Using curatedAdipoChIP

Mahmoud Ahmed

2021-11-21

curatedAdipoChIP

A Curated ChIP-Seq Dataset of MDI-induced Differentiated Adipocytes (3T3-L1)

Overview

In this document, we introduce the purpose of curatedAdipoChIP package, its contents and its potential use cases. This package is a curated dataset of ChIP-Seq samples. The samples are MDI-induced pre-adipocytes (3T3-L1) at different time points/stage of differentiation. The ChIP-antibodies used in this dataset are of transcription factors, chromatin remodelers and histone modifications. The package document the data collection, pre-processing and processing. In addition to the documentation the package contains the scripts that were used to generate the data in inst/scripts and access to the final RangedSummarizedExperiment object through ExperimentHub.

Introduction

What is curatedAdipoChIP?

It is an R package for documenting and distributing a curated dataset. The package doesn’t contain any R functions.

What is contained in curatedAdipoChIP?

The package contains two different things:

  1. Scripts for documenting/reproducing the data in inst/scripts
  2. Access to the final RangedSummarizedExperiment through ExperimentHub.

What is curatedAdipoChIP for?

The RangedSummarizedExperiment object contains the peak_counts, colData, rowRanges and metadata which can be used for the purposes of conducting differential peak binding or gene set enrichment analysis on the cell line model.

Installation

The curatedAdipoChIP package can be installed from Bioconductor using BiocManager.

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

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_chip:latest

Generating curatedAdipoChIP

Search starategy & data collection

The term “3T3-L1” was used to search the NCBI SRA repository. The results were sent to the run selector. 1,176 runs were viewed. The runs were faceted by Assay Type and the “chip-seq” which resulted in 739 runs. Only 235 samples from 20 different studies were included after being manually reviewed to fit the following criteria: * The raw data is available from GEO and has a GEO identifier (GSM#) * The raw data is linked to a published publicly available article * The protocols for generating the data sufficiently describe the origin of the cell line, the differentiation medium and the time points when the samples were collected. * In case the experimental designs included treatment other than the differentiation medias, the control (non-treated) samples were included.

Note: The data quality and the platform discrepancies are not included in these criteria

Pre-processing

The scripts to download and process the raw data are located in inst/scripts/ and are glued together to run sequentially by the GNU make file Makefile. The following is basically a description of the recipes in the Makefile with emphasis on the software versions, options, inputs and outputs.

1. Downloading data download_fastq

  • Program: wget (1.18)
  • Input: run.csv, the URLs column
  • Output: *.fastq.gz
  • Options: -N

2. Making a genome index make_index

  • Program: bowtie2-build (2.3.0)
  • Input: URL for mm10 mouse genome fasta files
  • Output: *.bt2 bowtie2 index for the mouse genome
  • Options: defaults

3. Dowinloading annotations get_annotation

  • Program: wget (1.18)
  • Input: URL for mm10 gene annotation file
  • Output: annotation.gtf
  • Options: -N

4. Aligning reads align_reads

  • Program: bowtie2 (2.3.0)
  • Input: *.fastq.gz and mm10/ bowtie2 index for the mouse genome
  • Output: *.sam
  • Options: --no-unal

5. Calling peaks peak_calling

  • Program: macs2 (2.1.2)
  • Input: *.bam and
  • Output: peaks.bed
  • Options: -B --nomodel --SPMR

6. Making a peakset get_peakset

  • Program: bedtools (2.26.0)
  • Input: *.bed
  • Output: peakset.bed
  • Options: -c 4,4 -o count_distinct,distinct

7. Counting reads in peaks count_features

  • Program: bedtools multicov (2.26.0)
  • Input: *.bam and peakset.bed
  • Output: peakset.txt
  • Options: defaults

8. Quality assessment fastqc

  • Program: fastqc (0.11.5)
  • Input: *.fastq.gz and *.sam
  • Output: *_fastqc.zip
  • Option: defaults

Processing

The aim of this step is to construct a self-contained object with minimal manipulations of the pre-processed data followed by a simple exploration of the data in the next section.

Making a summarized experiment object peak_object

The required steps to make this object from the pre-processed data are documented in the script and are supposed to be fully reproducible when run through this package. The output is a RangedSummarizedExperiment object containing the peak counts and the phenotype and features data and metadata.

The RangedSummarizedExperiment contains * The gene counts matrix peak_counts * The phenotype data colData. The column name links each peak to one or more samples * The feature data rowRanges * The metadata metadata which contain a data.frame of the studies from which the samples were collected.

Exploring the peak_counts object

In this section, we conduct a simple exploration of the data objects to show the content of the package and how they can be loaded and used.

The count matrix can be accessed using assay. Here we show the first five entries of the first five samples.

The phenotype/samples data is a data.frame, It can be accessed using colData. The time and stage columns encode the time point in hours and stage of differentiation respectively. The column factor records the ChIP antibody used in the sample.

Other columns in colData are selected information about the samples/runs or identifiers to different databases. The following table provides the description of each of these columns.

col_name description
id The GEO sample identifier.
runs A list of tibbles with information about individual run files.
study The SRA study identifier.
pmid The PubMed ID of the article where the data were published originally.
factor The name of the ChIP antibody that was used in the sample.
time The time point of the sample when collected in hours. The time is recorded from the beginning of the protocol as 0 hours.
stage The stage of differentiation of the sample when collected. Possible values are 0 to 3; 0 for non-differentiated; 1 for differentiating; and 2/3 for maturating samples.
bibtexkey The key of the study where the data were published originally. This maps to the studies object of the metadata which records the study information in bibtex format.
control_id The GEO sample identifier of the control (input) sample.
control_type The method for assigning the control samples. Possible values are “provided” when the same study has control sample/s or “other” when control samples were selected from other studies.
library_layout The type of RNA library. Possible values are SINGLE for single-end and PAIRED for paired-end runs.
instrument_model The name of the sequencing machine that was used to obtain the sequence reads.
qc The quality control output of fastqc on the separate files/runs.

The features data are a GRanges object and can be accessed using rowRanges.

# print GRanges object
rowRanges(peak_counts)
#> GRanges object with 837030 ranges and 10 metadata columns:
#>               seqnames            ranges strand |
#>                  <Rle>         <IRanges>  <Rle> |
#>        peak_1     chr1   3029191-3029410      * |
#>        peak_2     chr1   3031394-3031660      * |
#>        peak_3     chr1   3037771-3037971      * |
#>        peak_4     chr1   3049860-3050379      * |
#>        peak_5     chr1   3051655-3051881      * |
#>           ...      ...               ...    ... .
#>   peak_837852     chrY 90830107-90830335      * |
#>   peak_837853     chrY 90830368-90830710      * |
#>   peak_837854     chrY 90834938-90835190      * |
#>   peak_837855     chrY 90835545-90836986      * |
#>   peak_837856     chrY 90839338-90840071      * |
#>                                               peak        name
#>                                    <CharacterList> <character>
#>        peak_1                GSM2515964,GSM2515966      peak_1
#>        peak_2 GSM1370450,GSM1893625,GSM1893626,...      peak_2
#>        peak_3                           GSM2515948      peak_3
#>        peak_4 GSM1412513,GSM2515948,GSM2515949,...      peak_4
#>        peak_5                           GSM2515966      peak_5
#>           ...                                  ...         ...
#>   peak_837852                            GSM340808 peak_837852
#>   peak_837853                            GSM340808 peak_837853
#>   peak_837854                            GSM686970 peak_837854
#>   peak_837855 GSM1368009,GSM1368013,GSM1368014,... peak_837855
#>   peak_837856 GSM1370470,GSM1412512,GSM2233369,... peak_837856
#>                      annotation   geneChr geneStart   geneEnd geneLength
#>                     <character> <integer> <integer> <integer>  <integer>
#>        peak_1 Distal Intergenic         1   3214482   3671498     457017
#>        peak_2 Distal Intergenic         1   3214482   3671498     457017
#>        peak_3 Distal Intergenic         1   3214482   3671498     457017
#>        peak_4 Distal Intergenic         1   3214482   3671498     457017
#>        peak_5 Distal Intergenic         1   3214482   3671498     457017
#>           ...               ...       ...       ...       ...        ...
#>   peak_837852 Distal Intergenic        21  90785442  90816465      31024
#>   peak_837853 Distal Intergenic        21  90785442  90816465      31024
#>   peak_837854 Distal Intergenic        21  90785442  90816465      31024
#>   peak_837855 Distal Intergenic        21  90785442  90816465      31024
#>   peak_837856 Distal Intergenic        21  90785442  90816465      31024
#>               geneStrand      geneId distanceToTSS
#>                <integer> <character>     <numeric>
#>        peak_1          2        Xkr4        642088
#>        peak_2          2        Xkr4        639838
#>        peak_3          2        Xkr4        633527
#>        peak_4          2        Xkr4        621119
#>        peak_5          2        Xkr4        619617
#>           ...        ...         ...           ...
#>   peak_837852          1       Erdr1         44665
#>   peak_837853          1       Erdr1         44926
#>   peak_837854          1       Erdr1         49496
#>   peak_837855          1       Erdr1         50103
#>   peak_837856          1       Erdr1         53896
#>   -------
#>   seqinfo: 64 sequences from an unspecified genome; no seqlengths

Notice there are two types of data in this object. The first is the coordinates of the identified peaks ranges(peak_counts). The second is the annotation of the these regions mcols(peak_counts). The following table show the description of the second annotation item. Except for the first three columns, all annotations were obtained using ChIPSeeker::annotatePeak as described in the inst/scripts.

col_name description
peak The GEO sample identifier of the samples containing this peak in a CharacterList.
name The name of the peak in the set of unique merged peaks.
annotation genomic feature of the peak, for instance if the peak is located in 5’UTR, it will annotated by 5’UTR. Possible annotation is Promoter-TSS, Exon, 5’ UTR, 3’ UTR, Intron, and Intergenic.
geneChr Chromosome of the nearest gene
geneStart Gene start
geneEnd Gene end
geneLength Gene length
geneStrand Gene Strand
geneId Gene symbol
distanceToTSS Distance from peak to gene TSS

The metadata is a list of one object. studies is a data.frame containing the bibliography information of the studies from which the data were collected. Here we show the first entry in the studies object.

Summary of the studies in the dataset

GEO series ID PubMed ID Num. of Samples Time (hr) Differentiation Stage Factor
SRP000630 18981474 18 0/ 24/ 48/ 72/ 96/ 144 0/1/2/3 PPARG/ RXRG/ POLR2A
SRP002283 20442865 1 NA 3 E2F4
SRP002337 20887899 15 -48/ 0/ 48/ 168 0/1/3 H3K4me3/ H3K27me3/ H3K36me3/ H3K4me2/ H3K4me1/ H3K27ac/ PPARG
SRP002507 20478996 2 0/ 6 0/1 CEBPB
SRP006001 21427703 13 0/ 2/ 4/ 48/ 144 0/1/3 CEBPB/ CEBPD/ NR3C1/ STAT5A/ RXRG/ PPARG/ POLR2A
SRP008061 21914845 1 24 1 TCF7
SRP009613 24315104 6 NA 0 PPARG/ JUN/ CREB1/ PSMB1/ Ubiquitin
SRP016054 23178591 4 NA/ 168 0/3 H3K4me3/ H3K27me3/ H3K9me2/ H3K9me3
SRP028367 23885096 7 168 3 PPARG/ MED1/ CEBPA/ POLR2A/ CREB1
SRP029985 24912735 3 0/ 168 0/3 KDM1A/ NRF1
SRP041129 24788520 10 NA 3 MED1/ CREB1/ EP300/ NCOR1/ CEBPA/ CEBPB/ ATF2/ JUND/ FOSL2
SRP041249 24857652 19 4 1 ATF2/ ATF7/ JUN/ FOSL2/ KLF4/ KLF5/ PBX1/ STAT1/ VDR/ RXRG/ MED1/ EP300/ SMARCA4/ H3K27ac/ H3K4me1/ H3K4me2
SRP042079 24953653 2 0 0 GPS2
SRP043216 25503565 8 NA 0 H3K27ac/ H3K27me3/ H3K36me3/ H3K4me1/ H3K4me3/ H3K79me2/ H3K79me3/ H4K20me1
SRP064188 26590716 11 0/ 144 0/3 H3K27me3/ H3K9me3/ SETDB1/ MBD1/ POLR2A
SRP065028 28398509 1 168 3 KMT2B
SRP078506 27899593 14 0/ 4/ 48 0/1 H3K4me3/ KDM5A/ KDM5C
SRP080809 28107648 2 NA 0 CEBPB
SRP100871 28475875 52 4/ 0/ 48/ 96/ 168 1/0/2/3 CTCF/ H3K27ac/ H3K4me1/ H3K4me2/ HDAC2/ HDAC3/ MED1/ NCOR1/ EP300/ SMC1A

Some of the samples included in this object are control samples.

# show the number of control samples
table(is.na(peak_counts$control_id))
#> 
#> FALSE  TRUE 
#>   189    18

Also, some entries are recorded as NA such as in the column time when the the data are missing or couldn’t be determined from the metadata.

Example of using curatedAdipoChIP

Motivation

All the samples in this dataset come from the 3T3-L1 cell line. The MDI induction media, were used to induce adipocyte differentiation. The two important variables in the dataset are time and stage, which correspond to the time point and stage of differentiation when the sample were captured. Ideally, this dataset should be treated as a time course. However, for the purposes of this example, we only used samples from two differentiation stages 0 and 1 for the transcription factor (CEBPB) and treated them as independent groups. The goal of this example is to show how a typical differential binding analysis can be applied in the dataset. The main focus is to explain how the the data and metadata in peak_counts fit in each main piece of the analysis. We started by sub-setting the object to the samples of interest, filtering the low quality samples and low count genes. Then we applied the DESeq2 method with the default values.

Subsetting the object to samples and peaks of interest

First, we subset the peak_counts object to all samples in the differentiation stage 0 or 1 for the transcription factor CEBPB. Using the samples IDs we get chose only the features/peaks that was found in at least one of samples. Notice that other criteria for choosing peaks can be applied depending on the purpose of the analysis. The total number of samples is 8 with 4 samples in each stage. The total number of peaks is 49028.

Filtering low quality samples

Since the quality metrics are reported per run file, we need to get the SSR* id for each of the samples. Notice that, some samples would have more than one file. In this case because some of the samples are paired-end, so each of them would have two files SRR\*_1 and SRR\*_2. This kind of information is available in colData.

The qc object of the metadata contains the output of fastqc in a qc_read class. More information on this object can be accessed by calling ?fastqcr::qc_read. Here, we only use the per_base_sequence_quality to filter out low quality samples. This is by no means enough quality control but it should drive the point home.

After tidying the data, we get a data.frame with three columns; run, Mean and Base for the run ID, the mean quality score and the base number in each read. fastqc provide thorough documentation of this quality control module and others.

To identify the low quality samples, we categorize the runs run_average which correspond to the average of the per base mean scores. The following figure should make it easier to see why this cutoff were used in this case.

The run IDs of the “bad” samples is then used to remove them from the dataset.

Filtering peaks with low counts

To identify the low count feature/peaks, we keep only the features with at least 10 reads in 2 or more samples. Then we subset the object to exclude the rest.

Check sample reproducibility

One expects that samples from the same group should reflect similar biology. In fact, this is also expected for the differential binding analysis to be reliable. In the context of ChIP-Seq, several measures were proposed to quantify the similarities and discrepancies between the samples, check this article for more. Here, we use scatter plots of the pairs of samples in each group to show that there is a general agreement in terms of the counts in the peakset.

#> $`0`
#> NULL
#> 
#> $`1`
#> NULL

Applying differential binding using DESeq2

DESeq2 is a well documented and widely used R package for the differential binding analysis of ChIP-Seq data. Here we use the default values of DESeq to find the peaks which are deferentially bound in stage 1 compared to 0.

Citing the studies in this subset of the data

Speaking of studies, as mentioned earlier the studies object contains full information of the references of the original studies in which the data were published. Please cite them when using this dataset.

Citing curatedAdipoChIP

For citing the package use:

# citing the package
citation("curatedAdipoChIP")
#> 
#> To cite package 'curatedAdipoChIP' in publications use:
#> 
#>   Mahmoud Ahmed (2021). curatedAdipoChIP: A Curated ChIP-Seq Dataset of
#>   MDI-induced Differentiated Adipocytes (3T3-L1). R package version
#>   1.11.0. https://github.com/MahShaaban/curatedAdipoChIP
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {curatedAdipoChIP: A Curated ChIP-Seq Dataset of MDI-induced Differentiated Adipocytes
#> (3T3-L1)},
#>     author = {Mahmoud Ahmed},
#>     year = {2021},
#>     note = {R package version 1.11.0},
#>     url = {https://github.com/MahShaaban/curatedAdipoChIP},
#>   }

Session Info

devtools::session_info()
#> ─ Session info  ──────────────────────────────────────────────────────────────
#>  hash: man: light skin tone, bald, counterclockwise arrows button, flag: Cook Islands
#> 
#>  setting  value
#>  version  R Under development (unstable) (2021-11-16 r81199)
#>  os       Ubuntu 20.04.3 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  C
#>  ctype    en_US.UTF-8
#>  tz       America/New_York
#>  date     2021-11-21
#>  pandoc   2.5 @ /usr/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package                * version  date (UTC) lib source
#>  annotate                 1.73.0   2021-10-26 [2] Bioconductor
#>  AnnotationDbi            1.57.1   2021-10-29 [2] Bioconductor
#>  AnnotationHub          * 3.3.6    2021-11-15 [2] Bioconductor
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#>  BiocVersion              3.15.0   2021-10-26 [2] Bioconductor
#>  Biostrings               2.63.0   2021-10-26 [2] Bioconductor
#>  bit                      4.0.4    2020-08-04 [2] CRAN (R 4.2.0)
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#>  bitops                   1.0-7    2021-04-24 [2] CRAN (R 4.2.0)
#>  blob                     1.2.2    2021-07-23 [2] CRAN (R 4.2.0)
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#>  cachem                   1.0.6    2021-08-19 [2] CRAN (R 4.2.0)
#>  callr                    3.7.0    2021-04-20 [2] CRAN (R 4.2.0)
#>  cli                      3.1.0    2021-10-27 [2] CRAN (R 4.2.0)
#>  colorspace               2.0-2    2021-06-24 [2] CRAN (R 4.2.0)
#>  crayon                   1.4.2    2021-10-29 [2] CRAN (R 4.2.0)
#>  curatedAdipoChIP       * 1.11.0   2021-11-21 [1] Bioconductor
#>  curl                     4.3.2    2021-06-23 [2] CRAN (R 4.2.0)
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