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Integrating AVITI24 Data with Seurat

Seurat is a package in the software R that enables the analysis and investigation of single-cell multiomic data. This tutorial describes how to convert AVITI24 output data into a format for use with Seurat.

Before You Begin​

Make sure you have the following prerequisites for this tutorial:

The following command installs the Seurat packages in R:

install.packages("Seurat")

Install the cytoprofiling package by following the instructions at https://github.com/Elembio/cytoprofiling/tree/v1.0.0?tab=readme-ov-file#installation

Convert Data for Seurat​

  1. Run the following commands to load the RawCellStats.parquet data into R. For input_filename, use the file path to the RawCellStats.parquet file from your run.
library("Seurat")
library("cytoprofiling")
input_filename = "C:/location/of/RawCellStats.parquet"
aviti24_data <- load_cytoprofiling(input_filename)
  1. Filter and normalize the imported data.
aviti24_data <- normalize_cytoprofiling(filter_cells(aviti24_data))
  1. Convert the AVITI24 data into the data format compatible with Seurat.
seurat_data <- cytoprofiling_to_seurat(aviti24_data)
  1. Test your data with a workflow from the Seurat website.

    The following commands are adapted from the Seurat Guided Clustering Tutorial and provide an example workflow to test with your data.

seurat_data <- NormalizeData(seurat_data, normalization.method = "LogNormalize", scale.factor = 10000)
seurat_data <- FindVariableFeatures(seurat_data, selection.method = "vst", nfeatures = 50)
all.genes <- rownames(seurat_data)
seurat_data <- ScaleData(seurat_data, features = all.genes)
seurat_data <- RunPCA(seurat_data, features = VariableFeatures(object = seurat_data), npcs=20)
seurat_data <- RunUMAP(seurat_data, dims = 1:10)
DimPlot(seurat_data, reduction = "umap")
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