In Miaoyx323/stCancer: stCancer

options(knitr.table.format = "html") 
options(scipen=10)
knitr::opts_chunk$set(echo = TRUE, fig.path = savePath_basic)
knitr::opts_knit$set(root.dir = savePath_basic)

h.i <- 1
h.ii <- 1

r sampleName

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r h.i Data preprocessing

After the quality control, we perform following preprocessing steps based on some functions of the R package Seurat. Data is normalized using r normalization.method.

• SCTransform. Use this function as an alternative to the NormalizeData, FindVariableFeatures, ScaleData workflow.
• Highly variable genes. Calcuate the average expression and dispersion of each gene across all cells to select highly variable genes(HVGs).

(Hi-res image: view)

• PCA. Perform principal component analysis (PCA) and select PCs to perform clustering and visualization.
• Visualiztion. Using t-SNE or UMAP to persent each single spot in two-dimensional space.

h.i <- h.i + 1
h.ii <- 1

r h.i Spots annotation

r h.i.r h.ii Clustering

In order to identify clusters of all spots, we perform a graph-based clustering by running Seurat functions. The cluster information can be found in the meta.data seurat_clusters of the SeuratObject.

Here is the t-SNE and UMAP plot colored by spot clusters.

(Hi-res image: view)

Here is the result of clusters shown on the tissue image.

(Hi-res image: view)

h.ii <- h.ii + 1

h.i <- h.i + 1
h.ii <- 1

r h.i Output

Running this script generates following files by default:

r h.ii. Html report : report-stAnno.html.

h.ii <- h.ii + 1

r h.ii. Markdown report : report-stAnno.md.

h.ii <- h.ii + 1

r h.ii. Cluster information file : cluster/.

h.ii <- h.ii + 1

cat(h.ii, ". **NMF results file** : ", sep = "")
cat("[NMF/](./NMF/).\n", sep = "")
h.ii <- h.ii + 1

cat(h.ii, ". **Malignancy results file** : ", sep = "")
cat("[malignancy/](./malignancy/).\n", sep = "")
h.ii <- h.ii + 1

cat(h.ii, ". **Cell type informatrion file** : ", sep = "")
cat("[cell_type](./cell_type/).\n", sep = "")
h.ii <- h.ii + 1

cat(h.ii, ". **State information file** : ", sep = "")
cat("[phenotype](./cancer_state/).\n", sep = "")
h.ii <- h.ii + 1

cat(h.ii, ". **Interaction results file** : ", sep = "")
cat("[interact](./interact/).\n", sep = "")
h.ii <- h.ii + 1

r h.ii. SeuratObject after analysis above : SeuratObject.

h.ii <- h.ii + 1

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© G-Lab, Tsinghua University

Miaoyx323/stCancer documentation built on Nov. 14, 2024, 5:31 p.m.