gptbioinsightor 0.3.0

GPTBioInsightor utilizes the powerful capabilities of large language models to help people quickly gain knowledge and insight, enhancing their work efficiency.

GPTBioInsightor

GPTBioInsightor is a tool designed for single-cell data analysis, particularly beneficial for newcomers to a biological field or those in interdisciplinary areas who may lack sufficient biological background knowledge. GPTBioInsightor utilizes the powerful capabilities of large language models to help people quickly gain knowledge and insight, enhancing their work efficiency.

Documention

Please checkout the documentations and tutorials at GPTBioInsightor docs

Get started

Installation

Install GPTBioInsightor from PyPi:

pip install gptbioinsightor

Usage

import gptbioinsightor as gbi 

### Set API KEY of LLM 
import os
os.environ['API_KEY'] = "sk-***"

# set background of your data
background = "Cells are PBMCs from a Healthy Donor" 

# make sure you have perform DEG analysis for adata,like: sc.tl.rank_genes_groups(adata, "leiden", method="wilcoxon")
# Here, use qwen2-72b-instruct of Aliyun, but you also can use openai gpt-4o
res = gbi.gpt_celltype(adata, background=background, out="celltype.md", topgenes=15,provider="aliyun", model="qwen2-72b-instruct")
res
# {'0': 'CD4+ T Helper Cells',
#  '1': 'B Cells',
#  '2': 'Monocytes/Macrophages',
#  '3': 'Natural Killer (NK) cells',
#  '4': 'Cytotoxic T Cells (CD8+)',
#  '5': 'Monocytes/Macrophages',
#  '6': 'Dendritic Cells',
#  '7': 'Platelets'}

It will create two markdown files:

• *.most.md: it describes the most likely cell type for each gene set, annotated using a LLM, including the supporting gene markers, the reason behind the annotation, and potential cellular states.
• *.like.md: it describes the all likely cell type for each gene set, annotated using a LLM, including the supporting gene markers, the reason behind the annotation, and potential cellular states.

like:

# Most Possible celltypes
### Geneset 0: CD4+ T Helper Cells
**gene marker**: CD3D, CD3E, CCR7, CD27
**reason**: The presence of CD3D and CD3E, which are integral components of the T-cell receptor complex, along with CCR7 and CD27, which are characteristic of naïve and central memory CD4+ T helper cells, strongly supports this cell type.
**cell state/subtype**: Memory or naïve CD4+ T helper cells in a resting or surveillance state, ready to respond to antigenic challenges.

### Geneset 1: B Cells
**gene marker**: CD79A, MS4A1, CD79B, CD74, CD37
**reason**: These markers are highly specific to B lymphocytes, with CD79A and CD79B being components of the B-cell receptor complex, MS4A1 (CD20) being a well-known B-cell marker, and CD74 and CD37 also being commonly expressed in B cells.
**cell state/subtype**: Mature B cells, potentially activated and capable of antigen presentation, indicated by the presence of HLA-DRA.

### Geneset 2: Monocytes/Macrophages
**gene marker**: FCGR3A, FCER1G, AIF1, LILRA3, MT2A
**reason**: The combination of FCGR3A (CD16), FCER1G (part of Fc receptor complex), AIF1 (involved in macrophage activation), LILRA3 (implicated in immune regulation), and MT2A (a metal detoxification protein) strongly indicates monocytes/macrophages.
**cell state/subtype**: Activated monocytes/macrophages, possibly responding to inflammation or infection.

### Geneset 3: Natural Killer (NK) cells
**gene marker**: GNLY, GZMB, NKG7, PRF1, FCGR3A, TYROBP, XCL2, GZMA
**reason**: This set includes key markers of NK cell function, such as cytotoxic granule proteins (granzymes, perforin), signaling molecules (TYROBP), and the activating receptor CD16 (FCGR3A).
**cell state/subtype**: Activated NK cells, capable of cytotoxic activity against infected or transformed cells.

### Geneset 4: Cytotoxic T Cells (CD8+)
**gene marker**: CCL5, GZMK, NKG7, CST7, CD3D, GZMA, CTSW, CD8A, KLRG1, GZMH, NCR3
**reason**: The presence of CD8A, granzymes (GZMA, GZMK, GZMH), NKG7, and KLRG1 indicates cytotoxic T cells, which are known for their direct killing of infected or cancerous cells.
**cell state/subtype**: Activated or effector CD8+ T cells, potentially engaged in immune surveillance or responding to recent antigen exposure in a healthy individual.
### Geneset 5 : Monocytes/Macrophages
**gene marker**: S100A8, LYZ, S100A9, LGALS2, FCN1, CD14, GSTP1, FTL, TYROBP, GRN, APOBEC3A, GPX1
**reason**: The presence of a comprehensive set of markers, including S100A8, S100A9, CD14, and LYZ, strongly suggests monocytes/macrophages. These markers are indicative of both the cell lineage and the inflammatory state typical of these cells in response to stimuli.
**cell state/subtype**: Activated or inflammatory state due to the presence of alarmins and other inflammatory markers, indicating a response to infection or inflammation.

### Geneset 6 : Dendritic Cells
**gene marker**: HLA-DQA1, HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, HLA-DRB1, HLA-DRB5, CD74
**reason**: The high expression of MHC class II genes (HLA-DQA1, HLA-DPB1, etc.) and CD74, which is crucial for MHC class II antigen presentation, is characteristic of dendritic cells. These markers are essential for the function of antigen presentation to T cells.
**cell state/subtype**: Activated or mature dendritic cells, as indicated by the upregulation of MHC class II molecules, which occurs during the maturation process triggered by pathogen recognition.

### Geneset 7 : Platelets
**gene marker**: PPBP, PF4, GP9, GNG11
**reason**: The expression of PPBP, PF4, and GP9 is highly specific to platelets, which are crucial for hemostasis and thrombosis. GNG11, while not exclusive, supports the presence of platelet-related functions.
**cell state/subtype**: Activated or resting platelets. Given the presence of markers associated with platelet function and aggregation, these platelets might be in a state ready to respond to vascular damage or inflammation.

Contact

• Shenghui Huang ([e-mail])