Update README.md

README.md

@@ -17,10 +17,9 @@ tags:
 <h2 align="center"> ChatCell: Facilitating Single-Cell Analysis with Natural Language </h2>
 
 <p align="center">
-  <a href="https://www.zjukg.org/project/ChatCell">💻 Project Page</a> •
+  <a href="https://chat.openai.com/g/g-vUwj222gQ-chatcell">💻GPTStore App</a> •
   <a href="https://huggingface.co/datasets/zjunlp/ChatCell-Instructions">🤗 Dataset</a> •
   <a href="https://huggingface.co/spaces/zjunlp/Chatcell">🍎 Demo</a> •
-  <a href="https://arxiv.org/abs/2402.08303">📑 Paper</a> •
   <a href="#1">🏖️ Overview</a> •
   <a href="#2">🧬 Single-cell Analysis Tasks</a> •
   <a href="#3">🛠️ Quickstart</a> •
@@ -36,37 +35,35 @@ tags:
 
 ## 📌 Table of Contents
 
-- [🏖️ Overview](#1)
-- [🧬 Single-cell Analysis Tasks](#2)
-- [🛠️ Quickstart](#3)
+- [🛠️ Quickstart](#2)
+- [🧬 Single-cell Analysis Tasks](#3)
 - [📝 Cite](#4)
 
 
 ---
 
-<h2 id="1">🏖️ Overview</h2>
 
-**Background**
-- Single-cell biology examines the intricate functions of the cells, ranging from energy production to genetic information transfer, playing a critical role in unraveling the fundamental principles of life and mechanisms influencing health and disease. 
-- The field has witnessed a surge in single-cell RNA sequencing (scRNA-seq) data, driven by advancements in high-throughput sequencing and reduced costs.
-- Traditional single-cell foundation models leverage extensive scRNA-seq datasets, applying NLP techniques to analyze gene expression matrices—structured formats that simplify scRNA-seq data into computationally tractable representations—during pre-training. They are subsequently fine-tuned for distinct single-cell analysis tasks, as shown in Figure (a).
+<h2 id="2">🛠️ Quickstart</h2>
 
-<p align="center">
-<img src="./figures/overview.jpg" alt="image" width=100%>
-</p>
-<div align="center">
-Figure 1:  (a) Comparison of traditional single-cell engineering and <b>ChatCell</b>. (b) Overview of <b>ChatCell</b>.
-</div>
-<br>
-We present <b>ChatCell</b>, a new paradigm that leverages natural language to make single-cell analysis more accessible and intuitive.
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-small")
+model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-small")
+input_text="Detail the 100 starting genes for a Mix, ranked by expression level: "
+
+# Encode the input text and generate a response with specified generation parameters
+input_ids = tokenizer(input_text,return_tensors="pt").input_ids
+output_ids = model.generate(input_ids, max_length=512, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, do_sample=True)
+
+# Decode and print the generated output text
+output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True)
+print(output_text)
+```
 
-- Initially, we convert scRNA-seq data into a single-cell language that LLMs can readily interpret.
-- Subsequently, we employ templates to integrate this single-cell language with task descriptions and target outcomes, creating comprehensive single-cell instructions.
-- To improve the LLM's expertise in the single-cell domain,  we conduct vocabulary adaptation, enriching the model with a specialized single-cell lexicon.
-- Following this, we utilize unified sequence generation to empower the model to adeptly execute a range of single-cell tasks.
 
 
-<h2 id="2">🧬 Single-cell Analysis Tasks</h2>
+<h2 id="3">🧬 Single-cell Analysis Tasks</h2>
 
 We concentrate on the following single-cell tasks:
 
@@ -101,34 +98,13 @@ The drug sensitivity prediction task aims to predict the response of different c
 <img src="./figures/example4.jpg" alt="image" width=80%>
 </p>
 
-<h2 id="3">🛠️ Quickstart</h2>
-
-```python
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-tokenizer = AutoTokenizer.from_pretrained("zjunlp/chatcell-small")
-model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/chatcell-small")
-input_text="Detail the 100 starting genes for a Mix, ranked by expression level: "
-
-# Encode the input text and generate a response with specified generation parameters
-input_ids = tokenizer(input_text,return_tensors="pt").input_ids
-output_ids = model.generate(input_ids, max_length=512, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, do_sample=True)
-
-# Decode and print the generated output text
-output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True)
-print(output_text)
-```
-
-
 
 <h2 id="4">📝 Cite</h2>
 
-If you use our repository, please cite the following related paper:
 ```
 @article{fang2024chatcell,
   title={ChatCell: Facilitating Single-Cell Analysis with Natural Language},
   author={Fang, Yin and Liu, Kangwei and Zhang, Ningyu and Deng, Xinle and Yang, Penghui and Chen, Zhuo and Tang, Xiangru and Gerstein, Mark and Fan, Xiaohui and Chen, Huajun},
-  journal={arXiv preprint arXiv:2402.08303},
   year={2024},
 }
 ```