 covariates (an intercept and 16 for the dummy coding) for the 572 samples in our study. "
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{
"cell_type": "code",
"execution_count": null,
"id": "45b0a40d",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "9556f9b9",
"metadata": {},
"source": [
"We are now ready to fit COBRA"
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{
"cell_type": "code",
"execution_count": null,
"id": "c0b3776b",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "f07cae8c",
"metadata": {},
"source": [
"The batch corrected network consider only the mean effect after removing the contribution of the batch variables. It is computed as follows. "
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{
"cell_type": "code",
"execution_count": null,
"id": "feb8b5a5",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "3c577a75",
"metadata": {},
"source": [
"### 3.2 Differential co-expression analysis\n",
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{
"cell_type": "code",
"execution_count": null,
"id": "fc0a5747",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -205,6 +226,7 @@
},
{
"cell_type": "markdown",
"id": "21a412df",
"metadata": {},
"source": [
"In this case, the design matrix contains an intercept an a second column with an indicator for cancer/ healthy. The additional columns are for the variables we want to adjust for. Similarly as before, we consider the batch variable. "
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{
"cell_type": "code",
"execution_count": null,
"id": "d26518b1",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -224,6 +247,7 @@
},
{
"cell_type": "markdown",
"id": "0df93493",
"metadata": {},
"source": [
"We are now ready to fit COBRA and extract the component corresponding to the differential co-expression. Since the indicator variable for cancer is the second column in our design matrix, the COBRA-adjusted differential co-expression network corresponds to the second component of COBRA's decomposition. "
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{
"cell_type": "code",
"execution_count": null,
"id": "3887dba5",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "15b8e757",
"metadata": {},
"source": [
"### 3.3 Identifying the component for a covariate of interest\n",
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{
"cell_type": "code",
"execution_count": null,
"id": "c3f136eb",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "78f33e7b",
"metadata": {},
"source": [
"With this design, the last component of COBRA's decomposition describes the sex differes in cancer between male and females. "
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{
"cell_type": "code",
"execution_count": null,
"id": "a55644d4",
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "markdown",
"id": "8286994c",
"metadata": {},
"source": [
"## Reference\n",
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],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.10.12"
}
},
"nbformat": 4,
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