## id the treatments
unique(iMGL_Seurat_v3_withConditions$orig.ident)
## Loading required package: Seurat
## Loading required package: SeuratObject
## Loading required package: sp
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## Attaching package: 'SeuratObject'
## The following objects are masked from 'package:base':
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## intersect, t
## [1] "Ctrl" "Apop" "Myln" "Syn" "Ab"
## id differentially expressed genes in treatment with apoptotic neurons vs control
de_apop_v_ctrl <- FindMarkers(iMGL_Seurat_v3_withConditions, ident.1="Apop", ident.2="Ctrl", group.by = "orig.ident")
# Note, by setting ident.2="ctrl", we are benchmarking against control. So avg_log2F>0 mean genes identified are upregulated in apop.
# See most differentially expressed genes in apoptotic neuron treatment vs control
head(sort_by(de_apop_v_ctrl, de_apop_v_ctrl$avg_log2FC, decreasing=TRUE))
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## NEFM 0.000000e+00 698.2300 0.124 0.000 0.000000e+00
## SAT1 5.911374e-196 536.1890 1.000 0.999 1.418552e-191
## MAP1B 6.414340e-134 362.3457 0.052 0.000 1.539249e-129
## SST 4.001545e-151 350.5405 0.057 0.000 9.602507e-147
## CTSB 0.000000e+00 345.8450 0.989 0.966 0.000000e+00
## APOC1 6.122015e-153 344.7697 0.726 0.640 1.469100e-148
## Plot most differentially expressed genes for apop vs ctrl
VlnPlot(iMGL_Seurat_v3_withConditions, features = rownames(head(sort_by(de_apop_v_ctrl, de_apop_v_ctrl$avg_log2FC, decreasing=TRUE))), group.by = "orig.ident", combine = FALSE)
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## id differentially expressed genes in treatment with myln neurons vs control
de_myln_v_ctrl <- FindMarkers(iMGL_Seurat_v3_withConditions, ident.1="Myln", ident.2="Ctrl", group.by = "orig.ident")
# Note, by setting ident.2="ctrl", we are benchmarking against control. So avg_log2F>0 mean genes identified are upregulated in myln.
# See most differentially expressed genes in myln treatment vs control
head(sort_by(de_myln_v_ctrl, de_myln_v_ctrl$avg_log2FC, decreasing=TRUE))
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## S100A9 0.000000e+00 340.5788 0.974 0.979 0.000000e+00
## APOC1 1.098365e-111 252.5744 0.691 0.640 2.635748e-107
## APOE 8.635735e-130 210.6662 0.635 0.527 2.072317e-125
## CTSL 8.387829e-139 190.3542 0.710 0.650 2.012827e-134
## LYZ 8.595729e-75 180.4397 0.826 0.876 2.062717e-70
## S100A8 3.613475e-102 171.7835 0.705 0.817 8.671256e-98
## Plot most differentially expressed genes for myln vs ctrl
VlnPlot(iMGL_Seurat_v3_withConditions, features = rownames(head(sort_by(de_myln_v_ctrl, de_myln_v_ctrl$avg_log2FC, decreasing=TRUE))), group.by = "orig.ident", combine = FALSE)
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## id differentially expressed genes in treatment with syn vs control
de_syn_v_ctrl <- FindMarkers(iMGL_Seurat_v3_withConditions, ident.1="Syn", ident.2="Ctrl", group.by = "orig.ident")
# Note, by setting ident.2="ctrl", we are benchmarking against control. So avg_log2F>0 mean genes identified are upregulated in syn.
# See most differentially expressed genes in syn treatment vs control
head(sort_by(de_syn_v_ctrl, de_syn_v_ctrl$avg_log2FC, decreasing=TRUE))
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## CCL4L2 1.864481e-46 538.1584 0.039 0.010 4.474196e-42
## CCL4 1.232323e-65 421.1170 0.102 0.044 2.957205e-61
## APOE 8.343527e-103 380.8345 0.634 0.527 2.002196e-98
## LYZ 4.031031e-60 292.9002 0.839 0.876 9.673264e-56
## CCL3L1 4.922972e-45 265.4888 0.052 0.018 1.181366e-40
## CCL3 5.245354e-90 233.7497 0.221 0.124 1.258728e-85
## Plot most differentially expressed genes for syn vs ctrl
VlnPlot(iMGL_Seurat_v3_withConditions, features = rownames(head(sort_by(de_syn_v_ctrl, de_syn_v_ctrl$avg_log2FC, decreasing=TRUE))), group.by = "orig.ident", combine = FALSE)
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## id differentially expressed genes in treatment with ab vs control
de_ab_v_ctrl <- FindMarkers(iMGL_Seurat_v3_withConditions, ident.1="Ab", ident.2="Ctrl", group.by = "orig.ident")
# Note, by setting ident.2="ctrl", we are benchmarking against control. So avg_log2F>0 mean genes identified are upregulated in ab.
# See most differentially expressed genes in Ab treatment vs control
head(sort_by(de_ab_v_ctrl, de_ab_v_ctrl$avg_log2FC, decreasing=TRUE))
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## CCL4L2 0.000000e+00 630.5763 0.213 0.010 0.000000e+00
## CCL4 0.000000e+00 510.6495 0.338 0.044 0.000000e+00
## FABP5 1.039995e-144 477.6471 0.964 0.935 2.495677e-140
## TIMP3 8.080429e-126 445.1144 0.066 0.007 1.939061e-121
## CCL20 9.607734e-33 427.0613 0.206 0.149 2.305568e-28
## CCL3 0.000000e+00 421.3855 0.375 0.124 0.000000e+00
## Plot most differentially expressed genes for Ab vs ctrl
VlnPlot(iMGL_Seurat_v3_withConditions, features = rownames(head(sort_by(de_ab_v_ctrl, de_ab_v_ctrl$avg_log2FC, decreasing=TRUE))), group.by = "orig.ident", combine = FALSE)
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