Introduction to humanHippocampus2024
5 December 2024
Package
humanHippocampus2024 0.99.5
0.0.1 Introduction
Welcome to the humanHippocampus2024 package! In this vignettes, we are going
to show how to access the spatially-resolved transcriptomics (SRT) and
single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of
the anterior human hippocampus across ten adult neurotypical donors generated
by Lieber Institute for Brain Development (LIBD) researchers and collaborators.
0.0.2 Motivation
The main purpose to create R/Bioconductor package was to access the SRT and
snRNA-seq data from spatial_HPC project via an open-source and public
interface such that the data can be referenced or analyzed in other projects
conveniently.
0.0.3 Study Design
Experimental design to generate paired single-nucleus RNA-sequencing (snRNA-seq) and spatially-resolved transcriptomics (SRT) data in the human hippocampus. (A) Postmortem human tissue blocks from the anterior hippocampus were dissected from 10 adult neurotypical brain donors. Tissue blocks were scored and cryosectioned for snRNA-seq assays (red), and placement on Visium slides (Visium H&E, black; Visium Spatial Proteogenomics (SPG), yellow). (B) 10\(\mu\)m tissue sections from all ten donors were placed onto 2-5 capture areas to include the extent of the HPC(n=36 total capture areas), for measurement with the 10x Genomics Visium H&E platform. (C) 10\(\mu\)m tissue sections from two donors were placed onto 4 capture areas (n=8 total capture areas) for measurement with the 10x Genomics Visium-SPG platform. (D) Tissue sections (2-4 100\(\mu\)m cryosections per assay) from all ten donors were collected from the same tissue blocks for measurement with the 10x Genomics 3’ gene expression platform. For each donor, we sorted on both and PI+NeuN+ (n=26 total snRNA-seq libraries). (This figure was created with Biorender)
0.0.3.1 Data Access
All data, including raw FASTQ files and SpaceRanger/CellRanger processed data outputs, can be accessed via Gene Expression Omnibus (GEO) under accessions GSE264692 (SRT) and GSE264624 (snRNA-seq).
All R scripts created to perform analyses can be found here.
0.0.3.2 Contact
We value public questions, as they allow other users to learn from the answers. If you have any questions, please ask them at LieberInstitute/spatial_hpc/issues and refrain from emailing us. Thank you again for your interest in our work!
0.0.4 Package Tutorial
0.0.4.1 Installation
humanHippocampus2024 is an R package available via
Bioconductor repository for packages. GitHub
repository can be found
here.
Bioconductor version of 3.20 on R version of 4.4 is required.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("humanHippocampus2024")
## Check that you have a valid Bioconductor installation
BiocManager::valid()0.0.5 humanHippocampus2024 datasets
0.0.5.1 Load the packages
library(SummarizedExperiment)
library(SpatialExperiment)
library(humanHippocampus2024)0.0.5.2 Download datasets
## Connect to ExperimentHub
library(ExperimentHub)
#> Loading required package: AnnotationHub
#> Loading required package: BiocFileCache
#> Loading required package: dbplyr
#>
#> Attaching package: 'AnnotationHub'
#> The following object is masked from 'package:Biobase':
#>
#> cache
ehub <- ExperimentHub()
## Load the datasets of the package
myfiles <- query(ehub, "humanHippocampus2024")
## Resulting humanHippocampus2024 datasets from ExperimentHub query
myfiles
#> ExperimentHub with 2 records
#> # snapshotDate(): 2024-11-13
#> # $dataprovider: Lieber Institute for Brain Development (LIBD)
#> # $species: Homo sapiens
#> # $rdataclass: SpatialExperiment, SingleCellExperiment
#> # additional mcols(): taxonomyid, genome, description,
#> # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> # rdatapath, sourceurl, sourcetype
#> # retrieve records with, e.g., 'object[["EH9605"]]'
#>
#> title
#> EH9605 | spe
#> EH9606 | sce0.0.5.3 SRT dataset
SRT data in SpatialExperiment (spe) class was generated using 10x Genomics Visium (https://www.10xgenomics.com/products/spatial-gene-expression) (n=36 capture areas) and 10x Genomics Visium Spatial Proteogenomics (SPG) (https://www.10xgenomics.com/products/spatial-gene-and-protein-expression) (n=8 capture areas).
######################
# spe data
######################
# Downloading spatially-resolved transcriptomics data
# EH9605 | spe
spatial_hpc_spe <- myfiles[["EH9605"]]
#> see ?humanHippocampus2024 and browseVignettes('humanHippocampus2024') for documentation
#> downloading 1 resources
#> retrieving 1 resource
#> loading from cache
# This is a SpatialExperiment object
spatial_hpc_spe
#> class: SpatialExperiment
#> dim: 31483 150917
#> metadata(1): Obtained_from
#> assays(2): counts logcounts
#> rownames(31483): MIR1302-2HG AL627309.1 ... AC007325.4 AC007325.2
#> rowData names(7): source type ... gene_type gene_search
#> colnames(150917): AAACAACGAATAGTTC-1_V10B01-086_D1
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 ... TTGTTTCCATACAACT-1_Br2720_B1
#> TTGTTTGTATTACACG-1_Br2720_B1
#> colData names(150): sample_id in_tissue ... nmf99 nmf100
#> reducedDimNames(3): 10x_pca 10x_tsne 10x_umap
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
# Check sample info
head(colData(spatial_hpc_spe), 3)
#> DataFrame with 3 rows and 150 columns
#> sample_id in_tissue array_row array_col
#> <factor> <logical> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 V10B01-086_D1 TRUE 111 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 V10B01-086_D1 TRUE 25 50
#> AAACAATCTACTAGCA-1_V10B01-086_D1 V10B01-086_D1 TRUE 84 3
#> 10x_graphclust 10x_kmeans_10_clusters
#> <integer> <integer>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 2 3
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 1 2
#> AAACAATCTACTAGCA-1_V10B01-086_D1 2 3
#> 10x_kmeans_2_clusters 10x_kmeans_3_clusters
#> <integer> <integer>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 2 2
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 1 3
#> AAACAATCTACTAGCA-1_V10B01-086_D1 2 2
#> 10x_kmeans_4_clusters 10x_kmeans_5_clusters
#> <integer> <integer>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 2 2
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 3 3
#> AAACAATCTACTAGCA-1_V10B01-086_D1 2 2
#> 10x_kmeans_6_clusters 10x_kmeans_7_clusters
#> <integer> <integer>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 3 3
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 2 2
#> AAACAATCTACTAGCA-1_V10B01-086_D1 3 3
#> 10x_kmeans_8_clusters 10x_kmeans_9_clusters
#> <integer> <integer>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 2 3
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 3 2
#> AAACAATCTACTAGCA-1_V10B01-086_D1 2 3
#> key sum_umi sum_gene
#> <character> <numeric> <integer>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 AAACAACGAATAGTTC-1_V.. 8159 3633
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 AAACAAGTATCTCCCA-1_V.. 1450 954
#> AAACAATCTACTAGCA-1_V10B01-086_D1 AAACAATCTACTAGCA-1_V.. 5436 2622
#> expr_chrM expr_chrM_ratio ManualAnnotation
#> <numeric> <numeric> <character>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 403 0.0493933 NA
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 106 0.0731034 NA
#> AAACAATCTACTAGCA-1_V10B01-086_D1 555 0.1020971 NA
#> brnum dateImg experimenterImg slide
#> <factor> <Date> <factor> <factor>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 Br2743 2021-10-11 Stephanie Page V10B01-086
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 Br2743 2021-10-11 Stephanie Page V10B01-086
#> AAACAATCTACTAGCA-1_V10B01-086_D1 Br2743 2021-10-11 Stephanie Page V10B01-086
#> array position seqNum experimenterSeq
#> <factor> <factor> <factor> <factor>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 D1 TL 8v_scp Stephanie Page
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 D1 TL 8v_scp Stephanie Page
#> AAACAATCTACTAGCA-1_V10B01-086_D1 D1 TL 8v_scp Stephanie Page
#> dx race sex age
#> <character> <character> <character> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 Control CAUC M 61.54
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 Control CAUC M 61.54
#> AAACAATCTACTAGCA-1_V10B01-086_D1 Control CAUC M 61.54
#> pmi sum detected subsets_Mito_sum
#> <numeric> <numeric> <integer> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 21.5 8159 3633 403
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 21.5 1450 954 106
#> AAACAATCTACTAGCA-1_V10B01-086_D1 21.5 5436 2622 555
#> subsets_Mito_detected subsets_Mito_percent
#> <integer> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 12 4.93933
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 12 7.31034
#> AAACAATCTACTAGCA-1_V10B01-086_D1 13 10.20971
#> total low_sum_id low_sum_br
#> <numeric> <logical> <logical>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 8159 FALSE FALSE
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 1450 FALSE FALSE
#> AAACAATCTACTAGCA-1_V10B01-086_D1 5436 FALSE FALSE
#> low_detected_id low_detected_br
#> <logical> <logical>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 FALSE FALSE
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 FALSE FALSE
#> AAACAATCTACTAGCA-1_V10B01-086_D1 FALSE FALSE
#> discard_auto_br discard_auto_id sizeFactor
#> <logical> <logical> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 FALSE FALSE 2.400629
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 FALSE FALSE 0.426635
#> AAACAATCTACTAGCA-1_V10B01-086_D1 FALSE FALSE 1.599439
#> cluster neuron_cell_body domain
#> <factor> <logical> <factor>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 CA1.1 TRUE CA1
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 WM.2 FALSE WM.2
#> AAACAATCTACTAGCA-1_V10B01-086_D1 CA1.1 TRUE CA1
#> broad.domain nmf1 nmf2 nmf3
#> <factor> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 Neuron 0 NaN NaN
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 WM 0 NaN NaN
#> AAACAATCTACTAGCA-1_V10B01-086_D1 Neuron 0 NaN NaN
#> nmf4 nmf5 nmf6 nmf7
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 2.27395e-05 0 3.28587e-05 2.95829e-06
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 4.48045e-06 0 2.65465e-06 0.00000e+00
#> AAACAATCTACTAGCA-1_V10B01-086_D1 2.10045e-05 0 1.04418e-05 2.84576e-06
#> nmf8 nmf9 nmf10 nmf11
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf12 nmf13 nmf14 nmf15
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 1.22655e-05 3.52808e-05 0 1.08150e-04
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 4.33318e-06 2.06538e-05 0 0.00000e+00
#> AAACAATCTACTAGCA-1_V10B01-086_D1 1.09472e-05 2.54767e-05 0 8.50595e-05
#> nmf16 nmf17 nmf18 nmf19
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 NaN 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 NaN 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 NaN 0 0 0
#> nmf20 nmf21 nmf22 nmf23
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf24 nmf25 nmf26 nmf27
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf28 nmf29 nmf30 nmf31
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf32 nmf33 nmf34 nmf35
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0.00022619 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0.00000000 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0.00000000 0 0 0
#> nmf36 nmf37 nmf38 nmf39
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf40 nmf41 nmf42 nmf43
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0.00000e+00 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 2.69158e-05 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0.00000e+00 0
#> nmf44 nmf45 nmf46 nmf47
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0.00000e+00 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 2.97623e-05 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0.00000e+00 0 0 0
#> nmf48 nmf49 nmf50 nmf51
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 6.80418e-06 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 4.99429e-06 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 5.75757e-06 0 0 0
#> nmf52 nmf53 nmf54 nmf55
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 3.64089e-06
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 8.29104e-06
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 4.22433e-06
#> nmf56 nmf57 nmf58 nmf59
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0.00000000
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0.00018553
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0.00000000
#> nmf60 nmf61 nmf62 nmf63
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 1.74826e-05 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 5.27230e-06 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 1.64522e-05 0 0
#> nmf64 nmf65 nmf66 nmf67
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf68 nmf69 nmf70 nmf71
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf72 nmf73 nmf74 nmf75
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 1.03960e-05
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 6.35775e-06
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 1.08303e-05
#> nmf76 nmf77 nmf78 nmf79
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 4.89863e-07 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 1.52269e-05 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 1.49846e-06 0 0
#> nmf80 nmf81 nmf82 nmf83
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 2.18171e-05 9.56545e-07 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0.00000e+00 2.03628e-06 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 1.72909e-04 3.13879e-06 0 0
#> nmf84 nmf85 nmf86 nmf87
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf88 nmf89 nmf90 nmf91
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 1.39632e-06 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 1.97323e-06 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 3.43709e-06 0 0
#> nmf92 nmf93 nmf94 nmf95
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 4.08908e-06 0 0.00000e+00 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 5.38153e-06 0 4.48891e-05 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 5.54567e-06 0 0.00000e+00 0
#> nmf96 nmf97 nmf98 nmf99
#> <numeric> <numeric> <numeric> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0 0 0 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0 0 0 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0 0 0 0
#> nmf100
#> <numeric>
#> AAACAACGAATAGTTC-1_V10B01-086_D1 0
#> AAACAAGTATCTCCCA-1_V10B01-086_D1 0
#> AAACAATCTACTAGCA-1_V10B01-086_D1 0
# Check gene info
head(rowData(spatial_hpc_spe), 3)
#> DataFrame with 3 rows and 7 columns
#> source type gene_id gene_version gene_name
#> <factor> <factor> <character> <character> <character>
#> MIR1302-2HG HAVANA gene ENSG00000243485 5 MIR1302-2HG
#> AL627309.1 HAVANA gene ENSG00000238009 6 AL627309.1
#> AL627309.3 HAVANA gene ENSG00000239945 1 AL627309.3
#> gene_type gene_search
#> <character> <character>
#> MIR1302-2HG lncRNA MIR1302-2HG; ENSG000..
#> AL627309.1 lncRNA AL627309.1; ENSG0000..
#> AL627309.3 lncRNA AL627309.3; ENSG0000..
# Access the original counts
assays(spatial_hpc_spe)$counts[1:3, 1:3]
#> 3 x 3 sparse Matrix of class "dgCMatrix"
#> AAACAACGAATAGTTC-1_V10B01-086_D1 AAACAAGTATCTCCCA-1_V10B01-086_D1
#> MIR1302-2HG . .
#> AL627309.1 . .
#> AL627309.3 . .
#> AAACAATCTACTAGCA-1_V10B01-086_D1
#> MIR1302-2HG .
#> AL627309.1 .
#> AL627309.3 .
# Access the log-normalized counts
assays(spatial_hpc_spe)$logcounts[1:3, 1:3]
#> 3 x 3 sparse Matrix of class "dgCMatrix"
#> AAACAACGAATAGTTC-1_V10B01-086_D1 AAACAAGTATCTCCCA-1_V10B01-086_D1
#> MIR1302-2HG . .
#> AL627309.1 . .
#> AL627309.3 . .
#> AAACAATCTACTAGCA-1_V10B01-086_D1
#> MIR1302-2HG .
#> AL627309.1 .
#> AL627309.3 .
# Access the reduced dimension methods
reducedDimNames(spatial_hpc_spe)
#> [1] "10x_pca" "10x_tsne" "10x_umap"
# Access the spatial coordinates
spatialCoordsNames(spatial_hpc_spe)
#> [1] "pxl_col_in_fullres" "pxl_row_in_fullres"
rm(spatial_hpc_spe)0.0.5.4 snRNA-seq dataset
snRNA-seq data in SingleCellExperiment (sce) class was generated using 10x Genomics Chromium (https://www.10xgenomics.com/products/single-cell-gene-expression) (n=26 total snRNA-seq libraries).
######################
# sce data
######################
# Downloading single-nucleus RNA sequencing data
# EH9606 | sce
spatial_hpc_snrna_seq <- myfiles[["EH9606"]]
#> see ?humanHippocampus2024 and browseVignettes('humanHippocampus2024') for documentation
#> downloading 1 resources
#> retrieving 1 resource
#> loading from cache
# This is a SingleCellExperiment object
spatial_hpc_snrna_seq
#> class: SingleCellExperiment
#> dim: 36601 75411
#> metadata(1): Obtained_from
#> assays(2): counts logcounts
#> rownames(36601): TTR MALAT1 ... AC133551.1 AC141272.1
#> rowData names(7): source type ... gene_type poisson_deviance
#> colnames(75411): 1_AAACCCACAACGATCT-1 1_AAACGAAAGGTGAGCT-1 ...
#> 26_TTTGTTGGTGATTGGG-1 26_TTTGTTGGTTCAAAGA-1
#> colData names(132): Sample Barcode ... sex pmi
#> reducedDimNames(3): PCA MNN UMAP
#> mainExpName: NULL
#> altExpNames(0):
# Check sample info
head(colData(spatial_hpc_snrna_seq),3)
#> DataFrame with 3 rows and 132 columns
#> Sample Barcode key sum
#> <factor> <character> <character> <numeric>
#> 1_AAACCCACAACGATCT-1 1c-scp AAACCCACAACGATCT-1 AAACCCACAACGATCT-1_ 1583
#> 1_AAACGAAAGGTGAGCT-1 1c-scp AAACGAAAGGTGAGCT-1 AAACGAAAGGTGAGCT-1_ 3939
#> 1_AAACGAACACAAAGTA-1 1c-scp AAACGAACACAAAGTA-1 AAACGAACACAAAGTA-1_ 2398
#> detected subsets_Mito_sum subsets_Mito_detected
#> <integer> <numeric> <integer>
#> 1_AAACCCACAACGATCT-1 1062 16 9
#> 1_AAACGAAAGGTGAGCT-1 2133 7 6
#> 1_AAACGAACACAAAGTA-1 1401 14 7
#> subsets_Mito_percent total high_mito low_lib
#> <numeric> <numeric> <logical> <logical>
#> 1_AAACCCACAACGATCT-1 1.01074 1583 FALSE FALSE
#> 1_AAACGAAAGGTGAGCT-1 0.17771 3939 FALSE FALSE
#> 1_AAACGAACACAAAGTA-1 0.58382 2398 FALSE FALSE
#> low_genes discard_auto discard_semiauto doubletScore
#> <logical> <logical> <logical> <numeric>
#> 1_AAACCCACAACGATCT-1 FALSE FALSE FALSE 0.066584
#> 1_AAACGAAAGGTGAGCT-1 FALSE FALSE FALSE 0.076096
#> 1_AAACGAACACAAAGTA-1 FALSE FALSE FALSE 0.009512
#> discard brnum round sort sizeFactor logDoublet
#> <logical> <factor> <factor> <factor> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 FALSE Br8325 0 PI 0.0685330 -3.90868
#> 1_AAACGAAAGGTGAGCT-1 FALSE Br8325 0 PI 0.2037986 -3.71604
#> 1_AAACGAACACAAAGTA-1 FALSE Br8325 0 PI 0.0976826 -6.71604
#> k_5_louvain_initial k_5_louvain discard2 cell.type2
#> <factor> <factor> <logical> <factor>
#> 1_AAACCCACAACGATCT-1 1 1 FALSE Oligo
#> 1_AAACGAAAGGTGAGCT-1 2 2 FALSE Micro/Macro/T
#> 1_AAACGAACACAAAGTA-1 1 1 FALSE Oligo
#> nmf1 nmf2 nmf3 nmf4 nmf5
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0.00000e+00 0.00000e+00 1.89191e-06 0.00000e+00 0
#> 1_AAACGAAAGGTGAGCT-1 1.21532e-05 0.00000e+00 2.29324e-05 0.00000e+00 0
#> 1_AAACGAACACAAAGTA-1 0.00000e+00 5.00542e-07 2.31828e-06 4.34041e-06 0
#> nmf6 nmf7 nmf8 nmf9 nmf10
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0.00000e+00 0 5.29354e-06 0
#> 1_AAACGAAAGGTGAGCT-1 0 0.00000e+00 0 3.47221e-06 0
#> 1_AAACGAACACAAAGTA-1 0 2.17797e-06 0 2.49583e-05 0
#> nmf11 nmf12 nmf13 nmf14 nmf15
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0.00000e+00 1.54408e-06 0 0
#> 1_AAACGAAAGGTGAGCT-1 0 1.00794e-06 0.00000e+00 0 0
#> 1_AAACGAACACAAAGTA-1 0 0.00000e+00 0.00000e+00 0 0
#> nmf16 nmf17 nmf18 nmf19 nmf20
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0 0 0 0
#> 1_AAACGAAAGGTGAGCT-1 0 0 0 0 0
#> 1_AAACGAACACAAAGTA-1 0 0 0 0 0
#> nmf21 nmf22 nmf23 nmf24 nmf25
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0 0 4.29416e-05 0.00000e+00
#> 1_AAACGAAAGGTGAGCT-1 0 0 0 0.00000e+00 2.44844e-06
#> 1_AAACGAACACAAAGTA-1 0 0 0 1.62841e-05 6.50203e-06
#> nmf26 nmf27 nmf28 nmf29 nmf30
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0 0.00000e+00 0 0
#> 1_AAACGAAAGGTGAGCT-1 0 0 2.00962e-06 0 0
#> 1_AAACGAACACAAAGTA-1 0 0 0.00000e+00 0 0
#> nmf31 nmf32 nmf33 nmf34 nmf35
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 2.65198e-06 0 0.00000000 0.00000e+00 0
#> 1_AAACGAAAGGTGAGCT-1 0.00000e+00 0 0.00000000 0.00000e+00 0
#> 1_AAACGAACACAAAGTA-1 7.15550e-06 0 0.00012796 9.60702e-07 0
#> nmf36 nmf37 nmf38 nmf39 nmf40
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0.00000e+00 2.33231e-05 2.03929e-04 0.000000000 0
#> 1_AAACGAAAGGTGAGCT-1 8.18709e-06 2.76864e-05 0.00000e+00 0.000102191 0
#> 1_AAACGAACACAAAGTA-1 0.00000e+00 2.81777e-05 8.06468e-05 0.000000000 0
#> nmf41 nmf42 nmf43 nmf44 nmf45
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 1.02996e-04 0 0.000145559 0
#> 1_AAACGAAAGGTGAGCT-1 0 9.34782e-06 0 0.000000000 0
#> 1_AAACGAACACAAAGTA-1 0 0.00000e+00 0 0.000300507 0
#> nmf46 nmf47 nmf48 nmf49 nmf50
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0 0 0.00000e+00 0
#> 1_AAACGAAAGGTGAGCT-1 0 0 0 2.14158e-06 0
#> 1_AAACGAACACAAAGTA-1 0 0 0 3.97464e-06 0
#> nmf51 nmf52 nmf53 nmf54 nmf55
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0 0 0 8.10701e-06
#> 1_AAACGAAAGGTGAGCT-1 0 0 0 0 1.38251e-05
#> 1_AAACGAACACAAAGTA-1 0 0 0 0 0.00000e+00
#> nmf56 nmf57 nmf58 nmf59 nmf60
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 1.30526e-05 7.35371e-06 0.00000e+00 9.78114e-08 0
#> 1_AAACGAAAGGTGAGCT-1 4.79797e-06 1.47243e-06 5.15497e-06 0.00000e+00 0
#> 1_AAACGAACACAAAGTA-1 1.80609e-05 1.03655e-05 1.57277e-06 0.00000e+00 0
#> nmf61 nmf62 nmf63 nmf64 nmf65
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0 0 0 0
#> 1_AAACGAAAGGTGAGCT-1 0 0 0 0 0
#> 1_AAACGAACACAAAGTA-1 0 0 0 0 0
#> nmf66 nmf67 nmf68 nmf69 nmf70
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0.00000e+00 0 0 0 0
#> 1_AAACGAAAGGTGAGCT-1 2.59003e-07 0 0 0 0
#> 1_AAACGAACACAAAGTA-1 0.00000e+00 0 0 0 0
#> nmf71 nmf72 nmf73 nmf74 nmf75
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 1.29844e-05 4.28539e-06 0.00000e+00 0 3.74648e-05
#> 1_AAACGAAAGGTGAGCT-1 2.82051e-06 2.56282e-06 2.46759e-06 0 3.67568e-05
#> 1_AAACGAACACAAAGTA-1 2.46076e-06 7.01058e-06 5.40694e-07 0 2.11332e-05
#> nmf76 nmf77 nmf78 nmf79 nmf80
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0.00000e+00 0 0 1.17572e-06
#> 1_AAACGAAAGGTGAGCT-1 0 1.70319e-05 0 0 1.35350e-06
#> 1_AAACGAACACAAAGTA-1 0 4.96790e-05 0 0 0.00000e+00
#> nmf81 nmf82 nmf83 nmf84 nmf85
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0.0000e+00 0 0 1.51082e-06
#> 1_AAACGAAAGGTGAGCT-1 0 2.8178e-04 0 0 0.00000e+00
#> 1_AAACGAACACAAAGTA-1 0 8.0724e-06 0 0 0.00000e+00
#> nmf86 nmf87 nmf88 nmf89 nmf90
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 0 0 0.00000e+00 0.000000000
#> 1_AAACGAAAGGTGAGCT-1 0 0 0 7.13175e-07 0.000761411
#> 1_AAACGAACACAAAGTA-1 0 0 0 0.00000e+00 0.000000000
#> nmf91 nmf92 nmf93 nmf94 nmf95
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0 3.27635e-05 0 2.96047e-05 0
#> 1_AAACGAAAGGTGAGCT-1 0 5.68209e-06 0 1.85584e-06 0
#> 1_AAACGAACACAAAGTA-1 0 1.71284e-05 0 3.50191e-05 0
#> nmf96 nmf97 nmf98 nmf99 nmf100
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1_AAACCCACAACGATCT-1 0.00000e+00 0 0.000000000 0.0000e+00 0
#> 1_AAACGAAAGGTGAGCT-1 7.60918e-05 0 0.000141359 5.4575e-06 0
#> 1_AAACGAACACAAAGTA-1 0.00000e+00 0 0.000000000 0.0000e+00 0
#> fine.cell.class mid.cell.class broad.cell.class
#> <factor> <factor> <factor>
#> 1_AAACCCACAACGATCT-1 Oligo Oligo Oligo
#> 1_AAACGAAAGGTGAGCT-1 Micro/Macro/T Micro/Macro/T Micro
#> 1_AAACGAACACAAAGTA-1 Oligo Oligo Oligo
#> superfine.cell.class age sex pmi
#> <factor> <numeric> <character> <numeric>
#> 1_AAACCCACAACGATCT-1 Oligo.1 57.62 F 29
#> 1_AAACGAAAGGTGAGCT-1 Micro.1 57.62 F 29
#> 1_AAACGAACACAAAGTA-1 Oligo.1 57.62 F 29
# Check gene info
head(rowData(spatial_hpc_snrna_seq), 3)
#> DataFrame with 3 rows and 7 columns
#> source type gene_id gene_version gene_name
#> <factor> <factor> <character> <character> <character>
#> TTR HAVANA gene ENSG00000118271 10 TTR
#> MALAT1 HAVANA gene ENSG00000251562 8 MALAT1
#> DPP10 HAVANA gene ENSG00000175497 16 DPP10
#> gene_type poisson_deviance
#> <character> <numeric>
#> TTR protein_coding 15438503
#> MALAT1 lncRNA 10289034
#> DPP10 protein_coding 8757887
# Access the original counts
assays(spatial_hpc_snrna_seq)$counts[1:3, 1:3]
#> 3 x 3 sparse Matrix of class "dgCMatrix"
#> 1_AAACCCACAACGATCT-1 1_AAACGAAAGGTGAGCT-1 1_AAACGAACACAAAGTA-1
#> TTR 1 . 2
#> MALAT1 33 83 49
#> DPP10 . . .
# Access the log-normalized counts
assays(spatial_hpc_snrna_seq)$logcounts[1:3, 1:3]
#> 3 x 3 sparse Matrix of class "dgCMatrix"
#> 1_AAACCCACAACGATCT-1 1_AAACGAAAGGTGAGCT-1 1_AAACGAACACAAAGTA-1
#> TTR 3.962689 . 4.424551
#> MALAT1 8.914445 8.673362 8.973338
#> DPP10 . . .
# Access the reduced dimension methods
reducedDimNames(spatial_hpc_snrna_seq)
#> [1] "PCA" "MNN" "UMAP"0.0.6 Citation
citation("humanHippocampus2024")
#> To cite package 'humanHippocampus2024' in publications use:
#>
#> Hou C (2024). _Access to spatial HPC project data_.
#> doi:10.18129/B9.bioc.humanHippocampus2024
#> <https://doi.org/10.18129/B9.bioc.humanHippocampus2024>,
#> https://github.com/christinehou11/humanHippocampus2024/humanHippocampus2024
#> - R package version 0.99.5,
#> <http://www.bioconductor.org/packages/humanHippocampus2024>.
#>
#> Nelson ED, Tippani M, Ramnauth AD, Divecha HR, Miller RA, Eagles NJ,
#> Pattie EA, Kwon SH, Bach SV, Kaipa UM, Yao J, Kleinman JE,
#> Collado-Torres L, Han S, Maynard KR, Hyde TM, Martinowich K, Page SC,
#> Hicks SC (2024). "An integrated single-nucleus and spatial
#> transcriptomics atlas reveals the molecular landscape of the human
#> hippocampus." _bioRxiv_. doi:10.1101/2024.04.26.590643
#> <https://doi.org/10.1101/2024.04.26.590643>,
#> <https://www.biorxiv.org/content/10.1101/2024.04.26.590643v1>.
#>
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.0.0.7 Reproducibility
This package was developed using biocthis
Date the vignette was generated.
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────0.0.8 Bibliography
This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
[1] J. Allaire, Y. Xie, C. Dervieux, et al. rmarkdown: Dynamic Documents for R. R package version 2.29. 2024. URL: https://github.com/rstudio/rmarkdown.
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[3] A. Oleś. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.35.0. 2024. DOI: 10.18129/B9.bioc.BiocStyle. URL: https://bioconductor.org/packages/BiocStyle.
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