Differential gene abundance report: PTox-WP6-HepaRG

differentialabundance workflow versionv1.6.0dev-g8f7be3a

2026-07-17

Guigo Lab
Computational Biology of RNA Processing

Abstract

This report summarises the exploratory analysis of gene expression for HepaRG samples in WP6 of PTox project. The analysis was performed with the nf-core/differentialabundance pipeline.

Results

Counts

Input was a matrix of 49262 genes for 198 samples, reduced to 16483 genes after filtering for low abundance.

Filtering statistics

Filtering is applied to the input matrix according to the --filtering_* options, removing features that do not meet the specified criteria. This section summarizes the impact of each filtering rule, reporting how many genes were excluded per rule and how many passed all filters.

The following tables summarize retention and filtering failures.

Exploratory analysis

Abundance value distributions

The following plots show the abundance value distributions of input matrices. A log2 transformation is applied where not already performed.

Box plots

Whiskers in the above boxplots show 1.5 times the inter-quartile range.

Density plots

Sample relationships

Principal components plots

Variance stabilised

Normalised

Raw

Scree plot

Variance stabilised

Normalised

Raw

Principal components/ metadata associations

For the variance stabilised matrix, an ANOVA test was used to determine assocations between continuous principal components and categorical covariates (including the variable of interest).

The resulting p values are illustrated below.

The variable ‘group’ shows an association with PC1 (12.3%) (p = 0.00). The variable ‘dose’ shows an association with PC2 (5.9%) (p = 0.01). The variable ‘batch’ shows an association with PC1 (12.3%) (p = 0.00). The variable ‘regime’ shows an association with PC7 (2.1%) (p = 0.00). The variable ‘compound_name’ shows an association with PC1 (12.3%) (p = 0.00).

Outlier detection

group

14 possible outliers were detected in groups defined by group: P3_TC3_2Mi_HC_A2toD2, P3_TC3_2Mi_LC_A1toD1, P4_TC4_1Mi_HC_A2toD2, P4_TC4_1Mi_LC_E1toH1, P6_TC6_2Emi_LC_E1toH1, P9_TC9_1Vi_HC_A3toD3, P4_MoA2_KCA_HC_E3toH3, P5_MoA3_Asp_LC_A3toD3, P5_MoA3_Asp_LC_E3toH3, P1_DMSO_Control_A4toD4, P2_DMSO_Control_E4toH4, P5_DMSO_Control_A12toD12, P7_DMSO_Control_E4toH4, P9_DMSO_Control_A7toD7

batch

9 possible outliers were detected in groups defined by batch: P1_TC1_Imi_LC_E1toH1, P3_TC3_2Mi_HC_A2toD2, P3_TC3_2Mi_LC_A1toD1, P4_MoA2_KCA_HC_E3toH3, P4_TC4_1Mi_LC_A1toD1, P4_TC4_1Mi_LC_E1toH1, P5_DMSO_Control_A12toD12, P6_TC6_2Emi_LC_E1toH1, P7_TC7_2Ei_LC_A1toD1

compound_name

14 possible outliers were detected in groups defined by compound_name: P4_TC4_1Mi_LC_A1toD1, P4_TC4_1Mi_LC_E1toH1, P6_TC6_2Emi_LC_E1toH1, P7_TC7_2Ei_LC_A1toD1, P7_TC7_2Ei_LC_A5toD5, P7_TC7_2Ei_LC_E1toH1, P3_TC3_2Mi_HC_A2toD2, P3_TC3_2Mi_LC_A1toD1, P5_MoA3_Asp_LC_E3toH3, P7_DMSO_Control_E4toH4, P9_DMSO_Control_A4toD4, P9_DMSO_Control_A7toD7, P1_TC1_Imi_LC_E1toH1, P4_MoA2_KCA_HC_E3toH3

regime

18 possible outliers were detected in groups defined by regime: P7_DMSO_Control_E4toH4, P9_DMSO_Control_A4toD4, P9_DMSO_Control_A7toD7, P3_TC3_2Mi_HC_A2toD2, P3_TC3_2Mi_LC_A1toD1, P4_TC4_1Mi_LC_E1toH1, P7_POS_Control_AflaB1_A11toD11, P7_POS_Control_AflaB1_A7toD7, P7_POS_Control_AflaB1_E11toH11, P7_POS_Control_AflaB1_E7toH7, P7_TC7_2Ei_LC_A1toD1, P7_TC7_2Ei_LC_E1toH1, P9_TC9_1Vi_HC_A3toD3, P9_TC9_1Vi_HC_A6toD6, P9_TC9_1Vi_HC_A9toD9, P9_TC9_1Vi_LC_A5toD5, P9_TC9_1Vi_LC_A8toD8, P9_TC9_1Vi_LC_E5toH5

dose

21 possible outliers were detected in groups defined by dose: P3_TC3_2Mi_HC_A2toD2, P7_POS_Control_AflaB1_A11toD11, P7_POS_Control_AflaB1_A7toD7, P7_POS_Control_AflaB1_E11toH11, P7_POS_Control_AflaB1_E7toH7, P9_TC9_1Vi_HC_A3toD3, P9_TC9_1Vi_HC_A6toD6, P9_TC9_1Vi_HC_A9toD9, P3_TC3_2Mi_LC_A1toD1, P4_TC4_1Mi_LC_A1toD1, P4_TC4_1Mi_LC_E1toH1, P7_TC7_2Ei_LC_A1toD1, P7_TC7_2Ei_LC_A5toD5, P7_TC7_2Ei_LC_E1toH1, P7_TC7_2Ei_LC_E5toH5, P9_TC9_1Vi_LC_A5toD5, P9_TC9_1Vi_LC_A8toD8, P9_TC9_1Vi_LC_E5toH5, P7_DMSO_Control_E4toH4, P9_DMSO_Control_A4toD4, P9_DMSO_Control_A7toD7

Methods

Filtering

Filtering was carried out by selecting genes with an abundance of at least 10 in at least 2 samples.

Exploratory analysis

Differential analysis

Gene set analysis

GPROFILER2

Appendices

All parameters

Software versions

Note: For a more detailed accounting of the software and commands used (including containers), consult the execution report produced as part of the ‘pipeline info’ for this workflow.

nf-core/differentialabundance: Citations

nf-core

Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

Nextflow

Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

Pipeline tools

• GSEA

  Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-15550.

R packages

• affy

  Gautier L, Cope L, Bolstad BM, Irizarry RA. Affy–analysis of affymetrix genechip data at the probe level. Bioinformatics. 2004;20(3):307-315.

• DESeq2

  Love MI, Huber W, Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12):550. PubMed PMID: 25516281; PubMed Central PMCID: PMC4302049.

• GEOQuery

  Davis S, Meltzer PS. Geoquery: a bridge between the gene expression omnibus (Geo) and bioconductor. Bioinformatics. 2007;23(14):1846-1847.

• ggplot2

  H. Wickham (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

• gprofiler2

  Kolberg L, Raudvere U, Kuzmin I, Vilo J, Peterson H (2020). “gprofiler2– an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler.” F1000Research, 9 (ELIXIR)(709). R package version 0.2.2.

• Limma

  Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for rna-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.

• optparse

  Trevor L Davis (2018). optparse: Command Line Option Parser.

• plotly

  C. Sievert (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida.

• Proteus

  Gierlinski M, Gastaldello F, Cole C, Barton GJ. Proteus : An r Package for Downstream Analysis of Maxquant Output. Bioinformatics; 2018.

• R

  R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

• RColorBrewer

  Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes.

• RMarkdown

  JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone (2022). rmarkdown: Dynamic Documents for R.

• shinyngs

  Jonathan R Manning (2022). Shiny apps for NGS etc based on reusable components created using Shiny modules. Computer software. Vers. 1.5.3. Jonathan Manning, Dec. 2022. Web.

• SummarizedExperiment

  Morgan M, Obenchain V, Hester J and Pagès H (2020). SummarizedExperiment: SummarizedExperiment container.

Software packaging/containerisation tools

• Anaconda

  Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

• Bioconda

  Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

• BioContainers

  da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

• Docker

  Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. doi: 10.5555/2600239.2600241.

• Singularity

  Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.