Supplementary Material

You can get selected datasets that have been collected, analyzed, and made available by investigators at MD Anderson. You can also get the specific supplements, which may include figures, tables, data, and/or analysis code for manuscripts published by researchers at MD Anderson:

Chemosensitivity and Cell Lines
Supplementary data in support of the paper: Deriving Chemosensitivity from Cell Lines: Forensic Bioinformatics and Reproducible Research in High-Throughput Biology, by Baggerly KA and Coombes KR, Annals of Applied Statistics 2009; 3(4):1309-1334.
Alternative Splicing in Glioblastoma
Supplementary datasets in support of the paper: Global Analysis of Aberrant pre-mRNA Splicing in Glioblastoma Using Exon Expression Arrays, by Cheung HC, Baggerly KA, Tsavachidis S, Bachinski LL, Neubauer VL, Nixon TJ, Aldape KD, Cote GJ, Krahe R. These include CEL files for 24 glioblastoma samples (around 550Mb zipped), CEL files for 12 normal brain samples (around 266Mb zipped), and a text file with file names and tumor/normal status (1K).
Topographical Normalization
Supplementary dataset, containing code and data in support of the paper: Topographical Normalization of Reverse Phase Protein Arrays, by Neeley ES, Baggerly KA, Kornblau SM.
RPPA Data for Variable Slope Normalization
Supplementary dataset, containing code and clinical and RPPA protein data in support of the paper: Variable Slope Normalization of Reverse Phase Protein Arrays, by Neeley ES, Kornblau SM, Coombes KR, Baggerly KA.
RPPA Data in AML
Supplementary dataset, containing clinical and RPPA protein data on 256 newly diagnosed AML patients, in support of the paper: Functional Proteomic Profiling of AML Predicts Response and Survival, by Kornblau SM, Tibes R, Qiu YH, CHen W, Kantarjian H, Andreeff M, Coombes KR, Mills GB. Blood, to appear.
Run Batch Effects and Ovarian Cancer
Complete source code and results for the analysis described in: Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer, by Baggerly KA, Neeley ES, Coombes KR. J Clin Oncol. 2008; 26(7):1186-1187.

Dressman et al. replied to our correspondence. Our own reply-to-their-reply wil be posted on our addendum to our first web site.

(Ir)reproducibility of chemopredictors
Complete source code and results for the analysis described in: Microarrays: retracing steps , by Coombes KR, Wang J, Baggerly KA. Nature Medicine. 2007; 3(11):1276-1277. (Formerly known as: Genomic signatures based on the NCI60 cell lines do not predict patient response to chemotherapy.)

Potti and Nevins replied to our correspondence. Our own reply-to-their-reply can be found on our addendum to our first web site.

Microenvironment Gene List
Supplementary table for: Gene Expression Profile of Metastatic Human Pancreatic Cancer Cells Depends on the Organ Microenvironment, by Najamura T, Fidler IJ, Coombes KR. Cancer Res. 2007; 67:139-48.
Wavelet-Based Functional Mixed Models
Supplementary material, including description of Metroplis-Hastings and some MCMC trace plots, for "Wavelet-based Functional Mixed Models" by Morris and Carroll.
UniGene Affiliation
Supplementary material for Gene Sequence Signatures Revealed by Mining the UniGene Affiliation Network, by Zhang J, Zhang L, Coombes KR. Bioinformatics. 2006; 22:385-91
Validation by QRT-PCR on Low-Density Arrays
Supplementary figures and data Validation of oligonucleotide microarray data using microfluidic low-density arrays: a new statistical method to normalize real-time RT-PCR data, by Abruzzo LV, Lee KY, Fuller A, Silverman A, Keating MJ, Medeiros LJ, Coombes KR. Biotechniques. 2005; 38:785-92
List of Significant Peaks in Pancreatic Cancer
Supplementary Table S1 for: Plasma Protein Profiling for Diagnosis of Pancreatic Cancer Reveals the Presence of Host Response Proteins. by Koomen JM, Shih LN, Coombes KR, Li D, Xiao LC, Fidler IJ, Abbruzzese JL, Kobayashi R. Clin Cancer Res. 2005; 11:1110-1118.
Peak detection for MALDI using the average spectrum
Supplementary material for Feature Extraction and Quantification for Mass Spectrometry Data in Biomedical Applications Using the Mean Spectrum, by Morris JS, Coombes KR, Kooman J, Baggerly KA, and Kobayashi R. Bioinformatics 2005; 21:1764-75.
Diagnostic Protein Discovery using LCMS for Proteolytic Peptide Targeting
Supplementary material for : Diagnostic Protein Discovery using Proteolytic Peptide Targeting and Identification, by John M. Koomen, Haitao Zhao, Donghui Li, James L. Abbruzzese, Keith A. Baggerly, Ryuji Kobayashi. Rapid Communications in Mass Spectrometry, 2005; 18:2537-48..
Reproducibility of SELDI-TOF
Supplementary material, including Matlab and Perl code and additional Figures to accompany Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments, by Baggerly KA, Morris JS, Coombes KR. Bioinformatics 2004; 20:777-85
Signal in Noise
Supplementary material, including Matlab scripts and processed data to accompany Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer, by Baggerly KA, Edmonson SR, Morris JS, Coombes KR. Endocr Relat Cancer. 2004; 11:583-4
Processing Proteomics Spectra, Version 1
Supplements to: Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization, by Coombes KR, Fritsche HA Jr, Clarke C, Chen JN, Baggerly KA, Morris JS, Xiao LC, Hung MC, Kuerer HM. Clin Chem. 2003; 49:1615-23
Between Library Variation in SAGE
Supplements to: Differential expression in SAGE: accounting for normal between-library variation, by Baggerly KA, Deng L, Morris JS, Aldaz CM. Bioinformatics. 2003; 19:1477-83.
CAMDA 2001 Supplement
Supplementary material for our presentation at CAMDA 2001
Description of the PCANOVA method for evaluating the amount of group structure present in microarray data.
Supplementary material for the EGFR.signature.

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