LaTex document codes for single cell sequencing analysis \documentclass{article} \usepackage{lipsum} \usepackage{graphicx}
\title{Single Cell Sequencing Analysis} \author{Divya Agrawal} \date{November 2022}
\begin{document}
\maketitle
\begin{Introduction}
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Dataset is downloaded from Broad Insitute Single cell website- https://singlecell.broadinstitute.org/single_cell /study/SCP1256/visium-demo-study#study-summary Named as - Study: Visium demo study
Codes for single cell seq analysis install.packages("Seurat") install.packages("tidyverse") install.packages("dplyr") install.packages("patchwork") install.packages("ggplot2")
library(Seurat) library(tidyverse) library(dplyr) library(patchwork) library(ggplot2)
##load the datasets##
da.data <- Read10X(data.dir ="C:/Users/Divya Agrawal/Downloads/") da <- CreateSeuratObject(counts = da.data, min.cells = 4, min.features = 210)
##QC and filtering##
da[["percent.mt"]] <- PercentageFeatureSet(da, pattern = "^MT-") plot1<-FeatureScatter(da, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") plot1 plot2 <-FeatureScatter(da, feature1 = "nCount_RNA", feature2 = "percent.mt") plot2 plot1 +plot2 da <- subset(da, subset = nFeature_RNA >215 & nFeature_RNA > 2500 & percent.mt <5)
##normalise the data##
da <- NormalizeData(da, normalization.method = "LogNormalize", scale.factor = 10000)
##Find variable Features##
da <-FindVariableFeatures(da, selection.method = "vst", mfeatures=2000) tp10<- head(VariableFeatures(da), 10) tp10 plot1<- VariableFeaturePlot(da)
##scale the data##
all.genes <-rownames(da) pre_scaling <-da da <- ScaleData(da, features = all.genes)
##run linear dimensionality reduction ##
da<-RunPCA(da, features = VariableFeatures(object = da)) print(da[["pca"]], dims = 1:5, nfeatures = 5) VizDimLoadings(da, dims = 1:2, reduction= "pca") DimHeatmap(da, dims = 1, cells = 500) DimHeatmap(da, dims = 1:15, cells = 500) da <- JackStraw(da, num.replicate = 100) JackStrawPlot(da, dims = 1:20) da <-ScoreJackStraw(da, dims = 1:20) da <-ScoreJackStraw(da, dims = 1:20) JackStrawPlot(da, dims = 1:20)
##cluster##
da <- FindNeighbors(da, dims = 1:10) da <-FindClusters(da, resolution = 0.5) head(Idents(da),5) ##run non linear dimensionality reduction on top of dimensionality reduction da <- RunUMAP(da, dims = 1:10) DimPlot(da, reduction = "umap")
##assign the biological meaning to these clusters
da.markers <-FindAllMarkers(da, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) da.markers %>% group_by(cluster) %>% slice_max(n=2, order_by =avg_log2FC) da.markers
FeaturePlot(da, features = c("CTSH","CCL5","ENG","CD79A"))
##talk to a biologist
new.cluster.ids <- c("Naive CD T", "CD14+ Mono", "Memory CD4 T", "B","CD 8 T", "FCG3A+ Mono","NK cells", "DC" ,"platelet", "MAC complex") names(new.cluster.ids) <- levels(da) da <- RenameIdents(da, new.cluster.ids) DimPlot(da, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
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\begin{figure}[h] \centering \includegraphics[width = 1\textwidth]{figs/Rplot15.png} \caption{UMAP Plot} \label{fig:my_label} \end{figure}
\end{Introduction}
\end{document}