梗概
根据实验记录3的结果，调整参数来查看是否有更优的结果。
方法一：修改PC的选择
方法二：提高resolution（分辨率）
方法一：修改PC的选择
细胞聚类
resolution还是为0.6，PC选择从前10调整为前5.
spleen1 <- FindClusters(spleen, reduction.type = "pca", dims.use = 1:5, resolution = 0.6, print.output = 0, save.SNN = TRUE)
PrintFindClustersParams(spleen1)
Parameters used in latest FindClusters calculation run on: 2018-11-14 23:09:41
=============================================================================
Resolution: 0.6
-----------------------------------------------------------------------------
Modularity Function Algorithm n.start n.iter
1 1 100 10
-----------------------------------------------------------------------------
Reduction used k.param prune.SNN
pca 30 0.0667
-----------------------------------------------------------------------------
Dims used in calculation
=============================================================================
1 2 3 4 5
tSNE聚类
spleen1 <- RunTSNE(spleen1, dims.use = 1:5, do.fast= TRUE)
TSNEPlot(spleen1)
tsne2.jpeg
(保存图片的长度为900，高为500)
跟用1-10个聚类结果相比多了一个cluster
寻找细胞标志物
spleen1.markers <- FindAllMarkers(object = spleen1, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
print(x= head(x=spleen1.markers,n = 10))
取每个cluster的最高两个FC值基因作为marker
spleen1.markers %>% group_by(cluster) %>% top_n(2, avg_logFC)
# A tibble: 20 x 7
# Groups: cluster [10]
p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
1 5.13e-185 1.59 0.971 0.262 8.03e-181 0 MS4A1
2 4.38e-175 1.49 0.968 0.274 6.86e-171 0 CD79A
3 2.34e- 84 0.882 0.748 0.198 3.66e- 80 1 VPREB3
4 3.58e- 81 1.27 0.715 0.219 5.60e- 77 1 CD83
5 1.72e- 81 1.14 1 0.815 2.69e- 77 2 HSPA1A
6 2.13e- 57 1.05 0.99 0.894 3.34e- 53 2 HSPA8
7 1.47e- 70 0.826 0.931 0.639 2.31e- 66 3 LDHB
8 1.19e- 64 0.878 0.869 0.354 1.87e- 60 3 TRAC
9 2.99e-132 1.84 0.843 0.185 4.67e-128 4 CMC1
10 1.43e- 73 2.17 0.628 0.158 2.24e- 69 4 CCL3
11 4.25e-125 5.16 0.894 0.186 6.66e-121 5 S100A9
12 8.83e-106 5.26 0.845 0.196 1.38e-101 5 S100A8
13 4.05e- 55 1.37 0.721 0.174 6.34e- 51 6 AC092580.4
14 1.21e- 49 1.40 0.877 0.307 1.90e- 45 6 IL7R
15 1.92e- 84 2.56 1 0.156 3.01e- 80 7 GZMB
16 6.14e- 72 2.46 1 0.226 9.61e- 68 7 PRF1
17 2.67e- 57 2.62 1 0.233 4.19e- 53 8 STMN1
18 8.81e- 30 2.74 0.865 0.298 1.38e- 25 8 HIST1H4C
19 1.36e- 15 5.37 0.893 0.287 2.13e- 11 9 IGHG3
20 2.94e- 8 5.14 0.857 0.412 4.61e- 4 9 IGLC3
作图，查看基因在细胞里的表达情况，看是否与cluster匹配
FeaturePlot(spleen1,features.plot = c("MS4A1","CD83","HSPA1A","TRAC","CCL3","S100A8","IL7R","GZMB","HIST1H4C","IGHG3"),cols.use = c("grey","blue"),reduction.use = "tsne")
marker2.jpeg
方法二：提高分辨率（resolution）
spleen3 <- CreateSeuratObject(raw.data = spleen.data, min.cells = 3, min.genes = 200, project = "10X_spleen")
spleen3 <- AddMetaData(object = spleen3, metadata = percent.mito, col.name = "percent.mito")
spleen3 <- FilterCells(spleen3, subset.names = c("nGene", "percent.mito"), low.thresholds = c(300, -Inf), high.thresholds = c(5000,0.10))
spleen3
spleen3 <- NormalizeData(object=spleen3, normalization.method = "LogNormalize", scale.factor = 10000)
spleen3 <- FindVariableGenes(object = spleen3, mean.function = ExpMean, dispersion.function = LogVMR, x.low.cutoff = 0.0125, x.high.cutoff = 3, y.cutoff = 0.5)
spleen3 <-ScaleData(spleen3, vars.to.regress = c("nUMI","percent.mito"))
spleen3 <- RunPCA(spleen3, pc.genes = [email protected], do.print = TRUE, pcs.print = 1:5, genes.print = 5)
spleen3 <- FindClusters(spleen3, reduction.type = "pca", dims.use = 1:10, resolution = 1.0, print.output = 0, save.SNN = TRUE)
PrintFindClustersParams(spleen3)
spleen3 <- RunTSNE(spleen3, dims.use = 1:10, do.fast= TRUE)
TSNEPlot(spleen3)
步骤与实验记录3相同，但是在执行FindClusters命令时，将resolution=0.6提高为1.0
结果：
tSNE3(resolution=1.0).jpeg
与resolution=0.6聚类形状没有差异，但是多了一个cluster。
对比resolution=0.6的结果：
tSNE(resolution=0.6).jpeg
寻找细胞标志物
spleen3.markers <- FindAllMarkers(object = spleen3, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
取每个cluster的最高两个FC值基因作为marker
spleen3.markers %>% group_by(cluster) %>% top_n(2, avg_logFC)
# A tibble: 20 x 7
# Groups: cluster [10]
p_val avg_logFC pct.1 pct.2 p_val_adj cluster gene
1 4.75e-189 1.59 0.974 0.259 7.43e-185 0 MS4A1
2 2.16e-177 1.49 0.971 0.27 3.38e-173 0 CD79A
3 2.14e- 76 1.25 0.709 0.223 3.36e- 72 1 CD83
4 2.25e- 31 0.896 0.546 0.258 3.52e- 27 1 MYC
5 1.69e- 91 1.30 1 0.817 2.65e- 87 2 HSPA1A
6 7.43e- 21 1.39 0.38 0.162 1.16e- 16 2 IFNG
7 5.29e- 55 0.960 0.911 0.379 8.27e- 51 3 TRAC
8 1.57e- 46 0.836 0.853 0.347 2.46e- 42 3 CD3D
9 1.48e- 72 1.56 0.841 0.291 2.32e- 68 4 IL7R
10 1.56e- 28 1.15 0.495 0.179 2.44e- 24 4 AC092580.4
11 1.57e- 89 1.60 0.988 0.328 2.46e- 85 5 CCL5
12 2.93e- 41 1.27 0.553 0.158 4.58e- 37 5 GZMK
13 3.35e-121 5.14 0.878 0.186 5.25e-117 6 S100A9
14 4.17e-104 5.24 0.835 0.195 6.53e-100 6 S100A8
15 2.26e- 61 2.13 0.758 0.179 3.53e- 57 7 CCL3
16 2.87e- 15 1.92 0.570 0.295 4.49e- 11 7 HIST1H4C
17 6.53e-112 2.62 0.926 0.144 1.02e-107 8 GNLY
18 5.25e-104 2.34 0.981 0.21 8.22e-100 8 PRF1
19 4.39e- 25 5.56 0.923 0.283 6.87e- 21 9 IGHG3
20 1.65e- 19 5.22 0.897 0.332 2.59e- 15 9 IGHG1
作图，查看基因在细胞里的表达情况，看是否与cluster匹配
FeaturePlot(spleen3,features.plot = c("MS4A1","CD83","IFNG","TRAC","IL7R","CCL5","S100A8","CCL3","GNLY","IGHG3"),cols.use = c("grey","blue"),reduction.use = "tsne")