A suite of R packages for the analysis of DNA copy number microarray experiments Application in cancerology Philippe Hupé 1 , 2 1 UMR144 Institut Curie, CNRS 2 U900 Institut Curie, INSERM, Mines Paris Tech The R User Conference 2009 Rennes Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 1 / 19
Outline Biological / clinical context 1 R packages description 2 End-user interfaces / automatic workflow 3 Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 2 / 19
Biological / clinical context Outline Biological / clinical context 1 R packages description 2 End-user interfaces / automatic workflow 3 Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 3 / 19
Biological / clinical context DNA copy number alteration in tumour tumoral cell normal cell Chaos in cancer cells gain, loss or amplification of chromosomes or pieces of chromosomes. Molecular profiling of tumours Identification of DNA copy number alterations in each patient Is the pattern of alterations is related to patient outcome (e.g. relapse, metastasis)? Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 4 / 19
Biological / clinical context DNA copy number alteration in tumour tumoral cell normal cell Chaos in cancer cells gain, loss or amplification of chromosomes or pieces of chromosomes. Molecular profiling of tumours Identification of DNA copy number alterations in each patient Is the pattern of alterations is related to patient outcome (e.g. relapse, metastasis)? Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 4 / 19
Biological / clinical context High-throughput quantification of DNA copy number Microarray technology DNA copy number for 5 × 10 3 up to 2 × 10 6 genomic loci Probes spotted on a glass array (i.e. the microarray) microarray Colour study: squares with concentric circles Wassily Kandinsky , 1913 Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 5 / 19
Biological / clinical context High-throughput quantification of DNA copy number Microarray technology DNA copy number for 5 × 10 3 up to 2 × 10 6 genomic loci Probes spotted on a glass array (i.e. the microarray) N-MYC Unbalanced amplification translocation 1p - 17q 1q gain 17q gain 1q gain 1p loss DNA copy number profile of the tumour Karyotype of the tumour Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 5 / 19
Biological / clinical context High-throughput quantification of DNA copy number Microarray technology DNA copy number for 5 × 10 3 up to 2 × 10 6 genomic loci Probes spotted on a glass array (i.e. the microarray) N-MYC Unbalanced amplification translocation 1p - 17q 1q gain 17q gain 1q gain 1p loss DNA copy number profile of the tumour Karyotype of the tumour Huge amount of data ( ∼ 2 × 10 6 variables for each patient) Need for biostatistical algorithms and automatic bioinformatic pipelines Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 5 / 19
Biological / clinical context Biostatistical workflow ➊ Biological/clinical question ➋ Experimental design ➌ High-throughput experiments DNA copy number, mRNA expression ➏ ➐ ➍ Image analysis Quality Extraction of the control biological information ➎ Normalisation ➑ ➒ Clinical biostatistics Biological/clinical validation Classification and interpretation Systems biology R packages available from www.bioconductor.org MANOR: spatial normalisation GLAD: extraction of the biological information ITALICS: normalisation + extraction of the biological information Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 6 / 19
R packages description Outline Biological / clinical context 1 R packages description 2 End-user interfaces / automatic workflow 3 Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 7 / 19
R packages description MANOR: an algorithm to detect spatial bias Neuvial et al., BMC Bioinformatics, 2006 Abnormal Log-Ratio in the 1 corner Spatial trend estimation by 2 2D-LOESS Spatial segmentation 3 Bias area are removed 4 Spots are outliers in the 5 genomic profile Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 8 / 19
R packages description MANOR: an algorithm to detect spatial bias Neuvial et al., BMC Bioinformatics, 2006 Abnormal Log-Ratio in the 1 corner Spatial trend estimation by 2 2D-LOESS Spatial segmentation 3 Bias area are removed 4 Spots are outliers in the 5 genomic profile Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 8 / 19
R packages description MANOR: an algorithm to detect spatial bias Neuvial et al., BMC Bioinformatics, 2006 Abnormal Log-Ratio in the 1 corner Spatial trend estimation by 2 2D-LOESS Spatial segmentation 3 Bias area are removed 4 Spots are outliers in the 5 genomic profile Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 8 / 19
R packages description MANOR: an algorithm to detect spatial bias Neuvial et al., BMC Bioinformatics, 2006 Abnormal Log-Ratio in the 1 corner Spatial trend estimation by 2 2D-LOESS Spatial segmentation 3 Bias area are removed 4 Spots are outliers in the 5 genomic profile Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 8 / 19
R packages description MANOR: an algorithm to detect spatial bias Neuvial et al., BMC Bioinformatics, 2006 Abnormal Log-Ratio in the 1 corner Spatial trend estimation by 2 2D-LOESS Spatial segmentation 3 Bias area are removed 4 Spots are outliers in the 5 genomic profile Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 8 / 19
R packages description GLAD: Gain and Loss Analysis of DNA Hupé et al., Bioinformatics, 2004 Profile segmentation The GLAD algorithm aims at identifying chromosomal regions with identical DNA copy number. Log-Ratio profile 1 Smoothing line estimation 2 Breakpoint detection 3 Status assignment 4 Outliers detection 5 It works with BAC array, cDNA array, oligonucleotide array (Affymetrix, Agilent, Nimblegen, Illumina) Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 9 / 19
R packages description GLAD: Gain and Loss Analysis of DNA Hupé et al., Bioinformatics, 2004 Profile segmentation The GLAD algorithm aims at identifying chromosomal regions with identical DNA copy number. Log-Ratio profile 1 Smoothing line estimation 2 Breakpoint detection 3 Status assignment 4 Outliers detection 5 It works with BAC array, cDNA array, oligonucleotide array (Affymetrix, Agilent, Nimblegen, Illumina) Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 9 / 19
R packages description GLAD: Gain and Loss Analysis of DNA Hupé et al., Bioinformatics, 2004 Profile segmentation The GLAD algorithm aims at identifying chromosomal regions with identical DNA copy number. Log-Ratio profile 1 Smoothing line estimation 2 Breakpoint detection 3 Status assignment 4 Outliers detection 5 It works with BAC array, cDNA array, oligonucleotide array (Affymetrix, Agilent, Nimblegen, Illumina) Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 9 / 19
R packages description GLAD: Gain and Loss Analysis of DNA Hupé et al., Bioinformatics, 2004 Profile segmentation The GLAD algorithm aims at identifying chromosomal regions with identical DNA copy number. Log-Ratio profile 1 Smoothing line estimation 2 Breakpoint detection 3 Status assignment 4 Outliers detection 5 It works with BAC array, cDNA array, oligonucleotide array (Affymetrix, Agilent, Nimblegen, Illumina) Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 9 / 19
R packages description GLAD: Gain and Loss Analysis of DNA Hupé et al., Bioinformatics, 2004 Profile segmentation The GLAD algorithm aims at identifying chromosomal regions with identical DNA copy number. Log-Ratio profile 1 Smoothing line estimation 2 Breakpoint detection 3 Status assignment 4 Outliers detection 5 It works with BAC array, cDNA array, oligonucleotide array (Affymetrix, Agilent, Nimblegen, Illumina) Hupé et al. (Institut Curie, Paris, France) Analysis of DNA copy number experiments INSERM workshop, 2009 9 / 19
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