diff --git a/p2/quest/090225_biber_pca_plot.png b/p2/quest/090225_biber_pca_plot.png
new file mode 100644
index 0000000..492cd06
Binary files /dev/null and b/p2/quest/090225_biber_pca_plot.png differ
diff --git a/p2/quest/neurobiber-pca.log b/p2/quest/neurobiber-pca.log
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index 0000000..1a857f7
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+++ b/p2/quest/neurobiber-pca.log
@@ -0,0 +1,5 @@
+starting the job at: Tue Sep  2 15:27:43 CDT 2025
+setting up the environment
+running the neurobiber labeling script
+job finished, cleaning up
+job pau at: Tue Sep  2 15:28:28 CDT 2025
diff --git a/p2/quest/python_scripts/neurobiber_PCA.py b/p2/quest/python_scripts/neurobiber_PCA.py
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+++ b/p2/quest/python_scripts/neurobiber_PCA.py
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+from sklearn.decomposition import PCA 
+from sklearn.preprocessing import LabelEncoder
+import pandas as pd 
+#import torch
+import numpy as np
+import pandas as pd 
+import matplotlib.pyplot as plt
+
+
+def format_df_data(df):
+    #this accounts for the somewhat idiosyncratic way that I saved my data 
+    normalized_cols = [col for col in df.columns if col.startswith('normalized_')]
+    x = df[normalized_cols].astype(float).values
+    #x = np.vstack(df['features'].values)
+    return x
+
+if __name__ == "__main__":
+    biber_vec_df = pd.read_csv("/home/nws8519/git/mw-lifecycle-analysis/p2/quest/072525_pp_biberplus_labels.csv", low_memory=False)
+    biber_vecs = format_df_data(biber_vec_df)
+    #handoff to PCA model
+    pca = PCA(2)  
+    biber_vecs_pca = pca.fit_transform(biber_vecs)  
+        
+    #first looking at comment_type
+    le = LabelEncoder()
+    colors = le.fit_transform(biber_vec_df['comment_type'])
+
+    plt.scatter(biber_vecs_pca[:, 0], biber_vecs_pca[:, 1],
+            c=colors, edgecolor='none', alpha=0.5, cmap="viridis")
+    plt.xlabel('component 1')
+    plt.ylabel('component 2')
+    plt.colorbar()
+
+    plt.savefig("090225_biber_pca_plot.png", dpi=300) 
diff --git a/p2/quest/slurm_jobs/pca_run.sh b/p2/quest/slurm_jobs/pca_run.sh
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+++ b/p2/quest/slurm_jobs/pca_run.sh
@@ -0,0 +1,32 @@
+#!/bin/bash
+#SBATCH -A p32852
+#SBATCH -p gengpu
+#SBATCH --gres=gpu:a100:1
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=1
+#SBATCH --time=24:00:00
+#SBATCH --mem=64G
+#SBATCH --cpus-per-task=4
+#SBATCH --job-name=neurobiber-pca 
+#SBATCH --output=neurobiber-pca.log
+#SBATCH --mail-type=BEGIN,END,FAIL
+#SBATCH --mail-user=gaughan@u.northwestern.edu
+
+echo "starting the job at: $(date)"
+
+echo "setting up the environment"
+
+module purge
+eval "$(conda shell.bash hook)"
+conda activate neurobiber
+
+echo "running the neurobiber labeling script"
+
+python /home/nws8519/git/mw-lifecycle-analysis/p2/quest/python_scripts/neurobiber_PCA.py
+
+echo "job finished, cleaning up"
+
+conda deactivate
+
+echo "job pau at: $(date)"
+