Comit all changes that were different than main

Module1-PreliminaryPython/GeneRegulationAssignment.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "id": "68b73000-7c00-4a6c-84ba-4968e4464e0c",
    "metadata": {
     "tags": []
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "880cf97f-a552-45e6-8b18-feb55b01d5f2",
    "metadata": {
     "tags": []
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "b83c65b8-2177-4652-9631-5b1d075498c4",
    "metadata": {},
    "outputs": [
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 54,
    "id": "65ca202b-2b50-461a-b835-0436b4a3e81d",
    "metadata": {
     "tags": []
@@ -145,7 +145,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Shelby Ardehali will write about Cellular Localization - Mitochondrial Targeting Signals: Sequences directing proteins to mitochondria.\n"
+      "All probabilities are zero - Nothing to choose!\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "can only concatenate list (not \"str\") to list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[54], line 6\u001b[0m\n\u001b[1;32m      2\u001b[0m student, iStudent \u001b[38;5;241m=\u001b[39m codes\u001b[38;5;241m.\u001b[39mchooseMember(students,probsStudents)\n\u001b[1;32m      4\u001b[0m topic, iTopic \u001b[38;5;241m=\u001b[39m codes\u001b[38;5;241m.\u001b[39mchooseMember(topics,probsTopics)\n\u001b[0;32m----> 6\u001b[0m propose \u001b[38;5;241m=\u001b[39m student \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m will write about \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m topic\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(propose)\n",
+      "\u001b[0;31mTypeError\u001b[0m: can only concatenate list (not \"str\") to list"
      ]
     }
    ],
@@ -163,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 53,
    "id": "01f18225",
    "metadata": {},
    "outputs": [
@@ -172,13 +183,55 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Alex David will write about Post-Translational Modification - Protein Phosphorylation: Addition of phosphate groups to proteins.\n",
+      "Zoe Fiedler will write about Feedback Regulation and Feedback Inhibition - miRNA-mediated Regulation: Post-transcriptional regulation by microRNAs.\n",
+      "\n",
+      "Shelby Ardehali will write about Transcription Elongation and Termination - Elongation Factors: Proteins facilitating RNA polymerase movement along the DNA.\n",
+      "\n",
+      "Nicole Blais will write about Signal Transduction and Cellular Signaling - G-protein Coupled Receptors (GPCRs): Activation of gene expression through GPCR-mediated pathways.\n",
+      "\n",
+      "Cassandra Buffington will write about RNA Processing and Modification - Polyadenylation: Addition of poly-A tail to mRNA.\n",
+      "\n",
+      "Keigan Garrity will write about Post-Translational Modification - Ubiquitination: Addition of ubiquitin molecules for protein degradation.\n",
+      "\n",
+      "Sydney Graul will write about Cellular Localization - Endoplasmic Reticulum (ER) Targeting Signals: Sequences directing proteins to the ER.\n",
+      "\n",
+      "Carley Boulger will write about Epistasis and Genetic Interaction - Genetic Interactions: Interaction between genes influencing expression patterns.\n",
+      "\n",
+      "Becca Balliew will write about Cellular Localization - Nuclear Localization Signals (NLS): Sequences directing proteins to the nucleus.\n",
+      "\n",
+      "Kanita Hrustanovic will write about Post-Translational Modification - Protein Phosphorylation: Addition of phosphate groups to proteins.\n",
+      "\n",
+      "Alex Cerullo will write about Promoter Activation and Transcription Initiation - Promoter Binding Proteins: Transcription factors binding to gene promoters.\n",
+      "\n",
+      "Anika OBrian will write about Cellular Differentiation - Master Regulator Genes: Genes controlling cell fate determination and differentiation.\n",
+      "\n",
+      "Lauren Frueh will write about Feedback Regulation and Feedback Inhibition - Negative Feedback Loops: Mechanisms to dampen excessive gene expression.\n",
+      "\n",
+      "Abbie Tangen will write about Hormonal Regulation - Peptide Hormones: Signaling cascades activated by peptide hormones.\n",
+      "\n",
+      "Sarah Bermingham will write about Signal Transduction and Cellular Signaling - Intracellular Signaling Cascades: Series of molecular events triggered by signaling molecules.\n",
+      "\n",
+      "Mikayla Cox will write about Hormonal Regulation - Steroid Hormones: Influence gene expression through nuclear receptors.\n",
+      "\n",
+      "Shelby Bauer will write about RNA Processing and Modification - RNA Editing: Alteration of nucleotide sequence in RNA transcripts.\n",
+      "\n",
+      "Vivia Van De Mark will write about Translation Initiation and Elongation - eIFs (Eukaryotic Initiation Factors): Proteins facilitating translation initiation.\n",
+      "\n",
+      "Isabelle Lemma will write about Promoter Activation and Transcription Initiation - Mediator Complex: Bridges between transcription factors and RNA polymerase.\n",
+      "\n",
+      "Mitchell Knutsen will write about Stress Response - Heat Shock Proteins (HSPs): Induction in response to cellular stress.\n",
+      "\n",
+      "Alex David will write about Environmental and Metabolic Regulation - Nutrient Availability: Availability of nutrients influencing gene expression.\n",
+      "\n",
+      "Mark Metheny will write about Cellular Localization - Mitochondrial Targeting Signals: Sequences directing proteins to mitochondria.\n",
+      "\n",
+      "Zabiba Husen will write about Environmental and Metabolic Regulation - Oxygen and Redox Regulation: Cellular oxygen levels affecting gene expression.\n",
       "\n",
-      "Nicole Blais will write about Epistasis and Genetic Interaction - Genetic Interactions: Interaction between genes influencing expression patterns.\n",
+      "Ethan Harrell will write about RNA Processing and Modification - RNA Splicing: Removal of introns and joining of exons.\n",
       "\n",
-      "Nicole Blais will write about Epistasis and Genetic Interaction - Genetic Interactions: Interaction between genes influencing expression patterns.\n",
+      "Sherly Manoharan will write about Promoter Activation and Transcription Initiation - Enhancers and Silencers: DNA elements influencing transcription positively (enhancers) or negatively (silencers).\n",
       "\n",
-      "Shelby Ardehali will write about Cellular Localization - Mitochondrial Targeting Signals: Sequences directing proteins to mitochondria.\n"
+      "Austin Jones will write about Chromatin Remodeling and Epigenetic Regulation - Histone Modification: Acetylation, methylation, phosphorylation of histone proteins.\n"
      ]
     }
    ],

Module1-PreliminaryPython/M1D_InClassExample_PCA

@@ -0,0 +1,29 @@
+# Import necessary libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.decomposition import PCA
+
+# Load the Iris dataset
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+
+# Standardize the data (optional, but often recommended for PCA)
+X_standardized = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
+
+# Apply PCA to reduce the dimensionality to 2 components
+pca = PCA(n_components=2)
+X_pca = pca.fit_transform(X_standardized)
+
+# Plot the results
+plt.figure(figsize=(8, 6))
+colors = ['red', 'green', 'blue']
+for i in range(3):
+    plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1], c=colors[i], label=iris.target_names[i])
+
+plt.title('PCA of Iris Dataset')
+plt.xlabel('Principal Component 1')
+plt.ylabel('Principal Component 2')
+plt.legend()
+plt.show()