Introduction to smokingMouse

Daianna Gonzalez-Padilla

Lieber Institute for Brain Development (LIBD)

30 July 2024

Source: vignettes/smokingMouse.Rmd

Introduction

Welcome to the smokingMouse project. In this vignette we’ll show you how to access the smoking-nicotine-mouse LIBD datasets. You can find the analysis code and the data generation in here.

Motivation

The main motivation to create this Bioconductor package was to provide public and free access to all RNA-seq datasets that were generated for the smoking-nicotine-mouse project, containing variables of interest that make it possible to answer a wide range of biological questions related to smoking and nicotine effects in mice.

Overview

This bulk RNA-sequencing project consisted of a differential expression analysis (DEA) involving 4 data types: genes, transcripts, exons, and exon-exon junctions. The main goal of this study was to explore the effects of prenatal exposure to smoking and nicotine on the developing mouse brain. As secondary objectives, this work evaluated: 1) the affected genes by each exposure in the adult female brain in order to compare offspring and adult results, and 2) the effects of smoking on adult blood and brain to search for overlapping biomarkers in both tissues. Finally, DEGs identified in mice were compared against previously published results in human (Semick et al., 2020 and Toikumo et al., 2023).

Study design

Experimental design of the study. A) 21 pregnant mice were split into two experiments: in the first one prenatal nicotine exposure (PNE) was modeled administering nicotine (n=3) or vehicle (n=3) to the dams during gestation, and in the second maternal smoking during pregnancy (MSDP) was modeled exposing dams to cigarette smoke during gestation (n=8) or using them as controls (n=7). A total of 137 pups were born: 19 were born to nicotine-administered mice, 23 to vehicle-administered mice, 46 to smoking-exposed mice, and 49 to smoking control mice. 17 nonpregnant adult females were also nicotine-administered (n=9) or vehicle-administered (n=8) to model adult nicotine exposure, and 9 additional nonpregnant dams were smoking-exposed (n=4) or controls (n=5) to model adult smoking. Frontal cortex samples of all P0 pups (n=137: 42 for PNE and 95 for MSDP) and adults (n=47: 23 for the nicotine experiment and 24 for the smoking experiment) were obtained, as well as blood samples from the smoking-exposed and smoking control adults (n=24), totaling 208 samples. Number of donors and samples are indicated in the figure. B) RNA was extracted from such samples and bulk RNA-seq experiments were performed, obtaining expression counts for genes, transcripts, exons, and exon-exon junctions.

Workflow

The next table summarizes the analyses done at each feature level.

Summary of analysis steps across gene expression feature levels:

1. Data processing: counts of genes, exons, and exon-exon junctions were normalized to CPM and log2-transformed; transcript expression values were only log2-transformed since they were already in TPM. Lowly-expressed features were removed using the indicated functions and samples were separated by tissue and age in order to create subsets of the data for downstream analyses.

2. Exploratory Data Analysis (EDA): QC metrics of the samples were examined and used to filter the poor quality ones. Sample level effects were explored through dimensionality reduction methods and segregated samples in PCA plots were removed from the datasets. Gene level effects were evaluated with analyses of variance partition.

3. Differential Expression Analysis (DEA): with the relevant variables identified in the previous steps, the DEA was performed at the gene level for nicotine and smoking exposure in adult and pup brain samples, and for smoking exposure in adult blood samples; DEA at the rest of the levels was performed for both exposures in pup brain only. DE signals of the genes in the different conditions, ages, tissues, and species (using human results from 1^1Semick et al., 2020) were contrasted, as well as the DE signals of exons and transcripts vs those of their genes. Mean expression of DEGs and non-DEGs genes with and without DE features was also analyzed. Then, all resultant DEGs and DE features (and their genes) were compared by direction of regulation (up or down) between and within exposures (nicotine/smoking); mouse DEGs were also compared against human genes associated with TUD from 2^2Toikumo et al., 2023.

4. Functional Enrichment Analysis: GO & KEGG terms significantly enriched in the clusters of DEGs and genes of DE transcripts and exons were obtained.

5. DGE visualization: the log2-normalized expression of DEGs was represented in heat maps in order to distinguish the groups of up- and down-regulated genes.

6. Novel junction gene annotation: for uncharacterized DE junctions with no annotated gene, their nearest, preceding, and following genes were determined.

Abbreviations: Jxn: junction; Tx(s): transcript(s); CPM: counts per million; TPM: transcripts per million; TMM: Trimmed Mean of M-Values; TMMwsp: TMM with singleton pairing; QC: quality control; PC: principal component; DEA: differential expression analysis; DE: differential expression/differentially expressed; FC: fold-change; FDR: false discovery rate; DEGs: differentially expressed genes; TUD: tobacco use disorder; DGE: differential gene expression.

All R scripts created to perform such analyses can be found in code on GitHub.

Basics

Install smokingMouse

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. smokingMouse is an R package available via the Bioconductor repository for packages. R can be installed on any operating system from CRAN after which you can install smokingMouse by using the following commands in your R session:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("smokingMouse")

## Check that you have a valid Bioconductor installation
BiocManager::valid()

Required knowledge

smokingMouse is based on many other packages, in particular those that have implemented the infrastructure needed for dealing with RNA-seq data and differential expression. That is, packages such as SummarizedExperiment and limma.

If you are asking yourself the question “Where do I start using Bioconductor?” you might be interested in this blog post.

Asking for help

As package developers, we try to explain clearly how to use our packages and in which order to use the functions. But R and Bioconductor have a steep learning curve so it is critical to learn where to ask for help. The blog post quoted above mentions some but we would like to highlight the Bioconductor support site as the main resource for getting help: remember to use the smokingMouse tag and check the older posts. Other alternatives are available such as creating GitHub issues and tweeting. However, please note that if you want to receive help you should adhere to the posting guidelines. It is particularly critical that you provide a small reproducible example and your session information so package developers can track down the source of the error.

Citing smokingMouse

We hope that smokingMouse will be useful for your research. Please use the following information to cite the package and the overall approach.

## Citation info
citation("smokingMouse")
#> To cite package 'smokingMouse' in publications use:
#> 
#>   Gonzalez-Padilla D, Collado-Torres L (2024). _Provides access to
#>   smokingMouse project data_. doi:10.18129/B9.bioc.smokingMouse
#>   <https://doi.org/10.18129/B9.bioc.smokingMouse>,
#>   https://github.com/LieberInstitute/smokingMouse/smokingMouse - R
#>   package version 0.99.91,
#>   <http://www.bioconductor.org/packages/smokingMouse>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {Provides access to smokingMouse project data},
#>     author = {Daianna Gonzalez-Padilla and Leonardo Collado-Torres},
#>     year = {2024},
#>     url = {http://www.bioconductor.org/packages/smokingMouse},
#>     note = {https://github.com/LieberInstitute/smokingMouse/smokingMouse - R package version 0.99.91},
#>     doi = {10.18129/B9.bioc.smokingMouse},
#>   }

Quick start to using smokingMouse

To get started, please load the smokingMouse package.

smokingMouse datasets

The raw expression data were generated by LIBD researchers and are composed of read counts of genes, exons, and exon-exon junctions (jxns), and transcripts-per-million (TPM) of transcripts (txs), across the 208 mice samples (from brain/blood; adults/pups; nicotine-exposed/smoking-exposed/controls). The datasets available in this package were generated by Daianna Gonzalez-Padilla. The human data were generated in Semick et al., 2018 and contain the results of a DEA in adult and prenatal human brain samples exposed to cigarette smoke.

Description of the datasets

Mouse datasets:

  • They are 4 RangedSummarizedExperiment (RSE) objects that contain feature info in rowData(RSE) and sample info in colData(RSE).
  • Raw expression counts (and TPM for txs) can be accessed with assays(RSE)$counts and the log-transformed data (log2(CPM + 0.5) for genes, exons, and jxns, and log2(TPM + 0.5) for txs) with assays(RSE)$logcounts.

Human datasets:

  • They are two data frames with the DE statistics of human genes for cigarette smoke exposure in prenatal and adult human cortical tissue.

Data specifics

  • ‘rse_gene_mouse_RNAseq_nic-smo.Rdata’: (rse_gene object) the gene RSE object contains the raw and log-normalized expression data of 55,401 mouse genes across the 208 samples from brain and blood of control and nicotine/smoking-exposed pup and adult mice.
  • ‘rse_tx_mouse_RNAseq_nic-smo.Rdata’: (rse_tx object) the tx RSE object contains the raw and log-scaled expression data of 142,604 mouse transcripts across the 208 samples from brain and blood of control and nicotine/smoking-exposed pup and adult mice.
  • ‘rse_exon_mouse_RNAseq_nic-smo.Rdata’: (rse_exon object) the exon RSE object contains the raw and log-normalized expression data of 447,670 mouse exons across the 208 samples from brain and blood of control and nicotine/smoking-exposed pup and adult mice.
  • ‘rse_jx_mouse_RNAseq_nic-smo.Rdata’: (rse_jx object) the jx RSE object contains the raw and log-normalized expression data of 1,436,068 mouse exon-exon junctions across the 208 samples from brain and blood of control and nicotine/smoking-exposed pup and adult mice.

All the above datasets contain the sample and feature metadata and additional data of the results obtained in the filtering steps and the DEA.

  • ‘de_genes_prenatal_human_brain_smoking.Rdata’: (de_genes_prenatal_human_brain_smoking object) data frame with DE statistics of 18,067 human genes for cigarette smoke exposure in prenatal human cortical tissue.
  • ‘de_genes_adult_human_brain_smoking.Rdata’: (de_genes_adult_human_brain_smoking object) data frame with DE statistics of 18,067 human genes for cigarette smoke exposure in adult human cortical tissue.

Variables of mice data

Feature information in rowData(RSE) contains the following variables:

  • retained_after_feature_filtering: Boolean variable that equals TRUE if the feature passed the feature filtering based on expression levels and FALSE if not. Check code for the feature filtering analysis in here.
  • DE_in_adult_brain_nicotine: Boolean variable that equals TRUE if the feature is differentially expressed (DE) for nicotine vs vehicle administration in adult brain and FALSE if not. Check code for the DEA in here.
  • DE_in_adult_brain_smoking: Boolean variable that equals TRUE if the feature is differentially expressed (DE) for smoking exposure vs control in adult brain and FALSE if not. Check code for the DEA in here.
  • DE_in_adult_blood_smoking: Boolean variable that equals TRUE if the feature is differentially expressed (DE) for smoking exposure vs control in adult blood and FALSE if not. Check code for the DEA in here.
  • DE_in_pup_brain_nicotine: Boolean variable that equals TRUE if the feature is differentially expressed (DE) for nicotine vs vehicle exposure in pup brain and FALSE if not. Check code for the DEA in here.
  • DE_in_pup_brain_smoking: Boolean variable that equals TRUE if the feature is differentially expressed (DE) for smoking exposure vs control in pup brain and FALSE if not. Check code for the DEA in here.

The rest of the variables are outputs of the SPEAQeasy pipeline (Eagles et al., 2021). See here for their description.

Sample information in colData(RSE) contains the following variables:

  • The Quality Control (QC) variables sum,detected,subsets_Mito_sum, subsets_Mito_detected, subsets_Mito_percent, subsets_Ribo_sum,subsets_Ribo_detected, and subsets_Ribo_percent are returned by addPerCellQC() from scuttle. See here for more details.
  • retained_after_QC_sample_filtering: Boolean variable that equals TRUE if the sample passed the sample filtering based on QC metrics and FALSE if not. Check code for QC-based sample filtering in here.
  • retained_after_manual_sample_filtering: Boolean variable that equals TRUE if the sample passed the manual sample filtering based on PCA plots and FALSE if not. Check code for PCA-based sample filtering in here

The rest of the variables are outputs of SPEAQeasy. See their description here.

Variables of human data

Check Smoking_DLPFC_Devel code for human data generation and limma documentation for the description of differential expression statistics.

Downloading the data with smokingMouse

Using smokingMouse you can download these R objects. They are hosted by Bioconductor’s ExperimentHub (Morgan and Shepherd, 2024) resource. Below you can see how to obtain these objects.

## Load ExperimentHub for downloading the data
library("ExperimentHub")
#> Loading required package: BiocGenerics
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
#>     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
#>     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
#>     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     Position, rank, rbind, Reduce, rownames, sapply, setdiff, table,
#>     tapply, union, unique, unsplit, which.max, which.min
#> Loading required package: AnnotationHub
#> Loading required package: BiocFileCache
#> Loading required package: dbplyr

## Connect to ExperimentHub
ehub <- ExperimentHub::ExperimentHub()

## Load the datasets of the package
myfiles <- query(ehub, "smokingMouse")

## Resulting smokingMouse files from our ExperimentHub query
myfiles
#> ExperimentHub with 6 records
#> # snapshotDate(): 2024-04-29
#> # $dataprovider: Lieber Institute for Brain Development (LIBD)
#> # $species: Mus musculus, Homo sapiens
#> # $rdataclass: RangedSummarizedExperiment, GenomicRanges
#> # additional mcols(): taxonomyid, genome, description,
#> #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
#> #   rdatapath, sourceurl, sourcetype 
#> # retrieve records with, e.g., 'object[["EH8313"]]' 
#> 
#>            title                                
#>   EH8313 | rse_gene_mouse_RNAseq_nic-smo        
#>   EH8314 | rse_tx_mouse_RNAseq_nic-smo          
#>   EH8315 | rse_jx_mouse_RNAseq_nic-smo          
#>   EH8316 | rse_exon_mouse_RNAseq_nic-smo        
#>   EH8317 | de_genes_prenatal_human_brain_smoking
#>   EH8318 | de_genes_adult_human_brain_smoking
## Load SummarizedExperiment which defines the class container for the data
library("SummarizedExperiment")
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#> 
#> Attaching package: 'MatrixGenerics'
#> The following objects are masked from 'package:matrixStats':
#> 
#>     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#>     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#>     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#>     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#>     colWeightedMeans, colWeightedMedians, colWeightedSds,
#>     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#>     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#>     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#>     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: S4Vectors
#> 
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:utils':
#> 
#>     findMatches
#> The following objects are masked from 'package:base':
#> 
#>     expand.grid, I, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> 
#> Attaching package: 'Biobase'
#> The following object is masked from 'package:MatrixGenerics':
#> 
#>     rowMedians
#> The following objects are masked from 'package:matrixStats':
#> 
#>     anyMissing, rowMedians
#> The following object is masked from 'package:ExperimentHub':
#> 
#>     cache
#> The following object is masked from 'package:AnnotationHub':
#> 
#>     cache

######################
#     Mouse data 
######################
myfiles["EH8313"]
#> ExperimentHub with 1 record
#> # snapshotDate(): 2024-04-29
#> # names(): EH8313
#> # package(): smokingMouse
#> # $dataprovider: Lieber Institute for Brain Development (LIBD)
#> # $species: Mus musculus
#> # $rdataclass: RangedSummarizedExperiment
#> # $rdatadateadded: 2023-07-21
#> # $title: rse_gene_mouse_RNAseq_nic-smo
#> # $description: RangedSummarizedExperiment of bulk RNA-seq data from mouse b...
#> # $taxonomyid: 10090
#> # $genome: GRCm38
#> # $sourcetype: GTF
#> # $sourceurl: https://bioconductor.org/packages/smokingMouse
#> # $sourcesize: NA
#> # $tags: c("ExpressionData", "LIBD", "mouse", "Mus_musculus_Data",
#> #   "nicotine", "RNASeqData", "smoking") 
#> # retrieve record with 'object[["EH8313"]]'

## Download the mouse gene data
#  EH8313 | rse_gene_mouse_RNAseq_nic-smo
rse_gene <- myfiles[["EH8313"]]
#> see ?smokingMouse and browseVignettes('smokingMouse') for documentation
#> loading from cache

## This is a RangedSummarizedExperiment object
rse_gene
#> class: RangedSummarizedExperiment 
#> dim: 55401 208 
#> metadata(1): Obtained_from
#> assays(2): counts logcounts
#> rownames(55401): ENSMUSG00000102693.1 ENSMUSG00000064842.1 ...
#>   ENSMUSG00000064371.1 ENSMUSG00000064372.1
#> rowData names(13): Length gencodeID ... DE_in_pup_brain_nicotine
#>   DE_in_pup_brain_smoking
#> colnames: NULL
#> colData names(71): SAMPLE_ID FQCbasicStats ...
#>   retained_after_QC_sample_filtering
#>   retained_after_manual_sample_filtering

## Optionally check the memory size
# lobstr::obj_size(rse_gene)
# 159.68 MB

## Check sample info 
head(colData(rse_gene), 3)
#> DataFrame with 3 rows and 71 columns
#>     SAMPLE_ID FQCbasicStats perBaseQual perTileQual  perSeqQual perBaseContent
#>   <character>   <character> <character> <character> <character>    <character>
#> 1 Sample_2914          PASS        PASS        PASS        PASS      FAIL/WARN
#> 2 Sample_4042          PASS        PASS        PASS        PASS      FAIL/WARN
#> 3 Sample_4043          PASS        PASS        PASS        PASS      FAIL/WARN
#>     GCcontent    Ncontent SeqLengthDist SeqDuplication OverrepSeqs
#>   <character> <character>   <character>    <character> <character>
#> 1        WARN        PASS          WARN           FAIL        PASS
#> 2        WARN        PASS          WARN           FAIL        PASS
#> 3        WARN        PASS          WARN           FAIL        PASS
#>   AdapterContent KmerContent SeqLength_R1 percentGC_R1 phred15-19_R1
#>      <character> <character>  <character>  <character>   <character>
#> 1           PASS          NA       75-151           49          37.0
#> 2           PASS          NA       75-151           48          37.0
#> 3           PASS          NA       75-151           49          37.0
#>   phred65-69_R1 phred115-119_R1 phred150-151_R1 phredGT30_R1 phredGT35_R1
#>     <character>     <character>     <character>    <numeric>    <numeric>
#> 1          37.0            37.0            37.0           NA           NA
#> 2          37.0            37.0            37.0           NA           NA
#> 3          37.0            37.0            37.0           NA           NA
#>   Adapter65-69_R1 Adapter100-104_R1 Adapter140_R1 SeqLength_R2 percentGC_R2
#>         <numeric>         <numeric>     <numeric>  <character>  <character>
#> 1     0.000108294       0.000260890    0.00174971       75-151           49
#> 2     0.000210067       0.000352780    0.00154716       75-151           48
#> 3     0.000134434       0.000284284    0.00191475       75-151           49
#>   phred15-19_R2 phred65-69_R2 phred115-119_R2 phred150-151_R2 phredGT30_R2
#>     <character>   <character>     <character>     <character>    <numeric>
#> 1          37.0          37.0            37.0            37.0           NA
#> 2          37.0          37.0            37.0            37.0           NA
#> 3          37.0          37.0            37.0            37.0           NA
#>   phredGT35_R2 Adapter65-69_R2 Adapter100-104_R2 Adapter140_R2 ERCCsumLogErr
#>      <numeric>       <numeric>         <numeric>     <numeric>     <numeric>
#> 1           NA     0.000276104       0.000486875    0.00132235      -58.2056
#> 2           NA     0.000326771       0.000574851    0.00137044      -81.6359
#> 3           NA     0.000330534       0.000521084    0.00153550      -99.5348
#>                  bamFile   trimmed  numReads numMapped numUnmapped
#>              <character> <logical> <numeric> <numeric>   <numeric>
#> 1 Sample_2914_sorted.bam     FALSE  89386472  87621022     1765450
#> 2 Sample_4042_sorted.bam     FALSE  59980794  58967812     1012982
#> 3 Sample_4043_sorted.bam     FALSE  64864732  63961359      903373
#>   overallMapRate concordMapRate totalMapped mitoMapped  mitoRate
#>        <numeric>      <numeric>   <numeric>  <numeric> <numeric>
#> 1         0.9802         0.9748    87143087   10039739  0.103308
#> 2         0.9831         0.9709    58215746    7453208  0.113497
#> 3         0.9861         0.9811    62983384    8307331  0.116528
#>   totalAssignedGene  rRNA_rate      Tissue         Age         Sex        Expt
#>           <numeric>  <numeric> <character> <character> <character> <character>
#> 1          0.761378 0.00396315       Brain       Adult           F    Nicotine
#> 2          0.754444 0.00301119       Brain       Adult           F    Nicotine
#> 3          0.757560 0.00288706       Brain       Adult           F    Nicotine
#>          Group    Pregnant       plate    location concentration      medium
#>    <character> <character> <character> <character>   <character> <character>
#> 1 Experimental           0      Plate2         C12         165.9       Water
#> 2      Control           0      Plate1          B4         122.6       Water
#> 3      Control           0      Plate2          C9         128.5       Water
#>          date   Pregnancy    flowcell       sum  detected subsets_Mito_sum
#>   <character> <character> <character> <numeric> <numeric>        <numeric>
#> 1          NA          No   HKCMHDSXX  37119948     24435          2649559
#> 2          NA          No   HKCG7DSXX  24904754     23656          1913803
#> 3          NA          No   HKCMHDSXX  27083602     23903          2180712
#>   subsets_Mito_detected subsets_Mito_percent subsets_Ribo_sum
#>               <numeric>            <numeric>        <numeric>
#> 1                    26              7.13783           486678
#> 2                    31              7.68449           319445
#> 3                    31              8.05178           338277
#>   subsets_Ribo_detected subsets_Ribo_percent retained_after_QC_sample_filtering
#>               <numeric>            <numeric>                          <logical>
#> 1                    11              1.31110                               TRUE
#> 2                    13              1.28267                               TRUE
#> 3                    14              1.24901                               TRUE
#>   retained_after_manual_sample_filtering
#>                                <logical>
#> 1                                   TRUE
#> 2                                   TRUE
#> 3                                   TRUE

## Check gene info
head(rowData(rse_gene), 3)
#> DataFrame with 3 rows and 13 columns
#>                         Length            gencodeID          ensemblID
#>                      <integer>          <character>        <character>
#> ENSMUSG00000102693.1      1070 ENSMUSG00000102693.1 ENSMUSG00000102693
#> ENSMUSG00000064842.1       110 ENSMUSG00000064842.1 ENSMUSG00000064842
#> ENSMUSG00000051951.5      6094 ENSMUSG00000051951.5 ENSMUSG00000051951
#>                           gene_type    EntrezID          Symbol meanExprs
#>                         <character> <character>     <character> <numeric>
#> ENSMUSG00000102693.1            TEC       71042 MGI:MGI:1918292   0.00000
#> ENSMUSG00000064842.1          snRNA          NA              NA   0.00000
#> ENSMUSG00000051951.5 protein_coding      497097 MGI:MGI:3528744   7.94438
#>                      retained_after_feature_filtering DE_in_adult_blood_smoking
#>                                             <logical>                 <logical>
#> ENSMUSG00000102693.1                            FALSE                     FALSE
#> ENSMUSG00000064842.1                            FALSE                     FALSE
#> ENSMUSG00000051951.5                             TRUE                     FALSE
#>                      DE_in_adult_brain_nicotine DE_in_adult_brain_smoking
#>                                       <logical>                 <logical>
#> ENSMUSG00000102693.1                      FALSE                     FALSE
#> ENSMUSG00000064842.1                      FALSE                     FALSE
#> ENSMUSG00000051951.5                      FALSE                     FALSE
#>                      DE_in_pup_brain_nicotine DE_in_pup_brain_smoking
#>                                     <logical>               <logical>
#> ENSMUSG00000102693.1                    FALSE                   FALSE
#> ENSMUSG00000064842.1                    FALSE                   FALSE
#> ENSMUSG00000051951.5                    FALSE                   FALSE

## Access the original counts
class(assays(rse_gene)$counts)
#> [1] "matrix" "array"
dim(assays(rse_gene)$counts)
#> [1] 55401   208
assays(rse_gene)$counts[1:3, 1:3]
#>                      [,1] [,2] [,3]
#> ENSMUSG00000102693.1    0    0    0
#> ENSMUSG00000064842.1    0    0    0
#> ENSMUSG00000051951.5  811  710  812

## Access the log-normalized counts
class(assays(rse_gene)$logcounts)
#> [1] "matrix" "array"
assays(rse_gene)$logcounts[1:3, 1:3]
#>                           [,1]      [,2]      [,3]
#> ENSMUSG00000102693.1 -5.985331 -5.985331 -5.985331
#> ENSMUSG00000064842.1 -5.985331 -5.985331 -5.985331
#> ENSMUSG00000051951.5  4.509114  4.865612  4.944597


######################
#     Human data 
######################
myfiles["EH8318"]
#> ExperimentHub with 1 record
#> # snapshotDate(): 2024-04-29
#> # names(): EH8318
#> # package(): smokingMouse
#> # $dataprovider: Lieber Institute for Brain Development (LIBD)
#> # $species: Homo sapiens
#> # $rdataclass: GenomicRanges
#> # $rdatadateadded: 2023-07-21
#> # $title: de_genes_adult_human_brain_smoking
#> # $description: GRanges with the information of the differentialy expressed ...
#> # $taxonomyid: 9606
#> # $genome: GRCh37
#> # $sourcetype: GTF
#> # $sourceurl: https://bioconductor.org/packages/smokingMouse
#> # $sourcesize: NA
#> # $tags: c("ExpressionData", "LIBD", "mouse", "Mus_musculus_Data",
#> #   "nicotine", "RNASeqData", "smoking") 
#> # retrieve record with 'object[["EH8318"]]'

## Download the human gene data
# EH8318 | de_genes_adult_human_brain_smoking
de_genes_prenatal_human_brain_smoking <- myfiles[["EH8318"]]
#> see ?smokingMouse and browseVignettes('smokingMouse') for documentation
#> loading from cache

## This is a GRanges object
class(de_genes_prenatal_human_brain_smoking)
#> [1] "GRanges"
#> attr(,"package")
#> [1] "GenomicRanges"
de_genes_prenatal_human_brain_smoking
#> GRanges object with 18067 ranges and 9 metadata columns:
#>                   seqnames              ranges strand |    Length      Symbol
#>                      <Rle>           <IRanges>  <Rle> | <integer> <character>
#>   ENSG00000019169     chr2 119699742-119752236      + |      2079       MARCO
#>   ENSG00000260400    chr10   70458257-70460551      + |      2295            
#>   ENSG00000011201     chrX     8496915-8700227      - |      7131        KAL1
#>   ENSG00000068438     chrX   48334541-48344752      + |      2740       FTSJ1
#>   ENSG00000151229    chr12   40148823-40499891      - |     10027     SLC2A13
#>               ...      ...                 ...    ... .       ...         ...
#>   ENSG00000141556    chr17   80709940-80900724      + |     10472        TBCD
#>   ENSG00000125804    chr20   26035291-26073683      + |      6332     FAM182A
#>   ENSG00000228998    chr15   90818266-90820841      + |      2576            
#>   ENSG00000149636    chr20   35380194-35402221      - |      2739        DSN1
#>   ENSG00000122644     chr7   12726481-12730559      + |      3561       ARL4A
#>                    EntrezID        logFC   AveExpr            t     P.Value
#>                   <integer>    <numeric> <numeric>    <numeric>   <numeric>
#>   ENSG00000019169      8685   -1.6032766  -1.80183     -6.14514 4.66989e-09
#>   ENSG00000260400      <NA>    0.1515813   1.17142      4.09836 6.18298e-05
#>   ENSG00000011201      3730    0.1423143   4.24576      4.09392 6.29277e-05
#>   ENSG00000068438     24140   -0.0495086   4.30660     -4.05975 7.20166e-05
#>   ENSG00000151229    114134    0.0842742   7.02625      4.00115 9.05839e-05
#>               ...       ...          ...       ...          ...         ...
#>   ENSG00000141556 101929597 -9.07984e-06   6.16583 -4.49543e-04    0.999642
#>   ENSG00000125804    728882 -2.02864e-05   2.75543 -2.93079e-04    0.999766
#>   ENSG00000228998      <NA>  1.05697e-05   4.14580  2.50417e-04    0.999800
#>   ENSG00000149636     79980  2.46976e-06   2.96401  1.02959e-04    0.999918
#>   ENSG00000122644     10124  1.62178e-06   4.51605  4.83868e-05    0.999961
#>                     adj.P.Val         B
#>                     <numeric> <numeric>
#>   ENSG00000019169 0.000084371   3.65960
#>   ENSG00000260400 0.325280946   1.21448
#>   ENSG00000011201 0.325280946   1.55562
#>   ENSG00000068438 0.325280946   1.43237
#>   ENSG00000151229 0.327315930   1.15523
#>               ...         ...       ...
#>   ENSG00000141556    0.999863  -6.30298
#>   ENSG00000125804    0.999911  -5.80161
#>   ENSG00000228998    0.999911  -6.07834
#>   ENSG00000149636    0.999961  -5.84217
#>   ENSG00000122644    0.999961  -6.13228
#>   -------
#>   seqinfo: 25 sequences from an unspecified genome; no seqlengths

## Optionally check the memory size
# lobstr::obj_size(de_genes_prenatal_human_brain_smoking)
# 3.73 MB

## Access data of human genes as normally do with other GenomicRanges::GRanges()
## objects or re-cast it as a data.frame
de_genes_df <- as.data.frame(de_genes_prenatal_human_brain_smoking)
head(de_genes_df)
#>                 seqnames     start       end  width strand Length  Symbol
#> ENSG00000019169     chr2 119699742 119752236  52495      +   2079   MARCO
#> ENSG00000260400    chr10  70458257  70460551   2295      +   2295        
#> ENSG00000011201     chrX   8496915   8700227 203313      -   7131    KAL1
#> ENSG00000068438     chrX  48334541  48344752  10212      +   2740   FTSJ1
#> ENSG00000151229    chr12  40148823  40499891 351069      -  10027 SLC2A13
#> ENSG00000136319    chr14  20724717  20774153  49437      -   9705    TTC5
#>                 EntrezID       logFC   AveExpr         t      P.Value
#> ENSG00000019169     8685 -1.60327659 -1.801830 -6.145143 4.669893e-09
#> ENSG00000260400       NA  0.15158127  1.171418  4.098360 6.182984e-05
#> ENSG00000011201     3730  0.14231431  4.245759  4.093917 6.292772e-05
#> ENSG00000068438    24140 -0.04950860  4.306597 -4.059746 7.201659e-05
#> ENSG00000151229   114134  0.08427416  7.026246  4.001149 9.058392e-05
#> ENSG00000136319    91875 -0.09445164  3.683599 -3.931427 1.186235e-04
#>                    adj.P.Val        B
#> ENSG00000019169 8.437096e-05 3.659604
#> ENSG00000260400 3.252809e-01 1.214477
#> ENSG00000011201 3.252809e-01 1.555617
#> ENSG00000068438 3.252809e-01 1.432373
#> ENSG00000151229 3.273159e-01 1.155227
#> ENSG00000136319 3.497224e-01 1.000380

Reproducibility

The smokingMouse package and the study analyses were made possible thanks to:

This package was developed using biocthis.

Date the vignette was generated.

#> [1] "2024-07-30 01:48:04 UTC"

Wallclock time spent generating the vignette.

#> Time difference of 10.89 secs

R session information.

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#>  collate  en_US.UTF-8
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#>  tz       UTC
#>  date     2024-07-30
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Bibliography

This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

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[6] H. Wickham. “testthat: Get Started with Testing”. In: The R Journal 3 (2011), pp. 5–10. URL: https://journal.r-project.org/archive/2011-1/RJournal_2011-1_Wickham.pdf.

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[8] Y. Xie. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.48. 2024. URL: https://yihui.org/knitr/.