Limma Analysis Output:

Links to PDF copies of plots are in 'Plots' section below

Differential Expression Counts:

	Up	Flat	Down
RTMetastatic-RMControl	400	6944	3694

Alt-click links to download file.

Click floppy disc icon associated history item to download all files.

.tsv files can be viewed in Excel or any spreadsheet program.

Additional Information

Genes without more than 0.5 CPM in at least 2 samples are insignificant and filtered out.
14664 of 25702 (57.05%) genes were filtered out for low expression.
TMM was the method used to normalise library sizes.
The limma-voom method was used.
Weights were not applied to samples.
eBayes was used with robust settings (robust=TRUE).
MD Plot highlighted genes are significant at FDR of 0.05 and exhibit log2-fold-change of at least 0.

Summary of experimental data:

*CHECK THAT SAMPLES ARE ASSOCIATED WITH CORRECT GROUP(S)*

SampleID	CellTypeStatus (Primary Factor)
Normal_3.gz	RMControl
Normal_8.gz	RMControl
Normal_5.gz	RMControl
TNBC_29.gz	RTMetastatic
TNBC_18.gz	RTMetastatic
TNBC_14.gz	RTMetastatic
Normal_11.gz	RMControl
Normal_10.gz	RMControl
Normal_9.gz	RMControl
Normal_7.gz	RMControl
Normal_6.gz	RMControl
Normal_4.gz	RMControl
Normal_2.gz	RMControl
Normal_1.gz	RMControl
TNBC_50.gz	RTMetastatic
TNBC_45.gz	RTMetastatic
TNBC_44.gz	RTMetastatic
TNBC_43.gz	RTMetastatic
TNBC_41.gz	RTMetastatic
TNBC_40.gz	RTMetastatic
TNBC_34.gz	RTMetastatic
TNBC_20.gz	RTMetastatic

Citations

Please cite the following paper for this tool:
Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS, Blewitt ME, Asselin-Labat ML, Smyth GK, Ritchie ME (2015). Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Research, 43(15), e97.

limma

Please cite the paper below for the limma software itself. Please also try to cite the appropriate methodology articles that describe the statistical methods implemented in limma, depending on which limma functions you are using. The methodology articles are listed in Section 2.1 of the limma User's Guide. Cite no. 3 only if sample weights were used.

Smyth GK (2005). Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. doit,. Irizarry, W. Huber (eds), Springer, New York, pages 397-420.
Law CW, Chen Y, Shi W, and Smyth GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29.
Ritchie ME, Diyagama D, Neilson J, van Laar R, Dobrovic A, Holloway A and Smyth GK (2006). Empirical array quality weights for microarray data. BMC Bioinformatics 7, Article 261.

edgeR

Please cite the first paper for the software itself and the other papers for the various original statistical methods implemented in edgeR. See Section 1.2 in the edgeR User's Guide for more detail.

Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140
Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887
Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332
McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297

Please report problems or suggestions to: su.s@wehi.edu.au

Session Info

Task started at:	2020-06-04 00:49:28
Task ended at:	2020-06-04 00:50:28
Task run time:	1 mins

Limma Analysis Output:

Differential Expression Counts:

Plots:

Tables:

R Data Object:

Glimma Interactive Results:

Additional Information

Summary of experimental data:

Citations

limma

edgeR