single cell sequencing understanding the structure of a data

Question

[Mo]

Hello guys,

I am trying to understand the structure of single cell data

df<- new("Seurat", assays = list(RNA = new("Assay", counts = new("dgCMatrix", 
    i = c(1L, 4L, 1L, 2L, 1L), p = c(0L, 2L, 4L, 5L), Dim = c(5L, 
    3L), Dimnames = list(c("0610005C13Rik", "0610007C21Rik", 
    "0610007L01Rik", "0610007N19Rik", "0610007P08Rik"), c("10X_P4_0_AAACCTGAGATTACCC", 
    "10X_P4_0_AAACCTGAGTGCCAGA", "10X_P4_0_AAACCTGCAAATCCGT")), 
    x = c(3, 2, 1, 1, 3), factors = list()), data = new("dgCMatrix", 
    i = c(1L, 4L, 1L, 2L, 1L), p = c(0L, 2L, 4L, 5L), Dim = c(5L, 
    3L), Dimnames = list(c("0610005C13Rik", "0610007C21Rik", 
    "0610007L01Rik", "0610007N19Rik", "0610007P08Rik"), c("10X_P4_0_AAACCTGAGATTACCC", 
    "10X_P4_0_AAACCTGAGTGCCAGA", "10X_P4_0_AAACCTGCAAATCCGT")), 
    x = c(3, 2, 1, 1, 3), factors = list()), scale.data = structure(numeric(0), .Dim = c(0L, 
0L)), key = "rna_", assay.orig = NULL, var.features = character(0), 
    meta.features = structure(list(), .Names = character(0), row.names = c("0610005C13Rik", 
    "0610007C21Rik", "0610007L01Rik", "0610007N19Rik", "0610007P08Rik"
    ), class = "data.frame"), misc = list())), meta.data = structure(list(
    orig.ident = structure(c(1L, 1L, 1L), .Label = "10X", class = "factor"), 
    nCount_RNA = c(5, 2, 3), nFeature_RNA = c(2L, 2L, 1L), cell = c("10X_P4_0_AAACCTGAGATTACCC", 
    "10X_P4_0_AAACCTGAGTGCCAGA", "10X_P4_0_AAACCTGCAAATCCGT"), 
    channel = c("10X_P4_0", "10X_P4_0", "10X_P4_0"), mouse.id = c("3-M-8", 
    "3-M-8", "3-M-8"), tissue = c("Tongue", "Tongue", "Tongue"
    ), subtissue = c(NA_character_, NA_character_, NA_character_
    ), mouse.sex = c("M", "M", "M"), method = c("droplet", "droplet", 
    "droplet"), cell_ontology_class = c("basal cell of epidermis", 
    "keratinocyte", "keratinocyte"), cell_ontology_id = c("CL:0002187", 
    "CL:0000312", "CL:0000312"), free_annotation = c("proliferating", 
    "differentiated", "suprabasal differentiating")), row.names = c("10X_P4_0_AAACCTGAGATTACCC", 
"10X_P4_0_AAACCTGAGTGCCAGA", "10X_P4_0_AAACCTGCAAATCCGT"), class = "data.frame"), 
    active.assay = "RNA", active.ident = structure(c(`10X_P4_0_AAACCTGAGATTACCC` = 1L, 
    `10X_P4_0_AAACCTGAGTGCCAGA` = 1L, `10X_P4_0_AAACCTGCAAATCCGT` = 1L
    ), .Label = "10X", class = "factor"), graphs = list(), neighbors = list(), 
    reductions = list(), images = list(), project.name = "SeuratProject", 
    misc = list(), version = structure(list(c(4L, 0L, 4L)), class = c("package_version", 
    "numeric_version")), commands = list(), tools = list())

My main goal is to know how the TP53 is expressed across different cells in different organs. Any idea how to annotate this data?

[rpolicastro]

It's hard to give any specific information since you haven't provided any information about the data, but judging from the metadata the cell type might be present in the cell_ontology_class column. Read through the Seurat vignettes for more information on how to process and analyze the data.

[Mo]

rpolicastro I have read all their vignettes, it looks so useless they have so many examples with various data structure. I am using Tabula, are you able to give me a more specific line or any specific place that I could check?

[rpolicastro]

Start off with their basic tutorial. Also consider reading Orchestrating Single Cell Analysis which uses an orthogonal workflow based off of bioconductor and scran (which I prefer). If you want to follow OSCA you can convert the Seurat object to a SingleCellExperiment with the below code.

library("SingleCellExperiment")

sce <- SingleCellExperiment(list(counts=GetAssayData(df, assay="RNA", slot="data")), colData=df[[]])