Q： 得到的差异基因和保守基因，用哪个作为 marker 基因好？ / FindallMarkers 和findconservedmarkers 结果上有什么区别吗？
A： 如果只有一个样本，就用 cluster 中的差异表达基因做 marker 基因。如果做了数据整合，比如处理组和对照组，就要先用 FindConservedMarkers 函数找到保守的差异表达基因，用它们来作为 marker 基因对 cluster 做注释，注释完以后，再用 FindMarkers 函数比较这个 cluster 中的处理组细胞和对照组细胞之间的差异表达基因。
需要解答：那要是不止两组，有好几组，用哪个函数呢？解决方案___
转自：如何使用 Seurat 分析单细胞测序数据（ Q&A - 简书 (jianshu.com)
FindConservedMarkers实例
Seurat之整合分析（1） - 简书 (jianshu.com)
2020-05-18 seurat 包 Stimulated vs Control PBMCs - 简书 (jianshu.com)
单细胞测序数据整合分析示例 - 简书 (jianshu.com)
10x Genomics PBMC（七）：整合数据后的聚类分析 - 简书 (jianshu.com)
2023.05.11
昨天找到了一篇帖子：【细胞通讯】PlantPhoneDB（2），是植物的样本，实验组和对照组。用SCT标准化，FindIntegrationAnchors做了去批次。作者在做完FindClusters之后，关键的两步是DefaultAssay(objs) <- 'SCT' 和 objs<- PrepSCTFindMarkers(objs,assay = "SCT", verbose = TRUE) 贴一下作者的代码： 先按照这个跑一下看看
library(Seurat)
library(tidyverse)
library(ggplot2)
library(ggsci)
library(ggpubr)
library(pheatmap)
library(RColorBrewer)
library(patchwork)
library(lsa)
library(viridis)
library(hrbrthemes)
library(circlize)
library(chorddiag)
library(ggplotify)
library(data.table)
library(parmigene)
library(readxl)
library(infotheo)
library(igraph)
library(muxViz)
library(rgl)
library(tidyverse)
library(dplyr)
//参数选择和作者paper中用的参数一样
pbmc <- readRDS("pbmc3k_final.rds")
pbmc@meta.data$labels <- Idents(pbmc)
control.data <- Read10X(data.dir = "control/filtered_feature_bc_matrix/")
control<- CreateSeuratObject(counts = control.data, project = "control", min.cells = 3, min.features = 200)
control <- subset(control, subset = nFeature_RNA > 200 & nCount_RNA > 1000)
control <- SCTransform(control, verbose = FALSE)
heat.data <- Read10X(data.dir = "heat/filtered_feature_bc_matrix/")
heat<- CreateSeuratObject(counts = heat.data, project = "heat", min.cells = 3, min.features = 200)
heat <- subset(heat, subset = nFeature_RNA > 200 & nCount_RNA > 1000)
heat <- SCTransform(heat, verbose = FALSE)
datasets <- c(control,heat)
features <- SelectIntegrationFeatures(object.list = datasets, nfeatures = 8000)
datasets <- PrepSCTIntegration(object.list = datasets, anchor.features = features, verbose = TRUE)
datasets <- lapply(X = datasets, FUN = RunPCA, verbose = FALSE, features = features)
anchors <- FindIntegrationAnchors(object.list = datasets, normalization.method = "SCT",anchor.features = features, verbose = TRUE, reference=1,reduction = "cca")
objs <- IntegrateData(anchorset = anchors, normalization.method = "SCT", verbose = TRUE)
objs <- RunPCA(objs, verbose = FALSE, approx = FALSE, npcs = 50)
objs <- RunUMAP(objs, reduction = "pca", dims = 1:50, umap.method = "umap-learn", metric = "correlation")
objs <- RunTSNE(objs, reduction = "pca",dims = 1:50,tsne.method = "Rtsne")
objs <- FindNeighbors(objs, reduction = "pca",dims = 1:50)
objs <- FindClusters(objs, resolution = 0.5, algorithm = 2)
DefaultAssay(objs) <- 'SCT'
objs<- PrepSCTFindMarkers(objs,assay = "SCT", verbose = TRUE)
DEG <- FindAllMarkers(objs,
logfc.threshold=0.25,
min.diff.pct = 0.25,
max.cells.per.ident = 10000,
only.pos=T)
mark_gene <- DEG %>% mutate(avg_logFC=avg_log2FC) %>% filter(p_val_adj<0.05)
signature <- readxl::read_excel('../ath_doi_202104.xlsx')
sig_gene <- signature %>% as.data.frame() %>% filter(Tissue=="Root") %>% mutate(V1=`Cell Type`,V2=Cell_Marker) %>% unique(.) %>% select(V1,V2)
贴一下报错，搜了一下ggrepel: 7 unlabeled data points (too many overlaps). Consider increasing max.overlaps ，说不要紧
DefaultAssay(integrated) <- 'SCT'
>
> integrated<- PrepSCTFindMarkers(integrated,assay = "SCT", verbose = TRUE)
Found 6 SCT models. Recorrecting SCT counts using minimum median counts: 2575
>
> DEG <- FindAllMarkers(integrated,
+
+ logfc.threshold=0.25,
+
+ min.diff.pct = 0.25,
+
+ only.pos=T)
Calculating cluster 0
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 41s
Calculating cluster 1
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=33s
Calculating cluster 2
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s
Calculating cluster 3
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=39s
Calculating cluster 4
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s
Calculating cluster 5
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Calculating cluster 6
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=50s
Calculating cluster 7
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=21s
Calculating cluster 8
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 24s
Calculating cluster 9
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=52s
Calculating cluster 10
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=29s
Calculating cluster 11
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=30s
Calculating cluster 12
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04m 21s
Calculating cluster 13
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=19s
Calculating cluster 14
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s
Calculating cluster 15
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 02s
Calculating cluster 16
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 47s
Calculating cluster 17
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 22s
Calculating cluster 18
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=48s
Calculating cluster 19
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=28s
Calculating cluster 20
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 53s
Calculating cluster 21
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=54s
Calculating cluster 22
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 11s
Calculating cluster 23
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 05s
Warning messages:
1: ggrepel: 7 unlabeled data points (too many overlaps). Consider increasing max.overlaps
2: In UseMethod("depth") :
no applicable method for 'depth' applied to an object of class "NULL"
3: In UseMethod("depth") :
no applicable method for 'depth' applied to an object of class "NULL"
4: In UseMethod("depth") :
no applicable method for 'depth' applied to an object of class "NULL"
>
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