{
  "id": 460988,
  "title": " Predicting Gene Expression Changes",
  "url": "/competitions/open-problems-single-cell-perturbations/discussion/460988",
  "author_name": "vinit",
  "post_date": "2023-12-12T06:09:16.981000",
  "votes": 2,
  "comment_count": 0,
  "views": 0,
  "content": "<h1>Write-Ups Guide</h1>\n<p>**Small Molecule Impact on Gene Expression<br>\n**1. Problem Statement:<br>\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data. OurWrite-Ups Format <br>\n**Small Molecule Impact on Gene Expression<br>\n**1. Problem Statement:<br>\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data.Our objective is to predict the differential expression values for Myeloid and B cells based on a majority of compounds. The training data consists of measurements from 144 compounds in T cells (CD4+, CD8+, regulatory) and NK cells. However, only 10% of compounds have measurements in Myeloid and B cells. This scenario simulates a scientific context where predictions are needed for new cell types, but only limited measurements are available.</p>\n<p>**Exploratory Data Analysis (EDA):</p>\n<ul>\n<li>Investigated data distribution, missing values, and statistical properties.</li>\n<li>Explored relationships between molecular descriptors and gene expression.</li>\n<li>Visualized the distribution of gene expression levels across different cell types.</li>\n</ul>\n<p>**Model Architecture:</p>\n<ul>\n<li>Model selection as it showed promising results in initial experiments.</li>\n<li>Experimented with different architectures, considering the complex relationships between small molecules and gene expression.</li>\n</ul>\n<h3>Training:</h3>\n<p>**   Training Strategy:</p>\n<ul>\n<li>Trained the model on the available T cell data (CD4+, CD8+, regulatory) and NK cell data, which comprises the majority of compounds.</li>\n</ul>\n<h3>Transfer Learning:</h3>\n<ul>\n<li>Utilized transfer learning techniques to adapt the model to Myeloid and B cell predictions using the limited available data for these cell types.</li>\n</ul>\n<p>**Future Improvements:</p>\n<ul>\n<li>Considered potential enhancements, such as fine-tuning model architecture or incorporating external data.</li>\n</ul>\n<h1>Write-Ups Implementation</h1>\n<h1>Title: Predicting Gene Expression Changes in Different Cell Types due to Small Molecules</h1>\n<h2>Introduction:</h2>\n<ul>\n<li>Describe the problem and the dataset. Importance of understanding how small molecules impact gene expression in various cell types.</li>\n</ul>\n<h2>Dataset Overview:</h2>\n<ul>\n<li>Dataset key features:<ul>\n<li>cell_type: The annotated cell type of each cell based on RNA expression.</li>\n<li>sm_name: The primary name for the parent compound in a standardized representation.</li>\n<li>sm_lincs_id: The global LINCS ID for the parent compound.</li>\n<li>SMILES: Simplified molecular-input line-entry system.</li></ul></li>\n</ul>\n<h1>Exploratory Data Analysis (EDA):</h1>\n<ul>\n<li>Analysis of dataset, including visualizations and insights. Example:<br>\n`# Import necessary libraries<br>\nplt.figure(figsize=(10, 6))<br>\nsns.histplot(data['target_variable'], bins=50, kde=True)<br>\nplt.title('Distribution of Target Variable')<br>\nplt.xlabel('Differential Expression Values')<br>\nplt.ylabel('Frequency')<br>\nplt.show()</li>\n</ul>\n<h1>Summary statistics</h1>\n<p>print(data.describe())</p>\n<h1>Distribution of cell types</h1>\n<p>plt.figure(figsize=(12, 6))<br>\nsns.countplot(x='cell_type', data=data)<br>\nplt.title('Distribution of Cell Types')<br>\nplt.show()</p>\n<h1>Relationships between variables</h1>\n<p>plt.figure(figsize=(12, 8))<br>\nsns.scatterplot(x='sm_name', y='gene_A1BG', hue='cell_type', data=data)<br>\nplt.title('Gene Expression vs Small Molecule for A1BG')<br>\nplt.show()<br>\n`</p>\n<h4>EDA - Feature Analysis:</h4>\n<ul>\n<li><p>Distribution of gene expression features in T cells, NK cells, and the limited set of Myeloid and B cells.<br>\n`t_cell_genes = data[data['cell_type'].isin(['CD4+', 'CD8+', 'regulatory'])]['gene_expression']<br>\nnk_cell_genes = data[data['cell_type'] == 'NK']['gene_expression']<br>\nmyeloid_b_cell_genes = data[data['cell_type'].isin(['Myeloid', 'B'])]['gene_expression']</p>\n<p>plt.figure(figsize=(14, 8))<br>\nsns.kdeplot(t_cell_genes, label='T Cells (CD4+, CD8+, Regulatory)')<br>\nsns.kdeplot(nk_cell_genes, label='NK Cells')<br>\nsns.kdeplot(msns.kdeplot(myeloid_b_cell_genes, label='Myeloid and B Cells (Subset)')<br>\nplt.title('Distribution of Gene Expression Features Across Cell Types')<br>\nplt.xlabel('Gene Expression Values')<br>\nplt.ylabel('Density')<br>\nplt.legend()<br>\nplt.show()<br>\n`</p></li>\n</ul>\n<h1>Feature engineering on SMILES data (example: convert to molecular fingerprints)</h1>\n<h1>…</h1>\n<h1>Split data into train and test sets</h1>\n<p>X_train, X_test, y_train, y_test = train_test_split(data[['sm_name', 'cell_type', 'SMILES']], data['gene_A1BG'], test_size=0.2, random_state=42)</p>\n<h1>Random Forest Regressor</h1>\n<p>model = RandomForestRegressor(n_estimators=100, random_state=42)<br>\nmodel.fit(X_train, y_train)</p>\n<h1>Predictions on test set</h1>\n<p>predictions = model.predict(X_test)</p>\n<h1>Model Evaluation</h1>\n<p>mse = mean_squared_error(y_test, predictions)<br>\nprint(f'Mean Squared Error: {mse}')<br>\n`</p>",
  "messages": [
    {
      "id": 2558382,
      "postDate": "2023-12-12T06:09:16.980Z",
      "content": "<h1>Write-Ups Guide</h1>\n<p>**Small Molecule Impact on Gene Expression<br>\n**1. Problem Statement:<br>\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data. OurWrite-Ups Format <br>\n**Small Molecule Impact on Gene Expression<br>\n**1. Problem Statement:<br>\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data.Our objective is to predict the differential expression values for Myeloid and B cells based on a majority of compounds. The training data consists of measurements from 144 compounds in T cells (CD4+, CD8+, regulatory) and NK cells. However, only 10% of compounds have measurements in Myeloid and B cells. This scenario simulates a scientific context where predictions are needed for new cell types, but only limited measurements are available.</p>\n<p>**Exploratory Data Analysis (EDA):</p>\n<ul>\n<li>Investigated data distribution, missing values, and statistical properties.</li>\n<li>Explored relationships between molecular descriptors and gene expression.</li>\n<li>Visualized the distribution of gene expression levels across different cell types.</li>\n</ul>\n<p>**Model Architecture:</p>\n<ul>\n<li>Model selection as it showed promising results in initial experiments.</li>\n<li>Experimented with different architectures, considering the complex relationships between small molecules and gene expression.</li>\n</ul>\n<h3>Training:</h3>\n<p>**   Training Strategy:</p>\n<ul>\n<li>Trained the model on the available T cell data (CD4+, CD8+, regulatory) and NK cell data, which comprises the majority of compounds.</li>\n</ul>\n<h3>Transfer Learning:</h3>\n<ul>\n<li>Utilized transfer learning techniques to adapt the model to Myeloid and B cell predictions using the limited available data for these cell types.</li>\n</ul>\n<p>**Future Improvements:</p>\n<ul>\n<li>Considered potential enhancements, such as fine-tuning model architecture or incorporating external data.</li>\n</ul>\n<h1>Write-Ups Implementation</h1>\n<h1>Title: Predicting Gene Expression Changes in Different Cell Types due to Small Molecules</h1>\n<h2>Introduction:</h2>\n<ul>\n<li>Describe the problem and the dataset. Importance of understanding how small molecules impact gene expression in various cell types.</li>\n</ul>\n<h2>Dataset Overview:</h2>\n<ul>\n<li>Dataset key features:<ul>\n<li>cell_type: The annotated cell type of each cell based on RNA expression.</li>\n<li>sm_name: The primary name for the parent compound in a standardized representation.</li>\n<li>sm_lincs_id: The global LINCS ID for the parent compound.</li>\n<li>SMILES: Simplified molecular-input line-entry system.</li></ul></li>\n</ul>\n<h1>Exploratory Data Analysis (EDA):</h1>\n<ul>\n<li>Analysis of dataset, including visualizations and insights. Example:<br>\n`# Import necessary libraries<br>\nplt.figure(figsize=(10, 6))<br>\nsns.histplot(data['target_variable'], bins=50, kde=True)<br>\nplt.title('Distribution of Target Variable')<br>\nplt.xlabel('Differential Expression Values')<br>\nplt.ylabel('Frequency')<br>\nplt.show()</li>\n</ul>\n<h1>Summary statistics</h1>\n<p>print(data.describe())</p>\n<h1>Distribution of cell types</h1>\n<p>plt.figure(figsize=(12, 6))<br>\nsns.countplot(x='cell_type', data=data)<br>\nplt.title('Distribution of Cell Types')<br>\nplt.show()</p>\n<h1>Relationships between variables</h1>\n<p>plt.figure(figsize=(12, 8))<br>\nsns.scatterplot(x='sm_name', y='gene_A1BG', hue='cell_type', data=data)<br>\nplt.title('Gene Expression vs Small Molecule for A1BG')<br>\nplt.show()<br>\n`</p>\n<h4>EDA - Feature Analysis:</h4>\n<ul>\n<li><p>Distribution of gene expression features in T cells, NK cells, and the limited set of Myeloid and B cells.<br>\n`t_cell_genes = data[data['cell_type'].isin(['CD4+', 'CD8+', 'regulatory'])]['gene_expression']<br>\nnk_cell_genes = data[data['cell_type'] == 'NK']['gene_expression']<br>\nmyeloid_b_cell_genes = data[data['cell_type'].isin(['Myeloid', 'B'])]['gene_expression']</p>\n<p>plt.figure(figsize=(14, 8))<br>\nsns.kdeplot(t_cell_genes, label='T Cells (CD4+, CD8+, Regulatory)')<br>\nsns.kdeplot(nk_cell_genes, label='NK Cells')<br>\nsns.kdeplot(msns.kdeplot(myeloid_b_cell_genes, label='Myeloid and B Cells (Subset)')<br>\nplt.title('Distribution of Gene Expression Features Across Cell Types')<br>\nplt.xlabel('Gene Expression Values')<br>\nplt.ylabel('Density')<br>\nplt.legend()<br>\nplt.show()<br>\n`</p></li>\n</ul>\n<h1>Feature engineering on SMILES data (example: convert to molecular fingerprints)</h1>\n<h1>…</h1>\n<h1>Split data into train and test sets</h1>\n<p>X_train, X_test, y_train, y_test = train_test_split(data[['sm_name', 'cell_type', 'SMILES']], data['gene_A1BG'], test_size=0.2, random_state=42)</p>\n<h1>Random Forest Regressor</h1>\n<p>model = RandomForestRegressor(n_estimators=100, random_state=42)<br>\nmodel.fit(X_train, y_train)</p>\n<h1>Predictions on test set</h1>\n<p>predictions = model.predict(X_test)</p>\n<h1>Model Evaluation</h1>\n<p>mse = mean_squared_error(y_test, predictions)<br>\nprint(f'Mean Squared Error: {mse}')<br>\n`</p>",
      "rawMarkdown": "# Write-Ups Guide\n**Small Molecule Impact on Gene Expression\n**1. Problem Statement:\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data. OurWrite-Ups Format \n**Small Molecule Impact on Gene Expression\n**1. Problem Statement:\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data.Our objective is to predict the differential expression values for Myeloid and B cells based on a majority of compounds. The training data consists of measurements from 144 compounds in T cells (CD4+, CD8+, regulatory) and NK cells. However, only 10% of compounds have measurements in Myeloid and B cells. This scenario simulates a scientific context where predictions are needed for new cell types, but only limited measurements are available.\n\n**Exploratory Data Analysis (EDA):\n- Investigated data distribution, missing values, and statistical properties.\n- Explored relationships between molecular descriptors and gene expression.\n- Visualized the distribution of gene expression levels across different cell types.\n\n**Model Architecture:\n-  Model selection as it showed promising results in initial experiments.\n- Experimented with different architectures, considering the complex relationships between small molecules and gene expression.\n\n### Training:\n**   Training Strategy:\n   - Trained the model on the available T cell data (CD4+, CD8+, regulatory) and NK cell data, which comprises the majority of compounds.\n\n### Transfer Learning:\n   - Utilized transfer learning techniques to adapt the model to Myeloid and B cell predictions using the limited available data for these cell types.\n\n\n**Future Improvements:\n-  Considered potential enhancements, such as fine-tuning model architecture or incorporating external data.\n\n\n# Write-Ups Implementation\n\n\n# Title: Predicting Gene Expression Changes in Different Cell Types due to Small Molecules\n##   Introduction:\n-     Describe the problem and the dataset. Importance of understanding how small molecules impact gene expression in various cell types.\n\n##  Dataset Overview:\n-  Dataset key features:\n - cell_type: The annotated cell type of each cell based on RNA expression.\n - sm_name: The primary name for the parent compound in a standardized representation.\n - sm_lincs_id: The global LINCS ID for the parent compound.\n - SMILES: Simplified molecular-input line-entry system.\n\n# Exploratory Data Analysis (EDA):\n- Analysis of dataset, including visualizations and insights. Example:\n`# Import necessary libraries\nplt.figure(figsize=(10, 6))\nsns.histplot(data['target_variable'], bins=50, kde=True)\nplt.title('Distribution of Target Variable')\nplt.xlabel('Differential Expression Values')\nplt.ylabel('Frequency')\nplt.show()\n\n# Summary statistics\nprint(data.describe())\n\n# Distribution of cell types\nplt.figure(figsize=(12, 6))\nsns.countplot(x='cell_type', data=data)\nplt.title('Distribution of Cell Types')\nplt.show()\n\n# Relationships between variables\nplt.figure(figsize=(12, 8))\nsns.scatterplot(x='sm_name', y='gene_A1BG', hue='cell_type', data=data)\nplt.title('Gene Expression vs Small Molecule for A1BG')\nplt.show()\n`\n#### EDA - Feature Analysis:\n  - Distribution of gene expression features in T cells, NK cells, and the limited set of Myeloid and B cells.\n   `t_cell_genes = data[data['cell_type'].isin(['CD4+', 'CD8+', 'regulatory'])]['gene_expression']\n    nk_cell_genes = data[data['cell_type'] == 'NK']['gene_expression']\n    myeloid_b_cell_genes = data[data['cell_type'].isin(['Myeloid', 'B'])]['gene_expression']\n\n   plt.figure(figsize=(14, 8))\n  sns.kdeplot(t_cell_genes, label='T Cells (CD4+, CD8+, Regulatory)')\n  sns.kdeplot(nk_cell_genes, label='NK Cells')\n  sns.kdeplot(msns.kdeplot(myeloid_b_cell_genes, label='Myeloid and B Cells (Subset)')\n  plt.title('Distribution of Gene Expression Features Across Cell Types')\n  plt.xlabel('Gene Expression Values')\n  plt.ylabel('Density')\n  plt.legend()\n  plt.show()\n`\n\n# Feature engineering on SMILES data (example: convert to molecular fingerprints)\n# ...\n\n# Split data into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(data[['sm_name', 'cell_type', 'SMILES']], data['gene_A1BG'], test_size=0.2, random_state=42)\n\n\n# Random Forest Regressor\nmodel = RandomForestRegressor(n_estimators=100, random_state=42)\nmodel.fit(X_train, y_train)\n\n# Predictions on test set\npredictions = model.predict(X_test)\n\n# Model Evaluation\nmse = mean_squared_error(y_test, predictions)\nprint(f'Mean Squared Error: {mse}')\n`",
      "votes": 2
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2558382": "# Write-Ups Guide\n**Small Molecule Impact on Gene Expression\n**1. Problem Statement:\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data. OurWrite-Ups Format \n**Small Molecule Impact on Gene Expression\n**1. Problem Statement:\n    - The goal of this competition is to predict how small molecules influence gene expression in various cell types. Given a dataset of small molecules and their effects on gene expression in different cell lines, the objective is to develop a model that accurately predicts the impact of a given small molecule on gene expression in unseen data.Our objective is to predict the differential expression values for Myeloid and B cells based on a majority of compounds. The training data consists of measurements from 144 compounds in T cells (CD4+, CD8+, regulatory) and NK cells. However, only 10% of compounds have measurements in Myeloid and B cells. This scenario simulates a scientific context where predictions are needed for new cell types, but only limited measurements are available.\n\n**Exploratory Data Analysis (EDA):\n- Investigated data distribution, missing values, and statistical properties.\n- Explored relationships between molecular descriptors and gene expression.\n- Visualized the distribution of gene expression levels across different cell types.\n\n**Model Architecture:\n-  Model selection as it showed promising results in initial experiments.\n- Experimented with different architectures, considering the complex relationships between small molecules and gene expression.\n\n### Training:\n**   Training Strategy:\n   - Trained the model on the available T cell data (CD4+, CD8+, regulatory) and NK cell data, which comprises the majority of compounds.\n\n### Transfer Learning:\n   - Utilized transfer learning techniques to adapt the model to Myeloid and B cell predictions using the limited available data for these cell types.\n\n\n**Future Improvements:\n-  Considered potential enhancements, such as fine-tuning model architecture or incorporating external data.\n\n\n# Write-Ups Implementation\n\n\n# Title: Predicting Gene Expression Changes in Different Cell Types due to Small Molecules\n##   Introduction:\n-     Describe the problem and the dataset. Importance of understanding how small molecules impact gene expression in various cell types.\n\n##  Dataset Overview:\n-  Dataset key features:\n - cell_type: The annotated cell type of each cell based on RNA expression.\n - sm_name: The primary name for the parent compound in a standardized representation.\n - sm_lincs_id: The global LINCS ID for the parent compound.\n - SMILES: Simplified molecular-input line-entry system.\n\n# Exploratory Data Analysis (EDA):\n- Analysis of dataset, including visualizations and insights. Example:\n`# Import necessary libraries\nplt.figure(figsize=(10, 6))\nsns.histplot(data['target_variable'], bins=50, kde=True)\nplt.title('Distribution of Target Variable')\nplt.xlabel('Differential Expression Values')\nplt.ylabel('Frequency')\nplt.show()\n\n# Summary statistics\nprint(data.describe())\n\n# Distribution of cell types\nplt.figure(figsize=(12, 6))\nsns.countplot(x='cell_type', data=data)\nplt.title('Distribution of Cell Types')\nplt.show()\n\n# Relationships between variables\nplt.figure(figsize=(12, 8))\nsns.scatterplot(x='sm_name', y='gene_A1BG', hue='cell_type', data=data)\nplt.title('Gene Expression vs Small Molecule for A1BG')\nplt.show()\n`\n#### EDA - Feature Analysis:\n  - Distribution of gene expression features in T cells, NK cells, and the limited set of Myeloid and B cells.\n   `t_cell_genes = data[data['cell_type'].isin(['CD4+', 'CD8+', 'regulatory'])]['gene_expression']\n    nk_cell_genes = data[data['cell_type'] == 'NK']['gene_expression']\n    myeloid_b_cell_genes = data[data['cell_type'].isin(['Myeloid', 'B'])]['gene_expression']\n\n   plt.figure(figsize=(14, 8))\n  sns.kdeplot(t_cell_genes, label='T Cells (CD4+, CD8+, Regulatory)')\n  sns.kdeplot(nk_cell_genes, label='NK Cells')\n  sns.kdeplot(msns.kdeplot(myeloid_b_cell_genes, label='Myeloid and B Cells (Subset)')\n  plt.title('Distribution of Gene Expression Features Across Cell Types')\n  plt.xlabel('Gene Expression Values')\n  plt.ylabel('Density')\n  plt.legend()\n  plt.show()\n`\n\n# Feature engineering on SMILES data (example: convert to molecular fingerprints)\n# ...\n\n# Split data into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(data[['sm_name', 'cell_type', 'SMILES']], data['gene_A1BG'], test_size=0.2, random_state=42)\n\n\n# Random Forest Regressor\nmodel = RandomForestRegressor(n_estimators=100, random_state=42)\nmodel.fit(X_train, y_train)\n\n# Predictions on test set\npredictions = model.predict(X_test)\n\n# Model Evaluation\nmse = mean_squared_error(y_test, predictions)\nprint(f'Mean Squared Error: {mse}')\n`"
  }
}