In MahShaaban/curatedAdipoArray: A Curated Microarrays Dataset of MDI-induced Differentiated Adipocytes (3T3-L1) Under Genetic and Pharmacological Perturbations

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    warning = FALSE,
    message = FALSE
)

curatedAdipoArray

A Curated Microarrays Dataset of MDI-induced Differentiated Adipocytes (3T3-L1) Under Genetic and Pharmacological Perturbations

Overview

A curated dataset of Microarrays samples. The samples are MDI-induced pre- adipocytes (3T3-L1) at different time points/stage of differentiation under different types of genetic (knockdown/overexpression) and pharmacological (drug treatment) perturbations. The package documents the data collection and processing. In addition to the documentation, the package contains the scripts that was used to generated the data.

Introduction

What is curatedAdipoArray?

This package is for documenting and distributing a curated dataset of gene expression from MDI-induced 3T3-L1 adipocyte cell model under genetic and pharmacological modification.

What is contained in curatedAdipoArray?

The package contains two things:

1. Scripts for documenting and reproducing the dataset in inst/scripts.
2. Access to the clean and the processed data SummarizedExperiment objects through ExperimentHub.

What is curatedAdipoArray for?

The data contained in the package can be used in any number of downstream analysis such as differential expression and gene set enrichment.

Installation

The curatedAdipoArray package can be installed from Bioconductor using BiocManager.

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("curatedAdipoArray")

Docker image

The pre-processing and processing of the data setup environment is available as a docker image. This image is also suitable for reproducing this document. The docker image can be obtained using the docker CLI client.

$ docker pull bcmslab/adiporeg_array:latest

Generating curatedAdipoArray

Data collection and acquisition

We surveyed the literature for MDI-induced 3T3-L1 microarrays studies with or without course perturbations. 43 published studies were included. 47 datasets from these studies were examined for metadata and annotation completeness. One and two studies were excluded for missing probe intensities and probe annotations respectively. In addition to the data and the metadata, the original publications were also examined to extract the experimental design and other missing experimental details. The remaining datasets were curated, cleaned and packaged in two final datasets for each type of differentiation course perturbation.

Data processing and quality assessment

The probe intensities, probe annotation and metadata of each study were obtained from the gene expression omnibus (GEO) using GEOquery. The probe intensities (expression matrices) were collapsed using probe to gene symbol annotation using collapsRows. The metadata for studies, data series and samples were homogenized across studies using common vocabularies to describe the experimental designs. Information for each sample of the differentiation status and time point were retrieved and recorded. In addition, the treatment type, target, dose and time were added for each sample. The processed datasets were packaged in R/Bioconductor``SummarizedExperiment object individually.

For illustration purposes, the dataset objects were merged into two separate SummarizedExperiment for the genetic or the pharmacological perturbations. In each set, missing and low intensity genes were removed. Then the gene intensities were log transformed and normalized across studies using limma. Finally, the known batch effects (data series and platform) were removed using sva.

Exploring the data objects

# loading required libraries
library(ExperimentHub)
library(SummarizedExperiment)

# query package resources on ExperimentHub
eh <- ExperimentHub()
query(eh, "curatedAdipoArray")

listResources(eh, "curatedAdipoArray")[43]

genetic <- query(eh, 'curatedAdipoArray')[[43]]

Each of the objects attached to this package is an SummarizedExperiment. This object contains two main tables. The first is the expression matrix in the form of probe/gene intensities. The second table is the sample metadata.

# show class of the SummarizedExperiment
class(genetic)

# show the first table
assay(genetic)[1:5, 1:5]

# show the second table
colData(genetic)[1:5,]

The samples metadata were manually curated using controlled vocabularies to make comparing and combining the data easier. Table 1. show the columns and the descriptions of the metadata table.

column_desc <- list(
"series_id" = "The GEO series identifier.",
"sample_id"="The GEO sample identifier.",
"pmid"="The pubmed identifier of the published study.",
"time"="The time from the start of the differentiation protocol in hours.",
"media"="The differentiation media MDI or none.",
"treatment"="The treatment status: none, drug, knockdown or overexpression.",
"treatment_target"="The target of the treatment: gene name or a biological 
pathway.",
"treatment_type"="The type of the treatment.",
"treatment_subtype"="The detailed subtype of the treatment.",
"treatment_time"="The time of the treatment in relation to differentiation time
: -1, before; 0, at; and 1 after the start of the differentiation induction.",
"treatment_duration"="The duration from the treatment to the collection of the 
RNA from the sample.",
"treatment_dose"="The dose of the treatment.",                           
"treatment_dose_unit"="The dose unit of the treatment.",                       
"channels"="The number of the channels on the array chip: 1 or 2.",
"gpl"="The GEO GPL/annotation identifier.",
"geo_missing"="The availability of the data on GEO: 0 or 1.",
"symbol_missing"="The availability of the gene symbol of the probes in  the GPL
object.",                                 
"perturbation_type"="The type of the perturbation: genetic or pharmacological."
)
knitr::kable(
    data.frame(
        col_id = names(column_desc),
        description = unlist(column_desc, use.names = FALSE)
    )
)

Citing the studies in this subset of the data

The original articles where the datasets were first published are recorded by their pubmid ID. Please, cite the articles when using the related dataset.

Citing curatedAdipoArray

To cite the package use:

# citing the package
citation("curatedAdipoArray")

Related Packages

The packages curatedAdipoRNA and curatedAdipoChIP contain gene expression and DNA-binding of important factors in the same cell line model, respectively.

Session Info

devtools::session_info()

MahShaaban/curatedAdipoArray documentation built on Feb. 16, 2020, 2:30 a.m.