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("eonurk/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: CD8+ T cells

## >> Response: Neutrophil

## >> Response: CD8+ T cells

## >> Response: Plasma B cells (CD19+ CD27+)

## >> Response: CD8+ T cells

## >> Response: NK cells

## >> Response: Macrophage/Monocyte

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

## >> Response: Macrophage/Monocyte

## >> Response: Myeloid cells (e.g., neutrophils or 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()

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.