Zeisel data
Last updated: 2024-01-01
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Knit directory:
mage_2020_marker-gene-benchmarking/
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| Rmd | e08580f | Jeffrey Pullin | 2021-07-22 | Update Zeisel data analysis |
| Rmd | 13dae62 | Jeffrey Pullin | 2021-07-21 | Add preliminary analysis of Zeisel data |
library(scRNAseq)
library(SingleCellExperiment)
library(scran)
library(scater)
library(Seurat)Warning in fun(libname, pkgname): rgeos: versions of GEOS runtime 3.10.3-CAPI-1.16.1
and GEOS at installation 3.9.1-CAPI-1.14.2differlibrary(tibble)
library(dplyr)
source(here::here("code", "data-analysis-utils.R"))zeisel <- ZeiselBrainData()snapshotDate(): 2021-10-19see ?scRNAseq and browseVignettes('scRNAseq') for documentationloading from cachesee ?scRNAseq and browseVignettes('scRNAseq') for documentationloading from cachesee ?scRNAseq and browseVignettes('scRNAseq') for documentationloading from cachesnapshotDate(): 2021-10-19see ?scRNAseq and browseVignettes('scRNAseq') for documentationloading from cachecolLabels(zeisel) <- zeisel$level1classtable(zeisel$tissue)
ca1hippocampus sscortex
1314 1691 table(zeisel$level1class)
astrocytes_ependymal endothelial-mural interneurons
224 235 290
microglia oligodendrocytes pyramidal CA1
98 820 939
pyramidal SS
399 zeisel <- logNormCounts(zeisel)zeisel <- runPCA(zeisel)
zeisel <- runTSNE(zeisel, dimred = "PCA")plotTSNE(zeisel, colour_by = "level1class")
plotTSNE(zeisel, colour_by = "level2class")
mgs <- findMarkers(zeisel, test.type = "wilcox", pval.type = "all")
oligo_mgs <- mgs$oligodendrocytes %>%
as.data.frame() %>%
rownames_to_column("gene") %>%
rowid_to_column() %>%
as_tibble() %>%
print(n = 20)# A tibble: 20,006 × 11
rowid gene p.value FDR summary.AUC AUC.astrocytes_… AUC.endothelial…
<int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Plp1 3.79e-58 3.17e-54 0.996 0.989 0.995
2 2 Sept4 4.59e-58 3.17e-54 0.996 0.979 0.957
3 3 Mbp 4.75e-58 3.17e-54 0.996 0.988 0.998
4 4 Cnp 7.28e-58 3.64e-54 0.995 0.984 0.997
5 5 Mobp 1.35e-57 5.39e-54 0.994 0.987 0.993
6 6 Cryab 3.76e-57 1.25e-53 0.992 0.965 0.984
7 7 Mog 4.37e-57 1.25e-53 0.991 0.988 0.993
8 8 Dbndd2 6.00e-57 1.50e-53 0.991 0.965 0.988
9 9 Ugt8a 8.91e-57 1.98e-53 0.990 0.983 0.993
10 10 Gsn 7.47e-55 1.50e-51 0.981 0.972 0.972
11 11 Taldo1 1.44e-54 2.62e-51 0.980 0.976 0.985
12 12 Tmem88b 4.68e-54 7.80e-51 0.978 0.956 0.968
13 13 Ermn 7.47e-54 1.15e-50 0.977 0.971 0.976
14 14 281046… 9.43e-54 1.35e-50 0.976 0.968 0.981
15 15 Tspan2 2.21e-52 2.95e-49 0.970 0.972 0.983
16 16 Cldn11 2.94e-52 3.67e-49 0.970 0.971 0.984
17 17 Hmgcs1 9.40e-52 1.11e-48 0.830 0.830 0.970
18 18 Qdpr 1.02e-51 1.14e-48 0.967 0.964 0.975
19 19 Sept7 1.91e-51 2.01e-48 0.966 0.909 0.902
20 20 Mag 2.15e-51 2.16e-48 0.965 0.958 0.978
# … with 19,986 more rows, and 4 more variables: AUC.interneurons <dbl>,
# AUC.microglia <dbl>, AUC.pyramidal.CA1 <dbl>, AUC.pyramidal.SS <dbl>oligo_mgs# A tibble: 20,006 × 11
rowid gene p.value FDR summary.AUC AUC.astrocytes_e… AUC.endothelial…
<int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Plp1 3.79e-58 3.17e-54 0.996 0.989 0.995
2 2 Sept4 4.59e-58 3.17e-54 0.996 0.979 0.957
3 3 Mbp 4.75e-58 3.17e-54 0.996 0.988 0.998
4 4 Cnp 7.28e-58 3.64e-54 0.995 0.984 0.997
5 5 Mobp 1.35e-57 5.39e-54 0.994 0.987 0.993
6 6 Cryab 3.76e-57 1.25e-53 0.992 0.965 0.984
7 7 Mog 4.37e-57 1.25e-53 0.991 0.988 0.993
8 8 Dbndd2 6.00e-57 1.50e-53 0.991 0.965 0.988
9 9 Ugt8a 8.91e-57 1.98e-53 0.990 0.983 0.993
10 10 Gsn 7.47e-55 1.50e-51 0.981 0.972 0.972
# … with 19,996 more rows, and 4 more variables: AUC.interneurons <dbl>,
# AUC.microglia <dbl>, AUC.pyramidal.CA1 <dbl>, AUC.pyramidal.SS <dbl>oligo_mgs %>%
filter(gene == "Olig2")# A tibble: 1 × 11
rowid gene p.value FDR summary.AUC AUC.astrocytes_epe… AUC.endothelial…
<int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 315 Olig2 7.19e-12 4.57e-10 0.694 0.667 0.726
# … with 4 more variables: AUC.interneurons <dbl>, AUC.microglia <dbl>,
# AUC.pyramidal.CA1 <dbl>, AUC.pyramidal.SS <dbl>plotExpression(zeisel, "Olig2", x = "level1class")
oligo_mgs %>%
filter(gene == "Ptgds")# A tibble: 1 × 11
rowid gene p.value FDR summary.AUC AUC.astrocytes_epe… AUC.endothelial…
<int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 146 Ptgds 3.40e-22 4.65e-20 0.796 0.844 0.854
# … with 4 more variables: AUC.interneurons <dbl>, AUC.microglia <dbl>,
# AUC.pyramidal.CA1 <dbl>, AUC.pyramidal.SS <dbl>zeisel <- readRDS(here::here("data", "real_data", "zeisel.rds"))plotTSNE(zeisel, colour_by = "label") 
plotUMAP(zeisel, colour_by = "label") 
plot_cluster_counts(zeisel)
plot_cluster_mean_boxplot(zeisel)
devtools::session_info()─ Session info ──────────────────────────────────────────────────────────────
hash: weary face, Mrs. Claus: light skin tone, man student: medium-light skin tone
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spatstat.sparse 3.0-1 2023-03-12 [1] CRAN (R 4.1.0)
spatstat.utils 3.0-3 2023-05-09 [1] CRAN (R 4.1.0)
statmod 1.4.36 2021-05-10 [2] CRAN (R 4.1.2)
stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.0)
stringr 1.4.0 2019-02-10 [2] CRAN (R 4.1.2)
SummarizedExperiment * 1.24.0 2021-10-26 [1] Bioconductor
survival 3.2-13 2021-08-24 [2] CRAN (R 4.1.2)
tensor 1.5 2012-05-05 [2] CRAN (R 4.1.2)
testthat 3.1.0 2021-10-04 [2] CRAN (R 4.1.2)
tibble * 3.1.7 2022-05-03 [1] CRAN (R 4.1.0)
tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.1.0)
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utf8 1.2.3 2023-01-31 [1] CRAN (R 4.1.0)
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viridis 0.6.2 2021-10-13 [1] CRAN (R 4.1.0)
viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.1.0)
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withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.0)
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zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
zoo 1.8-10 2022-04-15 [1] CRAN (R 4.1.0)
[1] /home/jpullin/R/x86_64-pc-linux-gnu-library/4.1
[2] /apps/easybuild-2022/easybuild/software/MPI/GCC/11.3.0/OpenMPI/4.1.4/R/4.1.2/lib64/R/library
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