Biostatistical analysis part 2

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Last updated: 2022-02-21

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Knit directory: S:/KJP_Biolabor/Projects/scSeq_Hefendehl/

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#get sample data
samples.integrated@meta.data %>% as.data.frame() -> samplemeta

# convert to correct data type 

# define genotype as is
samplemeta$Genotype_corr = factor(samplemeta$Genotype=="wt", levels=c(F,T), labels = c("APPPS1+", "WT"))
samplemeta$Genotype_corr = relevel(samplemeta$Genotype_corr, ref="WT")

samplemeta$methoxy = factor(samplemeta$Genotype=="MX04+", levels=c(T,F), labels = c("MX04+", "MX04-"))

samplemeta$Treatment = as.factor(samplemeta$Treatment)
samplemeta$Treatment = relevel(samplemeta$Treatment, ref="Ctrl")
samplemeta$Mouse_ID = as.factor(samplemeta$Mouse_ID)
samplemeta$Sex = as.factor(samplemeta$Sex)
samplemeta$Brain_region = as.factor(samplemeta$Brain_region)
samplemeta$Celltype = as.factor(samplemeta$Celltype)

nCells=nrow(samplemeta)
nMice=nlevels(samplemeta$Mouse_ID)
nCelltypes=nlevels(samplemeta$Celltype)

Sample descriptive:

Data contains a total of 649 Cells from 9. Q: Original raw datset containing only frankfurt data included 1149 cells. What were the filter criteria in the primary cell type analysis

Cells per Mouse

Cells per Strain

table(Mouse_ID=samplemeta$Mouse_ID) %>% as.data.frame() %>% display_tab()

Mouse_ID	Freq
23#15773	66
23#15774	49
23#15792	47
386	41
387	44
388	113
409	86
457	115
461	88

Cells per Genotype

table(Genotype=samplemeta$Genotype_corr, Treatment=samplemeta$Treatment) %>% as.data.frame() %>% display_tab()

Genotype	Treatment	Freq
WT	Ctrl	162
APPPS1+	Ctrl	228
WT	Stroke	127
APPPS1+	Stroke	132

table(Genotype=samplemeta$Genotype_corr, Celltype=samplemeta$Celltype) %>% 
  as.data.frame() %>% display_tab()

Genotype	Celltype	Freq
WT	T/NK	3
APPPS1+	T/NK	19
WT	Microglia_0	66
APPPS1+	Microglia_0	120
WT	Microglia_1	93
APPPS1+	Microglia_1	71
WT	Microglia_2	51
APPPS1+	Microglia_2	56
WT	Microglia_3	44
APPPS1+	Microglia_3	55
WT	Microglia_4	15
APPPS1+	Microglia_4	11
WT	Microglia_5	11
APPPS1+	Microglia_5	12
WT	Granulocytes	6
APPPS1+	Granulocytes	16

variables=c("Celltype","Sex", "Genotype_corr", "Treatment","Phase", "Brain_region","nCount_RNA","pseudoaligned_reads", "percent.mito", "percent.ribo", "Mouse_ID")

Descriptive stats across Cell type

res = compareGroups(Celltype~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
print(export_table)

--------Summary descriptives table by 'Celltype'---------

_____________________________________________________________________________________________________________________________________________________ 
                         T/NK       Microglia_0    Microglia_1    Microglia_2    Microglia_3    Microglia_4    Microglia_5    Granulocytes  p.overall 
                         N=22          N=186          N=164          N=107           N=99           N=26           N=23           N=22                
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Sex:                                                                                                                                            .     
    f                 6 (27.3%)      27 (14.5%)     21 (12.8%)     11 (10.3%)     11 (11.1%)     1 (3.85%)      2 (8.70%)      8 (36.4%)              
    m                 16 (72.7%)    159 (85.5%)    143 (87.2%)     96 (89.7%)     88 (88.9%)     25 (96.2%)     21 (91.3%)     14 (63.6%)             
Genotype_corr:                                                                                                                               <0.001   
    WT                3 (13.6%)      66 (35.5%)     93 (56.7%)     51 (47.7%)     44 (44.4%)     15 (57.7%)     11 (47.8%)     6 (27.3%)              
    APPPS1+           19 (86.4%)    120 (64.5%)     71 (43.3%)     56 (52.3%)     55 (55.6%)     11 (42.3%)     12 (52.2%)     16 (72.7%)             
Treatment:                                                                                                                                   <0.001   
    Ctrl              3 (13.6%)     101 (54.3%)    113 (68.9%)     70 (65.4%)     68 (68.7%)     21 (80.8%)     12 (52.2%)     2 (9.09%)              
    Stroke            19 (86.4%)     85 (45.7%)     51 (31.1%)     37 (34.6%)     31 (31.3%)     5 (19.2%)      11 (47.8%)     20 (90.9%)             
Phase:                                                                                                                                        0.002   
    G1                1 (4.55%)      79 (42.5%)     70 (42.7%)     59 (55.1%)     42 (42.4%)     12 (46.2%)     14 (60.9%)     5 (22.7%)              
    G2M               11 (50.0%)     50 (26.9%)     38 (23.2%)     17 (15.9%)     21 (21.2%)     6 (23.1%)      4 (17.4%)      11 (50.0%)             
    S                 10 (45.5%)     57 (30.6%)     56 (34.1%)     31 (29.0%)     36 (36.4%)     8 (30.8%)      5 (21.7%)      6 (27.3%)              
Brain_region:                                                                                                                                <0.001   
    Cortex            3 (13.6%)     101 (54.3%)    113 (68.9%)     70 (65.4%)     68 (68.7%)     21 (80.8%)     12 (52.2%)     2 (9.09%)              
    Lesion            19 (86.4%)     85 (45.7%)     51 (31.1%)     37 (34.6%)     31 (31.3%)     5 (19.2%)      11 (47.8%)     20 (90.9%)             
nCount_RNA          164049 (80916) 150451 (78955) 134224 (54802) 170420 (76702) 152640 (72678) 172200 (70772) 176087 (64299) 144828 (65899)   0.002   
pseudoaligned_reads 165078 (81748) 150942 (78850) 134355 (54810) 170750 (76639) 152790 (72668) 172433 (70717) 176390 (64225) 145316 (65972)   0.002   
percent.mito         2.23 (0.90)    1.75 (1.17)    1.62 (1.01)    2.07 (1.07)    1.72 (1.15)    1.97 (1.02)    2.11 (0.77)    0.92 (1.00)    <0.001   
percent.ribo         6.53 (2.72)    2.68 (1.58)    2.75 (1.81)    2.44 (1.24)    3.28 (1.67)    2.11 (0.85)    2.91 (1.35)    2.01 (1.17)    <0.001   
Mouse_ID:                                                                                                                                       .     
    23#15773          0 (0.00%)      7 (3.76%)      25 (15.2%)     12 (11.2%)     17 (17.2%)     4 (15.4%)      1 (4.35%)      0 (0.00%)              
    23#15774          0 (0.00%)      1 (0.54%)      30 (18.3%)     10 (9.35%)     6 (6.06%)      1 (3.85%)      1 (4.35%)      0 (0.00%)              
    23#15792          1 (4.55%)      11 (5.91%)     13 (7.93%)     9 (8.41%)      6 (6.06%)      6 (23.1%)      1 (4.35%)      0 (0.00%)              
    386               1 (4.55%)      14 (7.53%)     9 (5.49%)      2 (1.87%)      6 (6.06%)      1 (3.85%)      5 (21.7%)      3 (13.6%)              
    387               11 (50.0%)     10 (5.38%)     5 (3.05%)      6 (5.61%)      5 (5.05%)      0 (0.00%)      1 (4.35%)      6 (27.3%)              
    388               1 (4.55%)      41 (22.0%)     21 (12.8%)     18 (16.8%)     21 (21.2%)     5 (19.2%)      6 (26.1%)      0 (0.00%)              
    409               1 (4.55%)      33 (17.7%)     16 (9.76%)     18 (16.8%)     9 (9.09%)      3 (11.5%)      3 (13.0%)      3 (13.6%)              
    457               1 (4.55%)      41 (22.0%)     24 (14.6%)     21 (19.6%)     18 (18.2%)     5 (19.2%)      3 (13.0%)      2 (9.09%)              
    461               6 (27.3%)      28 (15.1%)     21 (12.8%)     11 (10.3%)     11 (11.1%)     1 (3.85%)      2 (8.70%)      8 (36.4%)              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

export2xls(export_table,paste0(home,"/docs/Descriptives.xlsx"))

download data as excel file here

#get normalized counts 
# question to Desiree hat the Seurat object been initialized with normalized data?
counts <- samples.integrated@assays$RNA@counts %>% as.data.frame()

# drop no variance data and sort by samplemeta
counts <- counts[apply(counts,1, sd) > 0, rownames(samplemeta)]

# drop genes with low detection rate (more than 5 counts per cell)
counts_per_celltype=apply(counts, 1, function(x){tapply(x, samplemeta$Celltype, function(z){sum(z>5,na.rm=T)})})

# keep RNAs with at least 10 cells with goood expression
idx=which(colSums(counts_per_celltype)>10)

counts = counts[idx,]

Hierarchical clustering

to check where the variance in the data comes from

log2_cpm = log2(counts+1)

varsset=apply(log2_cpm, 1, var)

cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:2000],])

distance = dist(cpm.sel.trans)

sampleDistMatrix <- as.matrix(distance)

#colors for plotting heatmap
colors <- rev(colorRampPalette(brewer.pal(9, "Spectral"))(255))
colors=jetcolors(255)
colors=viridis(255)

cellcol = Dark8[1:nlevels(samplemeta$Celltype)]
names(cellcol) = levels(samplemeta$Celltype)

genotypecol = brewer.pal(4,"Accent")[c(1:nlevels(samplemeta$Genotype_corr))]
names(genotypecol) = levels(samplemeta$Genotype_corr)

strokecol = brewer.pal(5,"Set2")[1:nlevels(samplemeta$Treatment)+2]
names(strokecol) = levels(samplemeta$Treatment)

mousecol = brewer.pal(9,"Set1")[1:nlevels(samplemeta$Mouse_ID)]
names(mousecol) = levels(samplemeta$Mouse_ID)

braincol = brewer.pal(3,"Set2")[1:nlevels(samplemeta$Brain_region)]
names(braincol) = levels(samplemeta$Brain_region)

ann_colors = list(
  Genotype_corr = genotypecol, 
  Mouse_ID = mousecol,
  Brain_region = braincol,
  Celltype=cellcol,
  Treatment=strokecol
)

labels = samplemeta[,c("Genotype_corr","Mouse_ID", "Brain_region", "Celltype", "Treatment")] %>%  
  mutate_all(as.character) %>% as.data.frame()

rownames(labels)=rownames(samplemeta)

pheatmap(sampleDistMatrix,
         clustering_distance_rows = distance,
         clustering_distance_cols = distance,
         clustering_method = "ward.D2",
         scale ="none",
         show_rownames=F, show_colnames = F,
         legend=T,
         border_color = NA, 
         annotation_row = labels,
         annotation_col = labels,
         annotation_colors = ann_colors,
         col = colors, 
         main = "D62 Distances normalized log2 counts")

Genotype X Treatment Statistical modelling

getres=function(Celltype="Specify", 
                Hypothesis="~1+Sex+Genotype_corr*Treatment", 
                Target="Genotype_corrAPPPS1+:TreatmentStroke",
                Randomeffect="Sex"){
  
  res= comparison_rand(designform =Hypothesis, 
                       randomeffect = Randomeffect, 
                       Samples = samplemeta$Celltype==Celltype, 
                       log_cpm = log2_cpm, 
                       samplesdata = samplemeta,
                       target=Target)
  
  
  
  labels = samplemeta[samplemeta$Celltype==Celltype,c("Treatment", "Genotype_corr","Mouse_ID", "Brain_region", "Celltype")] %>%  
    mutate_all(as.character) %>% as.data.frame()
  
  labels=labels %>% arrange(Treatment,Genotype_corr)
  plotdata=log2_cpm[res$adj.P.Val<0.05,rownames(labels)]
  
  pheatmap(plotdata,
           #clustering_method = "ward.D2",
           cluster_cols = F,
           cluster_rows  = T,
           scale ="column",
           show_rownames=F, show_colnames = F,
           legend=T,
           border_color = NA, 
           #annotation_row = labels,
           annotation_col = labels,
           annotation_colors = ann_colors,
           col = colors, 
           main = "D62 Distances normalized log2 counts")
  return(res)
  
}

generate_output=function(CT="Celltype", ...){
  respath=paste0(home, "/docs/LMER_",CT,".xlsx")
  
  analysis = getres(Celltype = CT)
  analysis_sig = analysis[analysis$adj.P.Val<0.05,]
  analysis_sig %>% display_tab()
  
  write.xlsx2(analysis_sig, file =respath , sheetName = "significant genes")
  
  ResGO = getGOresults(rownames(analysis_sig),
                       rownames(analysis),
                       "mmusculus")
  
  if(length(ResGO)>0){
    p=gostplot(ResGO)
  } else{
    ResGO=data.frame(result="no significant enrichment identified")
    p="no significant enrichment identified"
  }
  
  write.xlsx2(ResGO$result, file = respath, sheetName = "GO_enrichment", append=T)
  return(list(results=analysis,results_sig=analysis_sig, plot=p))
}

Microglia_0

res_output=generate_output("Microglia_0")

res_output[["results_sig"]] %>% display_tab()

	logFC	AveExpr	t	P.Value	adj.P.Val	B
Tbata	3.587	0.454	12.325	0	0.000	45.731
AC099934.1	2.090	0.223	11.078	0	0.000	38.052
Rn7s1	4.776	0.932	10.691	0	0.000	35.689
AY036118	-11.089	6.541	-9.225	0	0.000	26.901
Prrx1	2.430	0.394	8.421	0	0.000	22.252
Tmem170	2.182	0.349	7.415	0	0.000	16.690
Rn7s2	3.690	0.821	7.385	0	0.000	16.527
Olfr907	2.683	0.908	6.916	0	0.000	14.060
Dock10	4.006	5.039	6.156	0	0.000	10.267
Mgmt	2.584	0.444	6.102	0	0.000	10.009
AW822252	2.125	0.732	6.047	0	0.000	9.746
Apoe	-11.365	7.561	-5.799	0	0.000	8.580
Trem2	-5.769	6.733	-5.695	0	0.000	8.100
D10Wsu102e	-2.590	7.009	-5.651	0	0.000	7.900
Lgals3bp	-7.877	3.952	-5.624	0	0.000	7.778
Hexb	-5.732	8.947	-5.570	0	0.000	7.535
Fcer1g	-5.674	6.858	-5.535	0	0.000	7.378
Ctsd	-6.148	9.513	-5.433	0	0.000	6.921
B2m	-5.709	7.942	-5.406	0	0.000	6.801
Gm44215	-4.526	3.605	-5.346	0	0.000	6.538
Gnaz	2.331	0.780	5.337	0	0.000	6.498
C1qb	-6.437	8.397	-5.323	0	0.000	6.438
Lipe	2.876	1.789	5.296	0	0.000	6.323
CT010467.1	-4.636	10.638	-5.271	0	0.000	6.210
C1qa	-6.026	8.093	-5.192	0	0.000	5.872
Lyz2	-8.393	5.945	-5.183	0	0.000	5.833
Itm2b	-5.455	8.165	-5.126	0	0.000	5.591
Cst3	-5.530	10.468	-5.116	0	0.000	5.549
Gm37144	2.617	1.609	5.109	0	0.000	5.518
Gm44645	1.757	0.462	5.070	0	0.000	5.355
Ctsb	-5.783	8.220	-5.011	0	0.001	5.107
C1qc	-5.531	8.304	-4.923	0	0.001	4.741
Myl2	1.632	0.203	4.922	0	0.001	4.738
Cst7	-7.499	3.339	-4.838	0	0.001	4.396
Grn	-5.009	6.427	-4.713	0	0.002	3.891
Plac8	1.716	0.166	4.641	0	0.002	3.605
Ctss	-4.872	9.414	-4.624	0	0.003	3.538
Ctsz	-5.247	7.907	-4.601	0	0.003	3.448
Pola2	-2.569	2.264	-4.585	0	0.003	3.387
Hexa	-5.510	6.097	-4.577	0	0.003	3.354
Cd52	-5.186	3.497	-4.571	0	0.003	3.331
AC127302.3	-2.872	2.889	-4.537	0	0.003	3.201
Gm37716	1.460	0.498	4.521	0	0.003	3.137
Gm26905	-3.427	6.505	-4.507	0	0.004	3.085
Cyba	-4.784	5.245	-4.505	0	0.004	3.076
Gpr171	1.221	0.095	4.387	0	0.006	2.628
Fhl3	1.952	0.607	4.357	0	0.006	2.514
Col6a3	2.168	0.420	4.335	0	0.007	2.432
Axl	-5.291	2.164	-4.318	0	0.007	2.370
Gm15500	-5.152	4.285	-4.317	0	0.007	2.364
Fth1	-4.820	7.555	-4.315	0	0.007	2.359
H2-D1	-5.244	6.578	-4.295	0	0.007	2.284
Gm10076	-3.698	2.856	-4.288	0	0.007	2.258
Cd81	-5.159	7.456	-4.267	0	0.008	2.181
Tyrobp	-4.351	7.358	-4.251	0	0.008	2.121
Upk1b	3.244	1.015	4.223	0	0.009	2.018
Atp6v0c	-5.080	4.998	-4.216	0	0.009	1.994
Clec7a	-5.543	2.762	-4.180	0	0.011	1.864
Gm26870	-3.534	9.134	-4.175	0	0.011	1.843
Selenop	-5.034	6.801	-4.116	0	0.013	1.634
Gm15564	1.506	0.765	4.101	0	0.014	1.578
Serinc3	-3.705	6.433	-4.098	0	0.014	1.568
Eef1a1	-4.593	7.535	-4.088	0	0.014	1.533
Fcgr3	-4.253	7.279	-4.049	0	0.016	1.395
Soat1	-3.694	2.004	-4.032	0	0.017	1.334
Gm14303	-2.844	2.264	-4.016	0	0.018	1.280
Ppp2r5a	-3.152	4.146	-3.984	0	0.020	1.166
Gm10719	-3.157	6.711	-3.953	0	0.022	1.060
Rpl13a	-4.195	4.835	-3.945	0	0.022	1.030
Lamp2	-4.707	4.178	-3.944	0	0.022	1.027
Rnaset2b	-4.092	6.400	-3.936	0	0.023	1.000
Fcgr2b	-4.898	4.773	-3.931	0	0.023	0.985
Mpeg1	-4.321	6.028	-3.879	0	0.027	0.805
Gm43652	1.437	0.726	3.874	0	0.027	0.790
Rab3gap1	-2.810	3.206	-3.862	0	0.028	0.748
Thy1	1.352	0.317	3.852	0	0.029	0.713
Mt1	-3.819	2.286	-3.846	0	0.029	0.693
Acbd5	-2.555	1.954	-3.800	0	0.034	0.540
Cry1	1.185	0.121	3.799	0	0.034	0.537
Bicd1	-2.199	2.661	-3.780	0	0.036	0.474
Lgmn	-4.134	7.604	-3.780	0	0.036	0.473
Cd9	-4.196	6.255	-3.738	0	0.041	0.334
Rps14	-4.030	4.782	-3.697	0	0.047	0.198
Rplp0	-4.638	5.462	-3.691	0	0.048	0.179
Ssr4	-4.074	2.953	-3.682	0	0.048	0.149
Erp29	-4.109	3.530	-3.681	0	0.048	0.147
Itgb5	-3.786	5.576	-3.677	0	0.049	0.132
Arhgef40	2.806	2.680	3.670	0	0.049	0.112
H2-K1	-5.044	5.315	-3.665	0	0.050	0.095

res_output[["plot"]]

get results table here

Microglia_1

res_output=generate_output("Microglia_1")

[1] "no significant GO terms identified"

res_output[["results_sig"]] %>% display_tab()

	logFC	AveExpr	t	P.Value	adj.P.Val	B
Rn7s1	4.839	2.251	9.871	0	0.000	28.811
AB124611	2.674	0.089	7.923	0	0.000	18.420
Hexb	-4.016	10.455	-6.220	0	0.000	10.096
Pola2	-4.177	1.551	-6.203	0	0.000	10.021
Gbp2	2.735	0.131	6.157	0	0.000	9.807
Gbp8	2.377	0.120	5.786	0	0.000	8.151
Ogfod2	2.767	0.186	5.578	0	0.000	7.248
AY036118	-7.305	4.063	-5.318	0	0.001	6.152
Adgre5	1.340	0.041	5.298	0	0.001	6.067
Lzic	2.338	0.160	5.247	0	0.001	5.860
Trdmt1	1.439	0.094	5.243	0	0.001	5.840
Gbp4	1.867	0.187	5.200	0	0.001	5.667
Vav3	0.781	0.024	5.136	0	0.001	5.404
Psmd10	3.468	0.411	4.955	0	0.002	4.679
C3	3.068	0.263	4.834	0	0.003	4.207
Dck	2.403	0.210	4.805	0	0.003	4.095
1700003F12Rik	0.918	0.044	4.771	0	0.003	3.962
Mndal	0.867	0.045	4.665	0	0.005	3.558
Eif1b	4.261	0.868	4.573	0	0.007	3.214
Galt	2.817	0.377	4.560	0	0.007	3.163
Sparc	-3.767	7.598	-4.490	0	0.009	2.907
Bicdl1	1.325	0.080	4.475	0	0.009	2.851
Gm15952	1.267	0.159	4.445	0	0.009	2.741
Cers6	2.071	0.193	4.443	0	0.009	2.735
Mrps5	2.607	0.350	4.404	0	0.011	2.591
AC099934.1	1.435	0.595	4.292	0	0.016	2.189
Lilrb4a	2.908	0.332	4.261	0	0.017	2.081
Zbtb41	1.102	0.076	4.254	0	0.017	2.056
Rn7s2	3.408	1.658	4.176	0	0.023	1.783
2810414N06Rik	1.847	0.206	4.162	0	0.023	1.735
Mapkbp1	2.390	0.252	4.153	0	0.023	1.703
Chchd10	1.846	0.165	4.116	0	0.026	1.576
Aplf	1.964	0.206	4.113	0	0.026	1.565
B4galt6	3.474	0.769	4.064	0	0.030	1.396
Cited4	0.792	0.042	4.051	0	0.030	1.353
Trmt112	4.687	1.995	4.046	0	0.030	1.336
Cd9	-5.297	6.392	-4.045	0	0.030	1.334
Stt3a	5.158	2.125	4.013	0	0.033	1.225
Sh3pxd2b	1.437	0.108	4.009	0	0.033	1.213
Mgmt	2.314	1.070	3.968	0	0.037	1.073
Aars2	1.446	0.127	3.961	0	0.037	1.051
Itgal	1.063	0.069	3.923	0	0.042	0.924
Gm29474	0.931	0.060	3.909	0	0.043	0.878
Homer3	1.961	0.288	3.895	0	0.045	0.831
Ciita	0.423	0.022	3.882	0	0.046	0.791
Dut	1.348	0.076	3.878	0	0.046	0.776

res_output[["plot"]]

[1] "no significant enrichment identified"

get results table here

Microglia_2

res_output=generate_output("Microglia_2")

res_output[["results_sig"]] %>% display_tab()

	logFC	AveExpr	t	P.Value	adj.P.Val	B
AY036118	-12.465	5.877	-14.742	0	0.000	43.970
Rn7s1	6.378	2.015	10.991	0	0.000	29.218
AC099934.1	2.240	0.649	10.369	0	0.000	26.596
Gab3	6.338	1.369	8.383	0	0.000	18.074
Tbata	3.179	1.094	7.291	0	0.000	13.423
Tmem170	2.548	0.931	6.594	0	0.000	10.527
Mgmt	2.924	1.015	5.783	0	0.000	7.291
Katnb1	2.103	0.162	5.168	0	0.002	4.966
Gm10719	-4.767	4.443	-5.043	0	0.003	4.510
Gm15564	2.546	1.222	4.989	0	0.003	4.315
Gm26870	-5.865	6.156	-4.874	0	0.004	3.907
Rn7s2	4.109	1.358	4.874	0	0.004	3.904
Sorbs2	2.072	0.168	4.872	0	0.004	3.899
Actr3b	2.047	0.143	4.855	0	0.004	3.836
Gm10800	-6.124	7.447	-4.830	0	0.004	3.750
Zbp1	1.536	0.157	4.795	0	0.005	3.626
Gm10801	-4.750	5.022	-4.641	0	0.008	3.090
Gm37401	3.758	1.196	4.394	0	0.019	2.251
Chchd5	1.938	0.189	4.392	0	0.019	2.247
Gm37411	1.619	0.409	4.288	0	0.028	1.903
Gm10717	-3.967	4.416	-4.276	0	0.028	1.864
Pola2	-2.882	2.295	-4.173	0	0.039	1.528
Cd209g	1.590	0.134	4.135	0	0.043	1.407
Bola1	2.978	0.571	4.096	0	0.048	1.282

res_output[["plot"]]

get results table here

Microglia_3

res_output=generate_output("Microglia_3")

[1] "no significant GO terms identified"

res_output[["results_sig"]] %>% display_tab()

	logFC	AveExpr	t	P.Value	adj.P.Val	B
Apoe	-10.436	8.936	-8.743	0	0.000	19.567
Rn7s1	4.748	1.890	8.097	0	0.000	16.821
Tmem170	2.313	0.742	7.709	0	0.000	15.178
Pcid2	2.817	0.185	5.869	0	0.000	7.648
Pigq	5.879	0.990	5.843	0	0.000	7.545
Tbata	2.851	0.831	5.706	0	0.000	7.013
Rnf14	3.786	0.386	5.702	0	0.000	7.000
AY036118	-8.708	5.396	-5.241	0	0.002	5.253
BC026585	4.424	0.909	5.231	0	0.002	5.217
C1galt1	1.556	0.079	5.185	0	0.002	5.047
Rbmx2	1.261	0.064	5.167	0	0.002	4.982
Ppil1	2.141	0.194	5.068	0	0.002	4.619
Vmn1r208	0.664	0.034	4.973	0	0.003	4.272
Cnnm4	3.559	0.429	4.794	0	0.006	3.634
Mgmt	2.936	0.931	4.707	0	0.008	3.327
Alad	2.413	0.203	4.663	0	0.009	3.177
Tpst2	6.187	2.268	4.608	0	0.010	2.984
Gt(ROSA)26Sor	3.419	0.561	4.595	0	0.010	2.939
Gm15564	2.307	1.137	4.450	0	0.016	2.445
Fkbp14	2.456	0.221	4.445	0	0.016	2.429
Clec7a	-7.063	3.980	-4.402	0	0.018	2.285
Zfp712	1.486	0.117	4.364	0	0.020	2.159
Gm21092	1.785	0.267	4.301	0	0.024	1.948
Gm10774	1.335	0.288	4.251	0	0.028	1.785
Alg14	3.880	0.756	4.232	0	0.029	1.721
Gm15417	0.945	0.079	4.203	0	0.030	1.628
Cox18	1.838	0.189	4.199	0	0.030	1.613
AC210924.1	1.671	0.222	4.194	0	0.030	1.597
Acot8	2.038	0.175	4.179	0	0.030	1.550
Wsb2	4.864	1.184	4.129	0	0.035	1.387
Smim8	2.813	0.443	4.110	0	0.037	1.327
Gm43328	0.673	0.064	4.053	0	0.044	1.144
Ano4	1.285	0.214	4.039	0	0.045	1.100
Rn7s2	3.378	1.489	4.031	0	0.045	1.073
Zfp513	3.066	0.452	4.011	0	0.046	1.011
Stam	3.000	0.485	4.004	0	0.046	0.990
Bin2	6.320	4.050	3.996	0	0.046	0.963
Dand5	2.065	0.195	3.987	0	0.046	0.937
Eif3d	4.376	0.900	3.985	0	0.046	0.930

res_output[["plot"]]

[1] "no significant enrichment identified"

get results table here

Microglia_4

calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference

idx=samplemeta$Celltype=="Microglia_4"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])

        
         WT APPPS1+
  Ctrl   11      10
  Stroke  4       1

table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()

    Fisher's Exact Test for Count Data

data:  .
p-value = 0.3562
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.005090941 3.588141911
sample estimates:
odds ratio 
 0.2877466 

#res_output=generate_output("Microglia_4")
#res_output[["results_sig"]] %>% display_tab()
#res_output[["plot"]]

get results table here

Microglia_5

res_output=generate_output("Microglia_5")

[1] "no significant GO terms identified"

res_output[["results_sig"]] %>% display_tab()

	logFC	AveExpr	t	P.Value	adj.P.Val	B
Sirpb1c	8.788	0.382	103.262	0.000	0.000	35.333
S100a4	8.061	0.350	94.720	0.000	0.000	35.105
Cenpt	7.209	0.313	84.717	0.000	0.000	34.755
Pilra	7.095	0.308	83.370	0.000	0.000	34.699
Fbxl8	6.966	0.303	81.854	0.000	0.000	34.634
Taf5l	6.826	0.297	80.210	0.000	0.000	34.559
Zgrf1	6.820	0.297	80.143	0.000	0.000	34.556
Sptan1	6.714	0.292	78.898	0.000	0.000	34.496
Cytip	6.569	0.286	77.188	0.000	0.000	34.410
Elac2	6.476	0.282	76.095	0.000	0.000	34.353
Map3k9	6.443	0.280	75.710	0.000	0.000	34.332
Mis18a	6.426	0.279	75.514	0.000	0.000	34.321
Nup35	6.426	0.279	75.514	0.000	0.000	34.321
Pdcd7	5.644	0.245	66.320	0.000	0.000	33.728
Slco3a1	5.248	0.228	61.668	0.000	0.000	33.342
S100a10	5.044	0.219	59.276	0.000	0.000	33.115
Gm43200	5.000	0.217	58.754	0.000	0.000	33.063
Npepl1	4.907	0.213	57.660	0.000	0.000	32.949
Sms-ps	4.654	0.202	54.687	0.000	0.000	32.613
2810001G20Rik	4.322	0.188	50.786	0.000	0.000	32.102
Gm1972	4.087	0.178	48.031	0.000	0.000	31.685
Pde8a	4.000	0.174	47.003	0.000	0.000	31.515
Zfp362	4.000	0.174	47.003	0.000	0.000	31.515
Glmn	3.807	0.166	44.740	0.000	0.000	31.113
Polr2d	3.807	0.166	44.740	0.000	0.000	31.113
Gm34237	3.513	0.153	41.277	0.000	0.000	30.407
Acsl3	3.322	0.144	39.035	0.000	0.000	29.882
Tsen54	3.322	0.144	39.035	0.000	0.000	29.882
Nedd4l	3.170	0.138	37.249	0.000	0.000	29.419
Pilrb1	2.855	0.124	33.552	0.000	0.000	28.312
Skint3	2.807	0.122	32.989	0.000	0.000	28.123
AI662270	6.170	0.312	27.808	0.000	0.000	26.082
Rps6ka4	2.322	0.101	27.285	0.000	0.000	25.841
AC131743.1	2.307	0.200	26.855	0.000	0.000	25.637
Mta3	7.282	0.365	25.446	0.000	0.000	24.930
Sirpb1b	5.404	0.278	24.343	0.000	0.000	24.332
Micall1	2.000	0.087	23.503	0.000	0.000	23.850
Taf1b	2.000	0.087	23.502	0.000	0.000	23.849
Gm44103	2.000	0.087	23.501	0.000	0.000	23.849
Gm20716	7.227	0.368	22.828	0.000	0.000	23.444
Hist1h4d	1.585	0.069	18.625	0.000	0.000	20.475
Ubash3a	1.585	0.069	18.625	0.000	0.000	20.475
Zbtb39	1.585	0.069	18.625	0.000	0.000	20.475
Fam69a	6.546	0.351	17.446	0.000	0.000	19.481
Batf3	4.829	0.259	16.998	0.000	0.000	19.082
Gm12411	2.473	0.131	15.768	0.000	0.000	17.919
Sh3bgrl2	3.459	0.194	15.592	0.000	0.000	17.744
Gm43444	3.310	0.180	15.103	0.000	0.000	17.245
Sde2	6.820	0.397	14.111	0.000	0.000	16.178
Mpnd	6.177	0.346	14.103	0.000	0.000	16.170
Ticam2	7.359	0.475	13.712	0.000	0.000	15.727
Dcaf15	5.846	0.331	13.248	0.000	0.000	15.183
C1qc	-11.264	9.703	-13.027	0.000	0.000	14.918
Gm45867	3.621	0.206	12.654	0.000	0.000	14.460
Svep1	2.807	0.166	12.653	0.000	0.000	14.458
Ppp1r26	1.006	0.044	11.821	0.000	0.000	13.386
Gm37390	1.001	0.044	11.759	0.000	0.000	13.304
Cnn2	1.000	0.043	11.751	0.000	0.000	13.293
Fbxo8	1.000	0.043	11.751	0.000	0.000	13.293
Pex12	1.000	0.043	11.751	0.000	0.000	13.293
Gm3755	1.000	0.043	11.751	0.000	0.000	13.293
Gm33023	3.334	0.194	11.736	0.000	0.000	13.273
Sms	5.081	0.298	11.601	0.000	0.000	13.090
Ms4a4b	4.543	0.295	11.527	0.000	0.000	12.990
Gsr	5.072	0.297	11.493	0.000	0.000	12.943
Strip1	5.337	0.696	11.319	0.000	0.000	12.704
Gm42725	5.149	0.281	10.799	0.000	0.000	11.969
1810014B01Rik	2.974	0.178	10.469	0.000	0.000	11.487
Naalad2	5.496	0.908	10.462	0.000	0.000	11.476
AC154640.4	6.128	0.409	10.304	0.000	0.000	11.241
Erlin1	5.685	0.344	10.280	0.000	0.000	11.204
Gm37521	3.195	0.194	9.828	0.000	0.000	10.512
1190007I07Rik	5.946	0.413	9.655	0.000	0.000	10.239
Gm10146	2.771	0.180	9.451	0.000	0.000	9.915
Lrif1	5.063	0.317	9.156	0.000	0.000	9.434
Tada3	4.833	0.368	9.112	0.000	0.000	9.362
Gm4739	4.492	0.283	8.955	0.000	0.000	9.100
Gpatch11	2.474	0.156	8.645	0.000	0.000	8.575
Slfn1	6.519	0.421	8.514	0.000	0.000	8.349
Proser1	6.169	0.428	8.364	0.000	0.000	8.086
Cog3	4.858	0.333	8.354	0.000	0.000	8.070
Gm17229	3.542	0.232	8.012	0.000	0.000	7.460
CT025600.1	3.532	0.265	7.809	0.000	0.000	7.090
Mtrr	2.211	0.144	7.726	0.000	0.000	6.938
Cep57l1	1.117	0.069	7.664	0.000	0.000	6.823
AC154636.3	6.015	0.447	7.591	0.000	0.000	6.687
Arhgap11a	6.000	0.431	7.453	0.000	0.000	6.429
Ice1	5.050	0.409	7.347	0.000	0.000	6.229
Emb	5.855	0.469	7.331	0.000	0.000	6.199
Tex2	8.813	0.534	7.133	0.000	0.000	5.819
Gle1	5.034	0.345	7.132	0.000	0.000	5.817
Gm38699	3.343	0.227	7.127	0.000	0.000	5.807
Vac14	4.962	0.342	7.030	0.000	0.000	5.619
Ino80	4.998	0.811	7.004	0.000	0.000	5.569
Gm4924	2.479	0.203	6.869	0.000	0.000	5.303
Gm36065	1.889	0.130	6.601	0.000	0.000	4.771
Plac8	6.636	0.502	6.503	0.000	0.000	4.575
AC187103.1	0.945	0.063	6.415	0.000	0.001	4.397
Eme2	8.008	0.506	6.192	0.000	0.001	3.939
N4bp1	8.477	0.540	6.053	0.000	0.001	3.652
Cep70	6.696	0.427	6.003	0.000	0.001	3.548
Gm13369	5.305	0.430	6.000	0.000	0.001	3.541
Ctr9	6.344	0.744	5.949	0.000	0.001	3.435
Tfdp1	5.123	0.427	5.905	0.000	0.002	3.343
Slc2a3	2.101	0.289	5.826	0.000	0.002	3.177
4930404A12Rik	5.736	0.469	5.795	0.000	0.002	3.112
Ddx23	5.421	0.761	5.784	0.000	0.002	3.088
Rad52	5.784	0.491	5.768	0.000	0.002	3.054
Gm2436	4.015	0.358	5.763	0.000	0.002	3.043
Ppp2r3c	7.224	0.773	5.738	0.000	0.002	2.991
A630001O12Rik	4.934	0.394	5.726	0.000	0.002	2.966
9130401M01Rik	6.235	0.579	5.674	0.000	0.002	2.854
Gm10825	2.889	0.307	5.639	0.000	0.003	2.781
Hmga1	5.108	0.497	5.605	0.000	0.003	2.709
Hexa	-10.179	7.250	-5.567	0.000	0.003	2.627
Rasgrp1	2.713	0.243	5.540	0.000	0.003	2.569
Gm45718	3.099	0.355	5.486	0.000	0.003	2.453
Fam98a	7.072	0.543	5.415	0.000	0.004	2.301
Birc3	8.829	0.973	5.392	0.000	0.004	2.252
Smim11	6.647	0.714	5.356	0.000	0.004	2.175
Sept6	6.211	0.524	5.347	0.000	0.004	2.154
Pnisr	-9.220	3.742	-5.317	0.000	0.005	2.091
Harbi1	6.814	0.947	5.310	0.000	0.005	2.075
Cib2	6.694	0.564	5.202	0.000	0.006	1.841
Zfp512	10.367	1.100	5.194	0.000	0.006	1.824
Ell	7.857	0.607	5.190	0.000	0.006	1.813
C1qa	-5.577	9.728	-5.149	0.000	0.006	1.726
Prrc2c	-9.764	3.907	-5.144	0.000	0.006	1.715
Gm6061	3.017	0.235	5.071	0.000	0.008	1.554
Gm37988	4.109	0.279	4.994	0.000	0.009	1.387
Qser1	4.853	0.435	4.980	0.000	0.009	1.355
AC164550.2	2.776	0.218	4.977	0.000	0.009	1.349
Cpt1a	7.508	0.803	4.904	0.000	0.011	1.190
Hist1h3e	5.477	0.436	4.890	0.000	0.011	1.158
Gm38126	4.553	0.490	4.886	0.000	0.011	1.149
Rpap2	6.922	0.639	4.878	0.000	0.011	1.133
Nup54	0.822	0.054	4.865	0.000	0.011	1.104
Rnaset2b	-8.388	7.105	-4.858	0.000	0.011	1.088
Idua	8.034	0.983	4.839	0.000	0.012	1.047
Lrrc45	7.832	0.539	4.838	0.000	0.012	1.044
Rbm34	8.203	0.996	4.810	0.000	0.012	0.983
Slc26a11	5.718	0.463	4.790	0.000	0.013	0.938
Hexb	-8.872	9.372	-4.774	0.000	0.013	0.903
Dlg1	6.591	0.912	4.738	0.000	0.014	0.822
Zfand2a	7.816	0.760	4.706	0.000	0.015	0.752
Xylt1	3.443	0.485	4.650	0.000	0.017	0.629
Zfyve16	6.636	0.542	4.620	0.000	0.018	0.563
Mpeg1	-6.781	7.133	-4.600	0.000	0.019	0.517
Slamf7	6.823	0.559	4.595	0.000	0.019	0.507
Pigyl	5.390	0.446	4.589	0.000	0.019	0.494
Gm42857	-6.906	3.936	-4.579	0.000	0.019	0.471
Gm26905	-10.468	5.057	-4.577	0.000	0.019	0.468
Apoe	-10.814	10.645	-4.569	0.000	0.019	0.450
Prmt3	6.694	0.592	4.551	0.000	0.020	0.410
Fhad1	2.912	0.239	4.550	0.000	0.020	0.406
Pdhx	3.495	0.288	4.530	0.000	0.021	0.362
Nol10	3.918	0.364	4.513	0.000	0.021	0.325
Sirpb1a	6.446	0.539	4.491	0.000	0.022	0.276
Gm16505	2.736	0.386	4.488	0.000	0.022	0.270
Slc12a8	1.764	0.174	4.475	0.000	0.023	0.241
Gm37090	4.076	0.575	4.449	0.000	0.024	0.184
Fbxo21	6.792	0.482	4.448	0.000	0.024	0.181
Igsf11	1.079	0.096	4.409	0.000	0.026	0.094
AC183268.1	3.082	0.413	4.385	0.000	0.027	0.041
Gm37733	6.052	0.837	4.384	0.000	0.027	0.039
Vps36	6.857	0.670	4.370	0.000	0.028	0.008
Pex6	7.874	0.819	4.367	0.000	0.028	0.001
Ppid	10.086	1.694	4.360	0.000	0.028	-0.014
Gpr35	5.583	0.593	4.345	0.000	0.029	-0.047
Nelfa	3.818	0.273	4.341	0.000	0.029	-0.057
Tcea1-ps1	3.905	0.355	4.317	0.000	0.031	-0.109
Ppp2r2d	6.807	0.965	4.301	0.000	0.031	-0.147
Mefv	6.192	0.652	4.300	0.000	0.031	-0.147
Me2	6.640	0.561	4.299	0.000	0.031	-0.149
Hnrnpul2	6.202	0.752	4.260	0.000	0.034	-0.236
Ech1	9.434	1.154	4.245	0.000	0.035	-0.270
Map2k4	7.258	0.919	4.243	0.000	0.035	-0.274
4930481A15Rik	7.796	0.954	4.232	0.000	0.036	-0.300
Tpm3-rs7	2.746	0.267	4.225	0.000	0.036	-0.315
2300009A05Rik	3.876	0.493	4.223	0.000	0.036	-0.320
Gabra2	2.847	0.470	4.218	0.000	0.036	-0.330
Fn1	8.167	1.215	4.201	0.000	0.037	-0.368
Mtmr3	5.930	0.597	4.199	0.001	0.037	-0.372
Mfsd4b5	2.740	0.311	4.188	0.001	0.038	-0.396
Mtr	5.299	0.579	4.166	0.001	0.040	-0.446
Ttc32	7.552	0.711	4.155	0.001	0.041	-0.471
Tnpo2	7.578	0.787	4.131	0.001	0.043	-0.524
Acot6	1.824	0.156	4.127	0.001	0.043	-0.534
Adcy8	1.802	0.156	4.114	0.001	0.044	-0.561
Khdrbs2	2.875	0.270	4.098	0.001	0.045	-0.597
Ripor1	7.634	0.764	4.077	0.001	0.047	-0.643
Dcaf5	6.259	0.583	4.063	0.001	0.048	-0.675
Tex10	5.290	0.389	4.062	0.001	0.048	-0.676
Haus6	2.883	0.250	4.052	0.001	0.049	-0.698

res_output[["plot"]]

[1] "no significant enrichment identified"

get results table here

Granulozytes

calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference

idx=samplemeta$Celltype=="Granulocytes"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])

        
         WT APPPS1+
  Ctrl    0       2
  Stroke  6      14

table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()

    Fisher's Exact Test for Count Data

data:  .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  0.0000 14.7518
sample estimates:
odds ratio 
         0 

# res_output=generate_output("Granulocytes ")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]

get results table here

T_NK

idx=samplemeta$Celltype=="T/NK"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])

        
         WT APPPS1+
  Ctrl    1       2
  Stroke  2      17

table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()

    Fisher's Exact Test for Count Data

data:  .
p-value = 0.3708
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
   0.0481749 117.2316789
sample estimates:
odds ratio 
  3.869104 

# res_output=generate_output("T/NK")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]

get results table here

Gene expression app

secret="C:/Users/andreas_chiocchetti/OneDrive/personal/fuchs_credentials.R"
source(secret)

counts=as.data.frame(samples.integrated@assays$RNA@data)

save(list=c("samplemeta", "counts"), file=paste0(home, "/geneviewer/Dataset.RData"))

rsconnect::setAccountInfo(name='molgenlab',
              token='86875F8B6550C3A26488035E69B1F18D',
              secret=shinySECRET)

rsconnect::deployApp(paste0(home, "/geneviewer"))

Preparing to deploy application...DONE
Uploading bundle for application: 5700187...DONE
Deploying bundle: 5602090 for application: 5700187 ...
Waiting for task: 1104605741
  building: Parsing manifest
  building: Building image: 6510932
  building: Fetching packages
  building: Installing packages
  building: Installing files
  building: Pushing image: 6510932
  deploying: Starting instances
  rollforward: Activating new instances
  terminating: Stopping old instances
Application successfully deployed to https://molgenlab.shinyapps.io/geneviewer/