Last updated: 2022-02-03

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

# convert to correct data type 

# define gentype 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="Mouse_ID"){
  
  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 3.585 0.454 12.282 0 0.000 45.438
AC099934.1 2.089 0.223 11.040 0 0.000 37.798
Rn7s1 4.776 0.932 10.655 0 0.000 35.451
AY036118 -11.081 6.541 -9.191 0 0.000 26.688
Prrx1 2.430 0.394 8.393 0 0.000 22.080
Tmem170 2.181 0.349 7.389 0 0.000 16.541
Rn7s2 3.690 0.821 7.361 0 0.000 16.391
Olfr907 2.682 0.908 6.890 0 0.000 13.921
Dock10 4.005 5.039 6.134 0 0.000 10.154
Mgmt 2.582 0.444 6.078 0 0.000 9.889
AW822252 2.123 0.732 6.022 0 0.000 9.622
Apoe -11.355 7.561 -5.777 0 0.000 8.474
Trem2 -5.762 6.733 -5.673 0 0.000 7.996
D10Wsu102e -2.591 7.009 -5.633 0 0.000 7.816
Lgals3bp -7.870 3.952 -5.603 0 0.000 7.678
Hexb -5.725 8.947 -5.549 0 0.000 7.437
Fcer1g -5.665 6.858 -5.511 0 0.000 7.266
Ctsd -6.140 9.513 -5.411 0 0.000 6.823
B2m -5.699 7.942 -5.382 0 0.000 6.695
Gm44215 -4.526 3.605 -5.328 0 0.000 6.459
Gnaz 2.329 0.780 5.316 0 0.000 6.405
C1qb -6.427 8.397 -5.302 0 0.000 6.345
Lipe 2.874 1.789 5.275 0 0.000 6.229
CT010467.1 -4.628 10.638 -5.247 0 0.000 6.109
C1qa -6.016 8.093 -5.171 0 0.000 5.783
Lyz2 -8.389 5.945 -5.164 0 0.000 5.750
Itm2b -5.448 8.165 -5.106 0 0.000 5.506
Cst3 -5.520 10.468 -5.093 0 0.000 5.451
Gm37144 2.615 1.609 5.088 0 0.000 5.426
Gm44645 1.755 0.462 5.048 0 0.000 5.260
Ctsb -5.775 8.220 -4.989 0 0.001 5.014
Myl2 1.632 0.203 4.907 0 0.001 4.676
C1qc -5.524 8.304 -4.903 0 0.001 4.659
Cst7 -7.498 3.339 -4.821 0 0.001 4.325
Grn -5.003 6.427 -4.692 0 0.002 3.806
Plac8 1.718 0.166 4.631 0 0.003 3.565
Ctss -4.862 9.414 -4.602 0 0.003 3.454
Ctsz -5.238 7.907 -4.580 0 0.003 3.367
Pola2 -2.571 2.264 -4.573 0 0.003 3.340
Hexa -5.506 6.097 -4.559 0 0.003 3.284
Cd52 -5.180 3.497 -4.551 0 0.003 3.252
AC127302.3 -2.871 2.889 -4.520 0 0.004 3.132
Gm37716 1.458 0.498 4.502 0 0.004 3.063
Gm26905 -3.426 6.505 -4.491 0 0.004 3.021
Cyba -4.779 5.245 -4.486 0 0.004 3.004
Gpr171 1.223 0.095 4.380 0 0.006 2.602
Fhl3 1.950 0.607 4.340 0 0.007 2.451
Col6a3 2.169 0.420 4.322 0 0.007 2.385
Axl -5.288 2.164 -4.302 0 0.008 2.311
Gm15500 -5.146 4.285 -4.298 0 0.008 2.294
Fth1 -4.811 7.555 -4.295 0 0.008 2.284
H2-D1 -5.238 6.578 -4.277 0 0.008 2.219
Gm10076 -3.694 2.856 -4.270 0 0.008 2.193
Cd81 -5.151 7.456 -4.249 0 0.009 2.115
Tyrobp -4.344 7.358 -4.231 0 0.009 2.050
Upk1b 3.244 1.015 4.210 0 0.010 1.971
Atp6v0c -5.076 4.998 -4.200 0 0.010 1.935
Clec7a -5.541 2.762 -4.165 0 0.011 1.807
Gm26870 -3.534 9.134 -4.160 0 0.011 1.791
Selenop -5.025 6.801 -4.098 0 0.014 1.568
Gm15564 1.507 0.765 4.090 0 0.014 1.542
Serinc3 -3.698 6.433 -4.079 0 0.015 1.502
Eef1a1 -4.585 7.535 -4.069 0 0.015 1.465
Fcgr3 -4.245 7.279 -4.032 0 0.017 1.335
Soat1 -3.697 2.004 -4.022 0 0.018 1.300
Gm14303 -2.839 2.264 -3.998 0 0.019 1.215
Ppp2r5a -3.151 4.146 -3.969 0 0.021 1.117
Gm10719 -3.157 6.711 -3.940 0 0.023 1.017
Lamp2 -4.702 4.178 -3.928 0 0.024 0.973
Rpl13a -4.190 4.835 -3.927 0 0.024 0.970
Rnaset2b -4.084 6.400 -3.917 0 0.024 0.935
Fcgr2b -4.894 4.773 -3.915 0 0.024 0.930
Mpeg1 -4.315 6.028 -3.861 0 0.029 0.747
Gm43652 1.435 0.726 3.858 0 0.029 0.734
Rab3gap1 -2.808 3.206 -3.847 0 0.030 0.697
Thy1 1.353 0.317 3.839 0 0.030 0.672
Mt1 -3.813 2.286 -3.828 0 0.031 0.635
Cry1 1.188 0.121 3.795 0 0.035 0.524
Acbd5 -2.554 1.954 -3.786 0 0.036 0.495
Bicd1 -2.198 2.661 -3.767 0 0.038 0.429
Lgmn -4.127 7.604 -3.763 0 0.038 0.418
Cd9 -4.193 6.255 -3.724 0 0.043 0.290 
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 4.840 2.251 9.842 0 0.000 28.568
AB124611 2.674 0.089 7.844 0 0.000 17.964
Hexb -4.018 10.455 -6.164 0 0.000 9.816
Pola2 -4.177 1.551 -6.144 0 0.000 9.723
Gbp2 2.735 0.131 6.096 0 0.000 9.507
Gbp8 2.376 0.120 5.729 0 0.000 7.881
Ogfod2 2.770 0.186 5.531 0 0.000 7.028
AY036118 -7.320 4.063 -5.282 0 0.001 5.987
Adgre5 1.340 0.041 5.246 0 0.001 5.837
Lzic 2.339 0.160 5.200 0 0.001 5.652
Trdmt1 1.439 0.094 5.190 0 0.001 5.610
Gbp4 1.865 0.187 5.151 0 0.001 5.450
Vav3 0.781 0.024 5.085 0 0.001 5.187
Psmd10 3.468 0.411 4.906 0 0.002 4.476
C3 3.067 0.263 4.786 0 0.003 4.009
Dck 2.402 0.210 4.758 0 0.004 3.903
1700003F12Rik 0.918 0.044 4.725 0 0.004 3.776
Mndal 0.867 0.045 4.620 0 0.006 3.378
Eif1b 4.259 0.868 4.539 0 0.008 3.078
Galt 2.814 0.377 4.514 0 0.008 2.988
Sparc -3.764 7.598 -4.449 0 0.010 2.748
Bicdl1 1.325 0.080 4.431 0 0.011 2.684
Cers6 2.076 0.193 4.414 0 0.011 2.623
Gm15952 1.267 0.159 4.404 0 0.011 2.584
Mrps5 2.606 0.350 4.360 0 0.013 2.425
AC099934.1 1.433 0.595 4.251 0 0.019 2.040
Lilrb4a 2.909 0.332 4.221 0 0.020 1.934
Zbtb41 1.103 0.076 4.216 0 0.020 1.916
Rn7s2 3.411 1.658 4.143 0 0.026 1.664
2810414N06Rik 1.849 0.206 4.127 0 0.027 1.609
Mapkbp1 2.387 0.252 4.108 0 0.028 1.545
Aplf 1.966 0.206 4.081 0 0.030 1.452
Chchd10 1.846 0.165 4.077 0 0.030 1.439
B4galt6 3.479 0.769 4.031 0 0.034 1.284
Cited4 0.792 0.042 4.011 0 0.035 1.215
Cd9 -5.293 6.392 -4.011 0 0.035 1.214
Trmt112 4.680 1.995 4.003 0 0.035 1.190
Stt3a 5.160 2.125 3.979 0 0.037 1.108
Mgmt 2.321 1.070 3.976 0 0.037 1.098
Sh3pxd2b 1.437 0.108 3.970 0 0.037 1.077
Aars2 1.447 0.127 3.928 0 0.042 0.939
Itgal 1.063 0.069 3.885 0 0.049 0.797
Gm29474 0.931 0.060 3.873 0 0.050 0.758
Homer3 1.961 0.288 3.866 0 0.050 0.735 
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 -12.456 5.877 -15.019 0 0.000 45.596
Rn7s1 6.385 2.015 11.188 0 0.000 30.400
AC099934.1 2.241 0.649 10.570 0 0.000 27.765
Gab3 6.340 1.369 8.543 0 0.000 18.964
Tbata 3.179 1.094 7.387 0 0.000 13.974
Tmem170 2.548 0.931 6.722 0 0.000 11.166
Mgmt 2.922 1.015 5.878 0 0.000 7.742
Katnb1 2.102 0.162 5.265 0 0.001 5.382
Gm10719 -4.777 4.443 -5.147 0 0.002 4.943
Gm15564 2.543 1.222 5.077 0 0.002 4.684
Gm26870 -5.882 6.156 -4.978 0 0.003 4.322
Sorbs2 2.075 0.168 4.975 0 0.003 4.313
Rn7s2 4.114 1.358 4.975 0 0.003 4.313
Actr3b 2.047 0.143 4.951 0 0.003 4.224
Gm10800 -6.145 7.447 -4.929 0 0.003 4.147
Zbp1 1.537 0.157 4.887 0 0.003 3.994
Gm10801 -4.772 5.022 -4.744 0 0.005 3.486
Chchd5 1.940 0.189 4.477 0 0.014 2.562
Gm37401 3.759 1.196 4.474 0 0.014 2.550
Gm10717 -3.987 4.416 -4.376 0 0.020 2.220
Gm37411 1.616 0.409 4.363 0 0.020 2.176
Pola2 -2.875 2.295 -4.244 0 0.030 1.783
Cd209g 1.589 0.134 4.214 0 0.032 1.685
Bola1 2.986 0.571 4.187 0 0.034 1.596
Olfr986 -3.227 1.697 -4.129 0 0.039 1.407
Gm14399 1.217 0.131 4.118 0 0.039 1.374
Tmem18 2.653 0.386 4.110 0 0.039 1.349
Prrx1 1.567 0.589 4.097 0 0.039 1.307
A930024E05Rik 2.016 0.166 4.083 0 0.039 1.262
Gm29423 1.341 0.133 4.080 0 0.039 1.252
Arl5c 3.191 0.740 4.079 0 0.039 1.249
Mfsd11 5.329 2.007 4.037 0 0.043 1.113
Rsad2 1.152 0.075 4.035 0 0.043 1.108
Ift122 1.905 0.398 4.012 0 0.046 1.035
Cpq 3.335 0.724 3.996 0 0.047 0.985 
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 -10.430 8.936 -8.947 0 0.000 20.625
Rn7s1 4.769 1.890 8.267 0 0.000 17.702
Tmem170 2.312 0.742 7.906 0 0.000 16.152
Pcid2 2.823 0.185 6.034 0 0.000 8.365
Pigq 5.880 0.990 5.999 0 0.000 8.222
Rnf14 3.795 0.386 5.864 0 0.000 7.691
Tbata 2.878 0.831 5.857 0 0.000 7.663
AY036118 -8.712 5.396 -5.382 0 0.001 5.825
BC026585 4.418 0.909 5.347 0 0.001 5.693
C1galt1 1.556 0.079 5.322 0 0.001 5.598
Rbmx2 1.261 0.064 5.303 0 0.001 5.529
Ppil1 2.138 0.194 5.195 0 0.001 5.124
Vmn1r208 0.664 0.034 5.102 0 0.002 4.780
Cnnm4 3.537 0.429 4.880 0 0.004 3.970
Mgmt 2.960 0.931 4.847 0 0.004 3.854
Alad 2.420 0.203 4.796 0 0.005 3.672
Tpst2 6.203 2.268 4.723 0 0.006 3.413
Gt(ROSA)26Sor 3.426 0.561 4.723 0 0.006 3.411
Fkbp14 2.454 0.221 4.558 0 0.011 2.836
Gm15564 2.301 1.137 4.550 0 0.011 2.809
Clec7a -7.087 3.980 -4.520 0 0.012 2.704
Zfp712 1.488 0.117 4.479 0 0.013 2.564
Gm21092 1.788 0.267 4.420 0 0.015 2.364
Alg14 3.917 0.756 4.364 0 0.018 2.174
Gm10774 1.334 0.288 4.340 0 0.019 2.093
Cox18 1.842 0.189 4.318 0 0.019 2.021
AC210924.1 1.675 0.222 4.310 0 0.019 1.994
Acot8 2.048 0.175 4.306 0 0.019 1.981
Gm15417 0.943 0.079 4.301 0 0.019 1.964
Wsb2 4.868 1.184 4.239 0 0.024 1.758
Smim8 2.822 0.443 4.212 0 0.025 1.669
Gm43328 0.672 0.064 4.148 0 0.030 1.460
Ano4 1.287 0.214 4.148 0 0.030 1.460
Stam 3.019 0.485 4.129 0 0.030 1.399
Zfp513 3.072 0.452 4.121 0 0.030 1.373
Rn7s2 3.370 1.489 4.119 0 0.030 1.367
Bin2 6.349 4.050 4.116 0 0.030 1.355
Eif3d 4.381 0.900 4.091 0 0.032 1.274
Dand5 2.063 0.195 4.085 0 0.032 1.258
Syncrip 4.081 1.144 4.054 0 0.035 1.157
Zfp37 1.796 0.242 4.039 0 0.036 1.107
Zbed3 3.190 0.550 3.969 0 0.045 0.888
Tpcn1 3.983 1.227 3.949 0 0.048 0.824
Nexn 1.263 0.145 3.943 0 0.048 0.804
Ewsr1 5.605 2.675 3.935 0 0.048 0.781 
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 0.382 104.854 0.000 0.000 35.615
S100a4 8.061 0.350 96.181 0.000 0.000 35.386
Cenpt 7.209 0.313 86.024 0.000 0.000 35.037
Pilra 7.095 0.308 84.655 0.000 0.000 34.981
Fbxl8 6.966 0.303 83.116 0.000 0.000 34.915
Taf5l 6.826 0.297 81.447 0.000 0.000 34.840
Zgrf1 6.820 0.297 81.379 0.000 0.000 34.837
Sptan1 6.714 0.292 80.115 0.000 0.000 34.778
Cytip 6.569 0.286 78.378 0.000 0.000 34.692
Elac2 6.476 0.282 77.269 0.000 0.000 34.634
Map3k9 6.443 0.280 76.877 0.000 0.000 34.613
Mis18a 6.426 0.279 76.678 0.000 0.000 34.603
Nup35 6.426 0.279 76.678 0.000 0.000 34.603
Pdcd7 5.644 0.245 67.343 0.000 0.000 34.010
Slco3a1 5.248 0.228 62.619 0.000 0.000 33.624
S100a10 5.044 0.219 60.190 0.000 0.000 33.397
Gm43200 5.000 0.217 59.660 0.000 0.000 33.344
Npepl1 4.907 0.213 58.549 0.000 0.000 33.230
Sms-ps 4.654 0.202 55.531 0.000 0.000 32.894
2810001G20Rik 4.322 0.188 51.569 0.000 0.000 32.383
Gm1972 4.087 0.178 48.772 0.000 0.000 31.966
Pde8a 4.000 0.174 47.728 0.000 0.000 31.796
Zfp362 4.000 0.174 47.728 0.000 0.000 31.796
Glmn 3.807 0.166 45.430 0.000 0.000 31.394
Polr2d 3.807 0.166 45.430 0.000 0.000 31.394
Gm34237 3.513 0.153 41.914 0.000 0.000 30.687
Acsl3 3.322 0.144 39.637 0.000 0.000 30.162
Tsen54 3.322 0.144 39.637 0.000 0.000 30.162
Nedd4l 3.170 0.138 37.824 0.000 0.000 29.699
Pilrb1 2.855 0.124 34.069 0.000 0.000 28.591
Skint3 2.807 0.122 33.497 0.000 0.000 28.403
AI662270 6.170 0.312 28.448 0.000 0.000 26.454
Rps6ka4 2.322 0.101 27.705 0.000 0.000 26.118
AC131743.1 2.307 0.200 27.273 0.000 0.000 25.916
Mta3 7.289 0.365 25.673 0.000 0.000 25.120
Sirpb1b 5.404 0.278 24.903 0.000 0.000 24.710
Micall1 2.000 0.087 23.866 0.000 0.000 24.125
Taf1b 2.000 0.087 23.864 0.000 0.000 24.125
Gm44103 2.000 0.087 23.863 0.000 0.000 24.124
Gm20716 7.223 0.368 23.262 0.000 0.000 23.768
Hist1h4d 1.585 0.069 18.912 0.000 0.000 20.745
Ubash3a 1.585 0.069 18.912 0.000 0.000 20.745
Zbtb39 1.585 0.069 18.912 0.000 0.000 20.745
Fam69a 6.541 0.351 17.758 0.000 0.000 19.787
Batf3 4.835 0.259 17.180 0.000 0.000 19.278
Sh3bgrl2 3.459 0.194 15.951 0.000 0.000 18.125
Gm12411 2.477 0.131 15.942 0.000 0.000 18.116
Gm43444 3.314 0.180 15.272 0.000 0.000 17.443
Sde2 6.820 0.397 14.450 0.000 0.000 16.572
Mpnd 6.185 0.346 14.254 0.000 0.000 16.356
Ticam2 7.373 0.475 13.678 0.000 0.000 15.704
Dcaf15 5.858 0.331 13.374 0.000 0.000 15.348
C1qc -11.302 9.703 -12.989 0.000 0.000 14.885
Svep1 2.807 0.166 12.944 0.000 0.000 14.831
Gm45867 3.617 0.206 12.888 0.000 0.000 14.762
Ppp1r26 1.006 0.044 12.003 0.000 0.000 13.637
Gm33023 3.331 0.194 11.942 0.000 0.000 13.555
Gm37390 1.001 0.044 11.941 0.000 0.000 13.554
Cnn2 1.000 0.043 11.932 0.000 0.000 13.543
Fbxo8 1.000 0.043 11.932 0.000 0.000 13.543
Pex12 1.000 0.043 11.932 0.000 0.000 13.543
Gm3755 1.000 0.043 11.932 0.000 0.000 13.543
Sms 5.076 0.298 11.805 0.000 0.000 13.374
Ms4a4b 4.536 0.295 11.730 0.000 0.000 13.273
Gsr 5.084 0.297 11.606 0.000 0.000 13.106
Strip1 5.337 0.696 11.420 0.000 0.000 12.851
Gm42725 5.149 0.281 10.895 0.000 0.000 12.113
Naalad2 5.496 0.908 10.591 0.000 0.000 11.672
1810014B01Rik 2.980 0.178 10.589 0.000 0.000 11.668
AC154640.4 6.121 0.409 10.502 0.000 0.000 11.540
Erlin1 5.677 0.344 10.470 0.000 0.000 11.492
Gm37521 3.190 0.194 10.007 0.000 0.000 10.792
1190007I07Rik 5.935 0.413 9.822 0.000 0.000 10.505
Gm10146 2.768 0.180 9.623 0.000 0.000 10.190
Lrif1 5.056 0.317 9.323 0.000 0.000 9.709
Tada3 4.831 0.368 9.254 0.000 0.000 9.595
Gm4739 4.485 0.283 9.118 0.000 0.000 9.372
Gpatch11 2.482 0.156 8.740 0.000 0.000 8.736
Slfn1 6.534 0.421 8.612 0.000 0.000 8.516
Cog3 4.858 0.333 8.556 0.000 0.000 8.419
Proser1 6.161 0.428 8.488 0.000 0.000 8.301
Gm17229 3.537 0.232 8.149 0.000 0.000 7.704
CT025600.1 3.524 0.265 7.941 0.000 0.000 7.329
Mtrr 2.207 0.144 7.863 0.000 0.000 7.186
Cep57l1 1.115 0.069 7.793 0.000 0.000 7.058
AC154636.3 6.033 0.447 7.712 0.000 0.000 6.909
Arhgap11a 6.000 0.431 7.634 0.000 0.000 6.762
Ice1 5.045 0.409 7.502 0.000 0.000 6.515
Emb 5.870 0.469 7.443 0.000 0.000 6.406
Gle1 5.026 0.345 7.254 0.000 0.000 6.046
Gm38699 3.356 0.227 7.208 0.000 0.000 5.957
Tex2 8.813 0.534 7.196 0.000 0.000 5.933
Vac14 4.954 0.342 7.150 0.000 0.000 5.845
Ino80 4.988 0.811 7.130 0.000 0.000 5.806
Gm4924 2.475 0.203 7.004 0.000 0.000 5.563
Gm36065 1.885 0.130 6.716 0.000 0.000 4.995
Plac8 6.623 0.502 6.610 0.000 0.000 4.781
AC187103.1 0.943 0.063 6.519 0.000 0.000 4.599
Eme2 8.008 0.506 6.246 0.000 0.001 4.043
N4bp1 8.477 0.540 6.106 0.000 0.001 3.754
Gm13369 5.291 0.430 6.097 0.000 0.001 3.735
Cep70 6.696 0.427 6.056 0.000 0.001 3.649
Tfdp1 5.118 0.427 6.036 0.000 0.001 3.607
Ctr9 6.329 0.744 6.026 0.000 0.001 3.587
Slc2a3 2.097 0.289 5.935 0.000 0.001 3.397
Gm2436 4.020 0.358 5.897 0.000 0.002 3.318
4930404A12Rik 5.736 0.469 5.875 0.000 0.002 3.272
Ddx23 5.417 0.761 5.866 0.000 0.002 3.253
Ppp2r3c 7.240 0.773 5.854 0.000 0.002 3.227
Rad52 5.795 0.491 5.843 0.000 0.002 3.205
A630001O12Rik 4.927 0.394 5.791 0.000 0.002 3.095
9130401M01Rik 6.217 0.579 5.728 0.000 0.002 2.962
Hmga1 5.089 0.497 5.677 0.000 0.002 2.852
Hexa -10.196 7.250 -5.632 0.000 0.003 2.757
Gm10825 2.904 0.307 5.625 0.000 0.003 2.741
Rasgrp1 2.703 0.243 5.602 0.000 0.003 2.693
Gm45718 3.089 0.355 5.540 0.000 0.003 2.560
Fam98a 7.056 0.543 5.505 0.000 0.003 2.484
Birc3 8.822 0.973 5.463 0.000 0.003 2.396
Smim11 6.658 0.714 5.436 0.000 0.004 2.338
Sept6 6.240 0.524 5.430 0.000 0.004 2.323
Pnisr -9.226 3.742 -5.399 0.000 0.004 2.258
Harbi1 6.804 0.947 5.395 0.000 0.004 2.249
Cib2 6.727 0.564 5.266 0.000 0.005 1.970
Zfp512 10.361 1.100 5.255 0.000 0.005 1.945
Ell 7.857 0.607 5.241 0.000 0.005 1.914
C1qa -5.605 9.728 -5.234 0.000 0.005 1.900
Prrc2c -9.812 3.907 -5.198 0.000 0.006 1.822
Gm6061 3.034 0.235 5.136 0.000 0.007 1.686
Qser1 4.869 0.435 5.064 0.000 0.008 1.530
AC164550.2 2.768 0.218 5.062 0.000 0.008 1.524
Gm37988 4.109 0.279 5.038 0.000 0.008 1.472
Cpt1a 7.529 0.803 5.001 0.000 0.009 1.392
Hist1h3e 5.461 0.436 4.973 0.000 0.009 1.330
Rpap2 6.896 0.639 4.956 0.000 0.009 1.294
Gm38126 4.560 0.490 4.953 0.000 0.009 1.286
Rnaset2b -8.436 7.105 -4.930 0.000 0.010 1.236
Idua 8.029 0.983 4.929 0.000 0.010 1.234
Nup54 0.822 0.054 4.916 0.000 0.010 1.205
Lrrc45 7.832 0.539 4.880 0.000 0.011 1.126
Hexb -8.893 9.372 -4.875 0.000 0.011 1.114
Slc26a11 5.704 0.463 4.868 0.000 0.011 1.099
Rbm34 8.200 0.996 4.868 0.000 0.011 1.099
Dlg1 6.594 0.912 4.828 0.000 0.012 1.010
Zfand2a 7.845 0.760 4.764 0.000 0.013 0.869
Xylt1 3.427 0.485 4.708 0.000 0.015 0.746
Mpeg1 -6.780 7.133 -4.700 0.000 0.015 0.729
Zfyve16 6.676 0.542 4.681 0.000 0.016 0.687
Pigyl 5.377 0.446 4.663 0.000 0.016 0.647
Slamf7 6.865 0.559 4.656 0.000 0.016 0.631
Apoe -10.851 10.645 -4.649 0.000 0.016 0.615
Gm42857 -6.895 3.936 -4.644 0.000 0.017 0.603
Prmt3 6.674 0.592 4.628 0.000 0.017 0.570
Fhad1 2.903 0.239 4.626 0.000 0.017 0.564
Nol10 3.911 0.364 4.591 0.000 0.018 0.487
Pdhx 3.517 0.288 4.590 0.000 0.018 0.485
Gm26905 -10.419 5.057 -4.583 0.000 0.018 0.470
Sirpb1a 6.429 0.539 4.563 0.000 0.019 0.425
Slc12a8 1.777 0.174 4.546 0.000 0.020 0.387
Igsf11 1.079 0.096 4.511 0.000 0.021 0.310
Gm37090 4.102 0.575 4.510 0.000 0.021 0.307
Fbxo21 6.792 0.482 4.487 0.000 0.022 0.256
Gm16505 2.716 0.386 4.461 0.000 0.023 0.199
AC183268.1 3.079 0.413 4.457 0.000 0.023 0.189
Gm37733 6.042 0.837 4.436 0.000 0.024 0.143
Pex6 7.916 0.819 4.428 0.000 0.025 0.124
Vps36 6.862 0.670 4.416 0.000 0.025 0.098
Ppid 10.056 1.694 4.409 0.000 0.025 0.082
Gpr35 5.562 0.593 4.409 0.000 0.025 0.082
Tcea1-ps1 3.893 0.355 4.385 0.000 0.026 0.030
Ppp2r2d 6.806 0.965 4.380 0.000 0.026 0.019
Nelfa 3.818 0.273 4.379 0.000 0.026 0.016
Mefv 6.225 0.652 4.378 0.000 0.026 0.014
Me2 6.618 0.561 4.371 0.000 0.027 -0.002
2300009A05Rik 3.891 0.493 4.322 0.000 0.030 -0.111
Hnrnpul2 6.174 0.752 4.310 0.000 0.030 -0.138
Tpm3-rs7 2.746 0.267 4.288 0.000 0.032 -0.185
Gabra2 2.837 0.470 4.286 0.000 0.032 -0.191
Map2k4 7.239 0.919 4.282 0.000 0.032 -0.200
Ech1 9.397 1.154 4.281 0.000 0.032 -0.202
4930481A15Rik 7.771 0.954 4.274 0.000 0.032 -0.217
Mtmr3 5.946 0.597 4.258 0.000 0.033 -0.254
Mfsd4b5 2.735 0.311 4.256 0.000 0.033 -0.258
Fn1 8.142 1.215 4.246 0.000 0.034 -0.280
Ttc32 7.534 0.711 4.203 0.000 0.037 -0.375
Mtr 5.276 0.579 4.196 0.001 0.037 -0.391
Acot6 1.818 0.156 4.194 0.001 0.037 -0.395
Tnpo2 7.555 0.787 4.180 0.001 0.038 -0.427
Adcy8 1.797 0.156 4.179 0.001 0.038 -0.429
Khdrbs2 2.866 0.270 4.161 0.001 0.039 -0.469
Ripor1 7.623 0.764 4.119 0.001 0.043 -0.562
Haus6 2.903 0.250 4.110 0.001 0.044 -0.582
Dcaf5 6.249 0.583 4.104 0.001 0.044 -0.595
Tex10 5.290 0.389 4.098 0.001 0.044 -0.609
Stk26 4.345 0.385 4.070 0.001 0.047 -0.671
Hist1h2ae 4.538 0.541 4.064 0.001 0.047 -0.685
March2 5.394 0.758 4.062 0.001 0.047 -0.689
Gm28959 3.928 0.441 4.052 0.001 0.048 -0.711
Mcm2 5.506 0.659 4.043 0.001 0.049 -0.730
Fam193a 7.138 0.950 4.041 0.001 0.049 -0.735 
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


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=German_Germany.1252  LC_CTYPE=German_Germany.1252    LC_MONETARY=German_Germany.1252 LC_NUMERIC=C                    LC_TIME=German_Germany.1252    

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gprofiler2_0.2.1            lm.beta_1.5-1               pheatmap_1.0.12             RColorBrewer_1.1-2          kableExtra_1.3.4            viridis_0.6.2               viridisLite_0.4.0           xlsx_0.6.5                  brms_2.16.3                 Rcpp_1.0.8                  compareGroups_4.5.1         data.table_1.14.2           SingleR_1.8.1               SeuratObject_4.0.4          Seurat_4.1.0                forcats_0.5.1               stringr_1.4.0               dplyr_1.0.7                 purrr_0.3.4                 readr_2.1.2                 tidyr_1.1.4                 tibble_3.1.6                ggplot2_3.3.5               tidyverse_1.3.1             DESeq2_1.34.0               SummarizedExperiment_1.24.0 Biobase_2.54.0              MatrixGenerics_1.6.0        matrixStats_0.61.0          GenomicRanges_1.46.1        GenomeInfoDb_1.30.1         IRanges_2.28.0              S4Vectors_0.32.3            BiocGenerics_0.40.0         limma_3.50.0                workflowr_1.7.0            

loaded via a namespace (and not attached):
  [1] scattermore_0.7           coda_0.19-4               bit64_4.0.5               knitr_1.37                dygraphs_1.1.1.6          irlba_2.3.5               DelayedArray_0.20.0       inline_0.3.19             rpart_4.1.16              KEGGREST_1.34.0           RCurl_1.98-1.5            generics_0.1.2            ScaledMatrix_1.2.0        callr_3.7.0               cowplot_1.1.1             RSQLite_2.2.9             mice_3.14.0               RANN_2.6.1                future_1.23.0             chron_2.3-56              bit_4.0.4                 tzdb_0.2.0                spatstat.data_2.1-2       webshot_0.5.2             xml2_1.3.3                lubridate_1.8.0           httpuv_1.6.5              StanHeaders_2.21.0-7      assertthat_0.2.1          xfun_0.29                 rJava_1.0-6               hms_1.1.1                 jquerylib_0.1.4           bayesplot_1.8.1           evaluate_0.14             promises_1.2.0.1          fansi_1.0.2               dbplyr_2.1.1              readxl_1.3.1              igraph_1.2.11             DBI_1.1.2                 geneplotter_1.72.0        Rsolnp_1.16               htmlwidgets_1.5.4         tensorA_0.36.2            spatstat.geom_2.3-1       ellipsis_0.3.2            crosstalk_1.2.0           backports_1.4.1           markdown_1.1              annotate_1.72.0           RcppParallel_5.1.5        deldir_1.0-6              sparseMatrixStats_1.6.0   vctrs_0.3.8               ROCR_1.0-11               abind_1.4-5               cachem_1.0.6              withr_2.4.3               HardyWeinberg_1.7.4       checkmate_2.0.0           sctransform_0.3.3         prettyunits_1.1.1         xts_0.12.1                goftest_1.2-3             svglite_2.0.0             cluster_2.1.2             lazyeval_0.2.2            crayon_1.4.2              genefilter_1.76.0         pkgconfig_2.0.3           nlme_3.1-155              nnet_7.3-17               rlang_1.0.0               globals_0.14.0            lifecycle_1.0.1           miniUI_0.1.1.1            colourpicker_1.1.1        modelr_0.1.8              rsvd_1.0.5                distributional_0.3.0      cellranger_1.1.0          rprojroot_2.0.2           polyclip_1.10-0           lmtest_0.9-39             flextable_0.6.10          Matrix_1.4-0              loo_2.4.1                 zoo_1.8-9                 reprex_2.0.1              base64enc_0.1-3           whisker_0.4               ggridges_0.5.3            processx_3.5.2            png_0.1-7                 bitops_1.0-7              getPass_0.2-2             KernSmooth_2.23-20        Biostrings_2.62.0         blob_1.2.2                DelayedMatrixStats_1.16.0 parallelly_1.30.0         shinystan_2.5.0           beachmat_2.10.0           scales_1.1.1              memoise_2.0.1             magrittr_2.0.2            plyr_1.8.6                ica_1.0-2                 threejs_0.3.3             zlibbioc_1.40.0           compiler_4.1.2            rstantools_2.1.1          fitdistrplus_1.1-6        cli_3.1.1                 XVector_0.34.0            listenv_0.8.0             Brobdingnag_1.2-7         patchwork_1.1.1           pbapply_1.5-0             ps_1.6.0                  MASS_7.3-55               mgcv_1.8-38               tidyselect_1.1.1          stringi_1.7.6             highr_0.9                 yaml_2.2.2                BiocSingular_1.10.0       locfit_1.5-9.4            ggrepel_0.9.1             bridgesampling_1.1-2      grid_4.1.2                sass_0.4.0                tools_4.1.2               future.apply_1.8.1        parallel_4.1.2            rstudioapi_0.13           uuid_1.0-3                git2r_0.29.0              gridExtra_2.3             farver_2.1.0              posterior_1.2.0           Rtsne_0.15                digest_0.6.29             shiny_1.7.1               broom_0.7.12              later_1.3.0               writexl_1.4.0             RcppAnnoy_0.0.19          httr_1.4.2                gdtools_0.2.3             AnnotationDbi_1.56.2      rsconnect_0.8.25          colorspace_2.0-2          rvest_1.0.2               XML_3.99-0.8              fs_1.5.2                  tensor_1.5                reticulate_1.24           truncnorm_1.0-8           splines_4.1.2             statmod_1.4.36            uwot_0.1.11               spatstat.utils_2.3-0      xlsxjars_0.6.1            shinythemes_1.2.0         plotly_4.10.0             systemfonts_1.0.3         xtable_1.8-4              jsonlite_1.7.3            rstan_2.21.3              R6_2.5.1                  pillar_1.7.0              htmltools_0.5.2           mime_0.12                 DT_0.20                   glue_1.6.1                fastmap_1.1.0             BiocParallel_1.28.3       BiocNeighbors_1.12.0      codetools_0.2-18          pkgbuild_1.3.1            mvtnorm_1.1-3             utf8_1.2.2                lattice_0.20-45           bslib_0.3.1               spatstat.sparse_2.1-0     leiden_0.3.9              gtools_3.9.2              officer_0.4.1             shinyjs_2.1.0             zip_2.2.0                 survival_3.2-13           rmarkdown_2.11            munsell_0.5.0             GenomeInfoDbData_1.2.7    haven_2.4.3               reshape2_1.4.4            gtable_0.3.0              spatstat.core_2.3-2