Skip to main content

Integrating AVITI24 Data with Seurat

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

Before You Begin​

Make sure that you have the following prerequisites:

To install the Seurat packages in R, run the following command:

install.packages("Seurat")

To install the cytoprofiling package, complete the following steps:

  1. From a browser, access the Element cytoprofiling repository at GitHub.

  2. Select the green Code button, and then select Download Zip from the dropdown menu.

  3. Locate the downloaded zip file in your system.

  4. Extract the zip file to your desired folder destination.

  5. From the R terminal, run the following commands:

    setwd(path/to/directory/that/src/directory/is/located/within)
    install.packages("devtools")
    library("devtools")
    devtools::install("src/R/cytoprofiling")

Convert Data for Seurat​

  1. To load the RawCellStats.parquet data into R, run the following commands. For input_filename, copy and use the RawCellStats.parquet file path from your run.

    If you manually enter the file path, verify your file path slashes. For Windows OS, you can use a backslash \ or forward slash / for powershell, and you can only use a backslash \ for command prompt.

    library("Seurat")
    library("cytoprofiling")
    input_filename = "C:/location/of/RawCellStats.parquet"
    aviti24_data <- load_cytoprofiling(input_filename)

  2. Filter and normalize the imported data:

    aviti24_data <- normalize_cytoprofiling(filter_cells(aviti24_data))

  3. Convert the AVITI24 data into the data format that is compatible with Seurat:

    seurat_data <- cytoprofiling_to_seurat(aviti24_data)

  4. 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")
Last updated on