Human spine data overview
Chih-Hsuan Wu
Last updated: 2024-10-19
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Knit directory: DEanalysis/
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Data introduction
For this case study, we are analyzing an scRNA-seq dataset that comprises human spine cells. The dataset consists of a total of 48644 cells contributed by 9 donors. After removing ambiguous genes and only keeping common genes across all samples, it includes sequencing data for 8092 genes.
In this project, we utilize four distinct counts as inputs.
Raw data: This refers to the raw counts without any normalization or adjustments applied.
Seurat normalized data: This data type represents the counts from each input sample after normalization using the ‘Seurat::NormalizeData(x, normalization.method = “LogNormalize”, scale.factor = 10000)’ function. This normalization method helps to account for differences in library sizes between samples and scales the data by a factor of 10,000.
Integrated data (Only for clustering result): The integrated data refers to the normalized counts after removing batch effects (Seurat v5 integration workflow). The workflow generates an integrated dimensional reduction embedding which can be used as input for clustering.
CPM data: CPM stands for Counts Per Million. This data is obtained by dividing the counts by the sum of library counts and multiplying the result by a million. The CPM values provide a normalized representation of the expression levels, allowing for meaningful comparisons between samples while accounting for differences in library sizes.
VST data: VST data (variance stabilizing transformation) is computed via the sctransform package (Hafemeister and Satija 2019), which returns Pearson residuals from a regularized negative binomial regression model that can be interpreted as normalized expression values.
Library size
UMAP
Variation of a single gene on different level
R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Monterey 12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] scater_1.32.1 scuttle_1.14.0
[3] Seurat_5.1.0 SeuratObject_5.0.2
[5] sp_2.1-4 SingleCellExperiment_1.26.0
[7] SummarizedExperiment_1.34.0 Biobase_2.64.0
[9] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[11] IRanges_2.38.1 S4Vectors_0.42.1
[13] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
[15] matrixStats_1.4.1 dplyr_1.1.4
[17] ggplot2_3.5.1
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[7] farver_2.1.2 rmarkdown_2.28
[9] zlibbioc_1.50.0 fs_1.6.4
[11] vctrs_0.6.5 ROCR_1.0-11
[13] DelayedMatrixStats_1.26.0 spatstat.explore_3.3-2
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[17] BiocNeighbors_1.22.0 SparseArray_1.4.8
[19] sass_0.4.9 sctransform_0.4.1
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[33] lifecycle_1.0.4 pkgconfig_2.0.3
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