updated tutorials

README.md

@@ -72,7 +72,7 @@ Detailed tutorials have been made on how to use some functionalities as a scaLR
 - **Differential gene expression analysis** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/differential_gene_expression/dge.ipynb)
 - **Gene recall curve** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/analysis/gene_recall_curve/gene_recall_curve.ipynb)
 - **Normalization** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/preprocessing/normalization.ipynb)
-- [Batch correction](https://github.com/infocusp/scaLR/blob/main/tutorials/preprocessing/batch_correction.ipynb)
+- **Batch correction** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/infocusp/scaLR/blob/main/tutorials/preprocessing/batch_correction.ipynb)
 - [SHAP analysis](https://github.com/infocusp/scaLR/blob/main/tutorials/analysis/shap_analysis/shap_heatmap.ipynb)
 
 ## Experiment Output Structure

tutorials/analysis/differential_gene_expression/dge.ipynb

@@ -165,7 +165,7 @@
    },
    "source": [
     "## <span style=\"color: steelblue;\">Downloading data</span>\n",
-    "- Downloading an anndata from `cellxgene` and making a subset anndata with 1000 genes for the downstream analysis."
+    "- Downloading an anndata from `cellxgene`([Jin et al. (2021) iScience](https://doi.org/10.1016/j.isci.2021.103115)) and making a subset anndata with 1000 genes for the downstream analysis."
    ]
   },
   {

tutorials/pipeline/scalr_pipeline.ipynb

@@ -8,7 +8,14 @@
    "source": [
     "<left><img src=\"https://github.com/infocusp/scaLR/raw/sj/fullntest_samples_analysis/img/scaLR_logo.png\" width=\"150\" height=\"180\"></left>\n",
     "\n",
-    "# Single-cell analysis using Low Resource (scaLR)"
+    "# <span style=\"color: steelblue;\">Single-cell analysis using Low Resource (scaLR)</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <span style=\"color: steelblue;\">Cloning scaLR</span>"
    ]
   },
   {
@@ -36,6 +43,13 @@
     "!git clone https://github.com/infocusp/scaLR.git"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Install all requirements after cloning the repository, excluding packages that are pre-installed in Colab."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -58,8 +72,12 @@
    },
    "outputs": [],
    "source": [
-    "# Install all requirements after cloning the repository, excluding the torch package since it is pre-installed in Colab.\n",
-    "!pip install $(grep -ivE \"torch\" scaLR/requirements.txt)"
+    "import sys\n",
+    "imported_packages = {pkg.split('.')[0] for pkg in sys.modules.keys()}\n",
+    "ignore_libraries = \"|\".join(imported_packages)\n",
+    "\n",
+    "!pip install $(grep -ivE \"$ignore_libraries\" scaLR/requirements.txt)\n",
+    "!pip install memory-profiler==0.61.0"
    ]
   },
   {