Biostatistical analysis part 2
AGC
2 2 2022
Last updated: 2022-02-03
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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 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
table(Mouse_ID=samplemeta$Mouse_ID) %>% as.data.frame() %>% display_tab()| Mouse_ID | Freq |
|---|---|
| 23#15773 | 66 |
| 23#15774 | 49 |
| 23#15792 | 47 |
| 386 | 41 |
| 387 | 44 |
| 388 | 113 |
| 409 | 86 |
| 457 | 115 |
| 461 | 88 |
Cells per Strain
table(Strain=samplemeta$Genotype_corr, Treatment=samplemeta$Treatment) %>% as.data.frame() %>% display_tab()| Strain | Treatment | Freq |
|---|---|---|
| WT | Ctrl | 162 |
| APPPS1+ | Ctrl | 228 |
| WT | Stroke | 127 |
| APPPS1+ | Stroke | 132 |
table(Strain=samplemeta$Genotype_corr, Celltype=samplemeta$Celltype) %>%
as.data.frame() %>% display_tab()| Strain | 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")
res = compareGroups(Celltype~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
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%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ #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$Celltype, function(z){sum(z>5,na.rm=T)})})
# keep RNAs with at least 5 cells with goood expression
idx=which(colSums(counts_per_celltype)>5)
counts = counts[idx,]Hierarchical clustering
to check where the variance in the data comes from
log2_cpm = log2(counts+1)
varsset=apply(log2_cpm, 1, var)
cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:2000],])
distance = dist(cpm.sel.trans)
sampleDistMatrix <- as.matrix(distance)
#colors for plotting heatmap
colors <- colorRampPalette(brewer.pal(9, "Spectral"))(255)
cellcol = Dark8[1:nlevels(samplemeta$Celltype)]
names(cellcol) = levels(samplemeta$Celltype)
genotypecol = brewer.pal(4,"Accent")[c(1:nlevels(samplemeta$Genotype_corr))]
names(genotypecol) = levels(samplemeta$Genotype_corr)
mousecol = brewer.pal(9,"Set1")[1:nlevels(samplemeta$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
)
labels = samplemeta[,c("Genotype_corr","Mouse_ID", "Brain_region", "Celltype")] %>%
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 ="row",
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")
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
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] pheatmap_1.0.12 RColorBrewer_1.1-2
[3] kableExtra_1.3.4 compareGroups_4.5.1
[5] data.table_1.14.2 SingleR_1.8.1
[7] SeuratObject_4.0.4 Seurat_4.1.0
[9] forcats_0.5.1 stringr_1.4.0
[11] dplyr_1.0.7 purrr_0.3.4
[13] readr_2.1.2 tidyr_1.1.4
[15] tibble_3.1.6 ggplot2_3.3.5
[17] tidyverse_1.3.1 DESeq2_1.34.0
[19] SummarizedExperiment_1.24.0 Biobase_2.54.0
[21] MatrixGenerics_1.6.0 matrixStats_0.61.0
[23] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[25] IRanges_2.28.0 S4Vectors_0.32.3
[27] BiocGenerics_0.40.0 limma_3.50.0
[29] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] scattermore_0.7 bit64_4.0.5
[3] knitr_1.37 irlba_2.3.5
[5] DelayedArray_0.20.0 rpart_4.1.16
[7] KEGGREST_1.34.0 RCurl_1.98-1.5
[9] generics_0.1.2 ScaledMatrix_1.2.0
[11] callr_3.7.0 cowplot_1.1.1
[13] RSQLite_2.2.9 mice_3.14.0
[15] RANN_2.6.1 future_1.23.0
[17] chron_2.3-56 bit_4.0.4
[19] tzdb_0.2.0 spatstat.data_2.1-2
[21] webshot_0.5.2 xml2_1.3.3
[23] lubridate_1.8.0 httpuv_1.6.5
[25] assertthat_0.2.1 xfun_0.29
[27] hms_1.1.1 jquerylib_0.1.4
[29] evaluate_0.14 promises_1.2.0.1
[31] fansi_1.0.2 dbplyr_2.1.1
[33] readxl_1.3.1 igraph_1.2.11
[35] DBI_1.1.2 geneplotter_1.72.0
[37] Rsolnp_1.16 htmlwidgets_1.5.4
[39] spatstat.geom_2.3-1 ellipsis_0.3.2
[41] backports_1.4.1 annotate_1.72.0
[43] deldir_1.0-6 sparseMatrixStats_1.6.0
[45] vctrs_0.3.8 ROCR_1.0-11
[47] abind_1.4-5 cachem_1.0.6
[49] withr_2.4.3 HardyWeinberg_1.7.4
[51] sctransform_0.3.3 goftest_1.2-3
[53] svglite_2.0.0 cluster_2.1.2
[55] lazyeval_0.2.2 crayon_1.4.2
[57] genefilter_1.76.0 pkgconfig_2.0.3
[59] nlme_3.1-155 nnet_7.3-17
[61] rlang_1.0.0 globals_0.14.0
[63] lifecycle_1.0.1 miniUI_0.1.1.1
[65] modelr_0.1.8 rsvd_1.0.5
[67] cellranger_1.1.0 rprojroot_2.0.2
[69] polyclip_1.10-0 lmtest_0.9-39
[71] flextable_0.6.10 Matrix_1.4-0
[73] zoo_1.8-9 reprex_2.0.1
[75] base64enc_0.1-3 whisker_0.4
[77] ggridges_0.5.3 processx_3.5.2
[79] png_0.1-7 viridisLite_0.4.0
[81] bitops_1.0-7 getPass_0.2-2
[83] KernSmooth_2.23-20 Biostrings_2.62.0
[85] blob_1.2.2 DelayedMatrixStats_1.16.0
[87] parallelly_1.30.0 beachmat_2.10.0
[89] scales_1.1.1 memoise_2.0.1
[91] magrittr_2.0.2 plyr_1.8.6
[93] ica_1.0-2 zlibbioc_1.40.0
[95] compiler_4.1.2 fitdistrplus_1.1-6
[97] cli_3.1.1 XVector_0.34.0
[99] listenv_0.8.0 patchwork_1.1.1
[101] pbapply_1.5-0 ps_1.6.0
[103] MASS_7.3-55 mgcv_1.8-38
[105] tidyselect_1.1.1 stringi_1.7.6
[107] highr_0.9 yaml_2.2.2
[109] BiocSingular_1.10.0 locfit_1.5-9.4
[111] ggrepel_0.9.1 grid_4.1.2
[113] sass_0.4.0 tools_4.1.2
[115] future.apply_1.8.1 parallel_4.1.2
[117] rstudioapi_0.13 uuid_1.0-3
[119] git2r_0.29.0 gridExtra_2.3
[121] farver_2.1.0 Rtsne_0.15
[123] digest_0.6.29 shiny_1.7.1
[125] Rcpp_1.0.8 broom_0.7.12
[127] later_1.3.0 writexl_1.4.0
[129] RcppAnnoy_0.0.19 httr_1.4.2
[131] gdtools_0.2.3 AnnotationDbi_1.56.2
[133] colorspace_2.0-2 rvest_1.0.2
[135] XML_3.99-0.8 fs_1.5.2
[137] tensor_1.5 reticulate_1.24
[139] truncnorm_1.0-8 splines_4.1.2
[141] uwot_0.1.11 spatstat.utils_2.3-0
[143] plotly_4.10.0 systemfonts_1.0.3
[145] xtable_1.8-4 jsonlite_1.7.3
[147] R6_2.5.1 pillar_1.7.0
[149] htmltools_0.5.2 mime_0.12
[151] glue_1.6.1 fastmap_1.1.0
[153] BiocParallel_1.28.3 BiocNeighbors_1.12.0
[155] codetools_0.2-18 utf8_1.2.2
[157] lattice_0.20-45 bslib_0.3.1
[159] spatstat.sparse_2.1-0 leiden_0.3.9
[161] officer_0.4.1 zip_2.2.0
[163] survival_3.2-13 rmarkdown_2.11
[165] munsell_0.5.0 GenomeInfoDbData_1.2.7
[167] haven_2.4.3 reshape2_1.4.4
[169] gtable_0.3.0 spatstat.core_2.3-2