Updated description of the project

README.md

@@ -6,30 +6,89 @@ tags:
 - tahoe-deepdive
 ---
 
-This is Frameshift's team submission for the Tahoe-DeepDive Hackathon 2025.
+# Frameshift Team Submission – Tahoe-DeepDive Hackathon 2025
 
-Team Name
-Frameshift
+## Team Name
+**Frameshift**
 
-Members
-Jesus Gonzalez Ferrer, UCSC, @JesusGF1
-Carlota Pereda, UCSF,@carlotapereda
-Laura Almonte, UCSF, @almonteloya
-Aidan Winters, Arc Institute/UCSF, @aidanwinters
-Michael Kosicki, LBL, @lotard
+## Members
+- Jesus Gonzalez Ferrer, UCSC — [@JesusGF1](https://github.com/JesusGF1)
+- Carlota Pereda, UCSF — [@carlotapereda](https://github.com/carlotapereda)
+- Laura Almonte, UCSF — [@almonteloya](https://github.com/almonteloya)
+- Aidan Winters, Arc Institute/UCSF — [@aidanwinters](https://github.com/aidanwinters)
+- Michael Kosicki, LBL — [@lotard](https://github.com/lotard)
 
-Project
+---
+
+## Project
+
+### Title
+**Defining context-specific responses to drug perturbations in Tahoe 100M dataset**
+
+### Overview
+Personalized (i.e. context-specific) treatments lead to better cancer outcomes.  
+We want to develop a framework that measures how drugs affect cells differently based on their genetic context, and explains the genetic programs that cells use to respond.  
+We define context-specificity as genotype-, cell line-, tissue-of-origin-, and patient-specific effects on gene expression.
+
+### Motivation
+Drugs don't work the same way for everyone. Oncotherapies sometimes lack efficacy and tend to be indiscriminate and toxic.  
+Broad-acting chemotherapies are effective but are limited by patient side effects.  
+We need better ways of stratifying patients, selecting adequate treatments, and simulating adverse effects before they happen.
+
+---
+
+## Methods
+
+### Data Selection
+We applied an array of methods to a subset of the Tahoe-100M dataset.  
+We focused on cell lines with **KRAS gain-of-function mutations**, especially **G12C**.  
+Selected drugs included known KRAS inhibitors, positive controls, and negative controls.
+
+### E-distance
+- Used precomputed `scVi` embeddings from Tahoe-100M.
+- Calculated distances to plate-paired `DMSO_TF` for each drug and cell line.
+- Visualized results.
+
+### MSE
+- Applied similar steps as E-distance.
+- Started from **pseudobulk samples** provided in the dataset.
+
+### Augur
+- A **scRNA classifier** to quantify separability between control and perturbed groups.
+- Score of 1 indicates high separability.
+- Applied across all cell lines and drug perturbations.
+
+### CellCap
+- A **generative model** for perturbation data.
+- Models correspondence between basal state and measured perturbation.
+- Learns interpretable response programs as weighted gene sets.
+
+---
+
+## Results
+
+- **E-distance** and **MSE** failed to detect context-specific drug effects across selected KRAS cell lines.
+- **Augur** and **CellCap**:
+  - Detected strong responses in **KRAS-G12C** lines.
+  - Captured cell-specific gene expression programs linked to KRAS mutations.
+
+---
+
+## Discussion
+
+The discovery of novel cancer therapies is limited by the lack of generalizable experimental and computational workflows. In a proof-of-concept analysis, we tested four computational methods on the Tahoe-100M dataset for identifying context-specific responses to KRAS inhibitors.
+
+- **Augur** and **CellCap** succeeded in detecting KRAS-inhibitor effects in KRAS-G12C cell lines.
+- **E-distance** and **MSE** failed to differentiate responses.
 
-Title
+We hypothesize that the success of Augur and CellCap lies in their ability to utilize **local, pathway-level expression** rather than global transcriptomic changes.
 
-Overview
-Personalized (ie. context-specific) treatments lead to better cancer outcomes. We want to develop a framework that is able to measure how
-drugs affect cells differently based on their genetic context and that is also to explain the genetic programs that cells use to respond.
+Preliminary results highlight genes associated with the **Ras-Raf pathway**, suggesting a targeted effect by the drugs.
 
-Motivation
+### Future Directions
+We aim to:
+- Scale our approach to all cell lines and drugs in Tahoe-100M.
+- Identify potential **cell-type specific drugs**.
+- Propose **candidates for clinical development**.
 
-Methods
-CellCap, Augur, MSE, E-distance.
-Results
 
-Discussion