ceLLama

ceLLama is a streamlined automation pipeline for cell type annotations using large-language models (LLMs).

Advantages:

• Privacy: Operates locally, ensuring no data leaks.
• Comprehensive Analysis: Considers negative genes.
• Speed: Efficient processing.
• Extensive Reporting: Generates customized reports.

ceLLama is ideal for quick and preliminary cell type checks!

Note

Check the tutorial for Scanpy example.

Installation

To install ceLLama, use the following command:

devtools::install_github("CelVoxes/ceLLama")

Usage

Step 1: Install Ollama

Download Ollama.

Step 2: Choose Your Model

Select your preferred model. For instance, to run the Llama3 model, use the following terminal command:

ollama run llama3.1

This initiates a local server, which can be verified by visiting http://localhost:11434/. The page should display “Ollama is running”.

Step 3: Annotate Cell Types

Load the required libraries and data:

library(Seurat)
library(tidyverse)
library(httr)

pbmc.data <- Read10X("../../../Downloads/filtered_gene_bc_matrices/hg19/")

pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)

# note that you can chain multiple commands together with %>%
pbmc <- pbmc %>%
    SCTransform(verbose = F) %>%
    RunPCA(verbose = F) %>%
    FindNeighbors(dims = 1:10, verbose = F) %>%
    FindClusters(resolution = 0.5, verbose = F) %>%
    RunUMAP(dims = 1:10, verbose = F)

DimPlot(pbmc, label = T, label.size = 3) + theme_void() + theme(aspect.ratio = 1)

Identify cluster markers:

DefaultAssay(pbmc) <- "RNA"

# Find cluster markers
pbmc.markers <- FindAllMarkers(pbmc, verbose = F, min.pct = 0.5)

# split into a lists per cluster
pbmc.markers.list <- split(pbmc.markers, pbmc.markers$cluster)

Run ceLLama:

# set seed, make temperature 0 for reproducible results
library(ceLLama)

res <- ceLLama(pbmc.markers.list, temperature = 0, seed = 101, n_genes = 30)

## >> Response: Activated T cells (Teff) or possibly a subset of activated CD4+ T cells.

## >> Response: Monocyte/Macrophage

## >> Response: Activated T cells (CD8+ T cells)

## >> Response: B cells (CD79A, CD74, CD79B, HLA-DRA, MS4A1, TCL1A) with a subset of activated B cells or plasma cells (elevated expression of HLA-DR and other MHC class II genes).

## >> Response: Activated T cells (CD4+ or CD8+)

## >> Response: CD8+ T cells (cytotoxic T cells)

## >> Response: Macrophage/Monocyte

## >> Response: CD8+ T cells (cytotoxic T cells)

## >> Response: Activated Macrophage/Immune Cell

## >> Response: Dendritic cells (DCs) or possibly monocytes/macrophages.

Tip

Increase temperature to diversify outputs. Set different base_prompt to customize annotations.

Transfer the labels:

# transfer the labels
annotations <- map_chr(res, 1)

names(annotations) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, annotations)

DimPlot(pbmc, label = T, repel = T, label.size = 3) + theme_void() + theme(aspect.ratio = 1) & NoLegend()

Using OpenAI API

You can also use OpenAI for annotating your cell types.

First, you can to create a .Renviron file where you keep your API key. OPENAI_API_KEY="Best_key_ever"

# Default is gpt-4o-mini
res.openai <-
    ceLLama(pbmc.markers.list, temperature = 0, seed = 101, n_genes = 30,
        use_openai = T, # money brr.
        model = "gpt-4o-mini", # set the model
        openai_api_key = Sys.getenv("OPENAI_API_KEY") # or just copy/paste
    )

## gpt-4o-mini is being used!

## >> Response: T cell

## gpt-4o-mini is being used!

## >> Response: Monocyte/macrophage lineage.

## gpt-4o-mini is being used!

## >> Response: CD4+ T cells

## gpt-4o-mini is being used!

## >> Response: B cell.

## gpt-4o-mini is being used!

## >> Response: Cytotoxic T lymphocytes (CTLs)

## gpt-4o-mini is being used!

## >> Response: Cytotoxic T lymphocytes (CTLs) or Natural Killer (NK) cells.

## gpt-4o-mini is being used!

## >> Response: Monocyte/macrophage lineage

## gpt-4o-mini is being used!

## >> Response: Cytotoxic CD8+ T cells

## gpt-4o-mini is being used!

## >> Response: Dendritic cells

## gpt-4o-mini is being used!

## >> Response: Megakaryocyte

# transfer the labels
annotations <- map_chr(res.openai, 1)

names(annotations) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, annotations)

## Warning: Cannot find identity NA

DimPlot(pbmc, label = T, repel = T, label.size = 3) + theme_void() + theme(aspect.ratio = 1) & NoLegend()

Creating Reports

Generate detailed reports explaining the annotations:

# Get the reason for the annotation! (a bit slower)
res <- ceLLama(pbmc.markers.list, temperature = 0, seed = 101, get_reason = T)

# These creates html report in the current directory
generate_report_md(res)
create_html_report()

View the full report here.

Disclaimer

Important

LLMs make mistakes, please check important info.

License

This project is licensed under the CC BY-NC 4.0 License. For more details, visit here.