Standard Data Analysis Report Agilent Gene Expression Service

Transcription

1 Standard Data Analysis Report Agilent Gene Expression Service Experiment: S Date: Prepared for: Dr. Researcher Genomic Sciences Lab Prepared by

2 S Standard Data Analysis Report Agilent Gene Expression Service 1 I. Report Content Customer Information Array and Sample Information Results RNA Sample QC RNA Labeling and Hybridization Microarray Design and QC Data Pre-Processing Probe Annotation Data Analysis Differential Expression Analysis Cluster Analysis Gene Ontology and Pathway Analysis Statistical Enrichment Analysis Advanced Data Analysis References II. Customer Information Customer Name Institute Telephone Fax Dr. Researcher Genomics Sciences Lab XXX-XXX-XXXX XXX-XXX-XXXX Address Service Code S Date 2011/01/01 Array type The array was custom designed and manufactured by Agilent Technologies

3 S Standard Data Analysis Report Agilent Gene Expression Service 2 III. Array and Sample Information Item Requisition Total RNA Sample Number 24 Sample Type RNA Extraction method Single / Dual Channel Array Type Cell Trizol (Invitrogen) Single (CY3) Custom-commercial(earray),8*15K Total Array Amount 24 Hybridization Protocol see manufacturer's Protocol Array Content Sample_ID Microarray ID group Sample _1_1 1 Sample _1_2 1 Sample _1_3 1 Sample _1_4 2 Sample _2_1 2 Sample _2_2 2 Sample _2_4 8

4 S Standard Data Analysis Report Agilent Gene Expression Service 3 IV. Results RNA Sample QC RNA quantity and purity was assessed using NanoDrop ND Acceptance criteria indicating acceptable RNA purity are set at A260/A and A260/A for absorbance ratios. RIN values are generated using Agilent RNA 6000 Nano assay to determine RNA integrity. Acceptance criteria indicating acceptable RNA integrity is set at 7 for RIN value. Sample ID Total Amount (ug/µl) A260/ A280 >1.8 A260/ A230 > RIN >7 Result Sample PASS Sample PASS Sample PASS Sample PASS RNA Labeling and Hybridization Cy3 labeling was performed for RNA amplified from each population. Hybridization to 8*15K custom genome microarray gene expression chips (Agilent Technologies) was conducted following the manufacturer s protocol. Microarray chips were then washed and immediately scanned using a DNA Microarray Scanner (Model G2565BA, Agilent Technologies). Microarray Design and QC Non-control probes are replicated up to ten times across each Agilent array. Probe replicates enable calculation of % CV (percent of the coefficient of variation) for each array. The CV can be used as a measure of quality of the array and it can help to detect a sample that deviates from the rest as an erroneous one. A lower median CV indicates better reproducibility across the array. median CV(%) samples <10 Sample Sample Sample Sample

5 S Standard Data Analysis Report Agilent Gene Expression Service 4 Check Items gnegctrlavenetsig gnegctrlavebgsubsig Control-probes %CV Non-control-probes %CV Technical reproducibility Description negative control probes s average signal negative control probes s background signal Positive and negative control probes s % CV value, A lower % CV indicates better reproducibility of the array Non-control probes s % CV, A lower % CV indicates better reproducibility of the array Pearson s correlation coefficient between technical replicates. Acceptance Criteria (Test Value) Result <40 (28.88) OK -10 to 5 (3) OK <10 (4.14) OK <10 (3.28) OK >0.99 (0.995) OK

6 S Standard Data Analysis Report Agilent Gene Expression Service 5 Data Pre-Processing Microarray data were extracted using Agilent Feature Extraction (AFE) Software (v ) available from Agilent, using the default variables. Outlier features on the arrays were flagged by the same software package. The raw data files were pre-processed using the Linear Models for Microarray Data (limma) package developed within the Bioconductor project in the R statistical programming environment. After log transformation, the data were normalized using quantile method. The AFE software attaches a flag to each feature that identifies different quantification errors of the signal. These quantification flags can be used to filter out signals that don t reach a minimum arbitrary criterion of quality. The data were filtered to 1) keep features within the dynamic range of the scanner and 2) retain features of good quality. To keep features within the dynamic range, we demanded that, for every replicated spot across the whole set of samples, at least 75% of the replicated probes in at least one experimental condition had a quantification flag denoting the signal as within the dynamic range. To retain good quality features for the analysis, we filtered out, for each replicated spot across the whole set of samples, those probes for which more than 25% of the replicates in at least one experimental condition had a flag indicating the presence of outliers. After pre-processing, 11,476 probes were left for statistical analysis. Probe Annotation Probes were mapped to Arabidopis genes by blasting against Arabidopsis transcripts from The Arabidopsis Information Resource collection and all sequences (all downloaded from TAIR, 2011/6/15). In total, all probes were perfectly matched to transcripts (1e-5) probes were specifically mapped to 366 transcripts which were not linked to an annotated gene. Those unmapped probes were excluded in this analysis. Category Term num_of_probes probes GO_BP GO: ~cellular process 6801 GO_BP GO: ~metabolic process 5964 GO_BP GO: ~cellular metabolic process 5443 GO_BP GO: ~primary metabolic process 4583 GO_BP GO: ~macromolecule metabolic process 3228

7 S Standard Data Analysis Report Agilent Gene Expression Service 6 PROBEID Target ID BLAST ID Blast.e.value BLAST Description Species CUST_13842_PI cdna_contig1348 AT1G E-103 pyrophosphorylase 1 Arabidopsis thalia CUST_8626_PI contig03294 AT1G E-39 Homeodomain-like superfamily protein Arabidopsis thalia

8 S Standard Data Analysis Report Agilent Gene Expression Service 7 V. Results Differential Expression Analysis The limma package ranks significantly differentially expressed (DE) genes in infected vs. noninfected cells for each of the time points by fitting a linear model to the data set. From the limma output, log-fold changes from one condition compared with another and B-statistic (log-odds that a gene is differentially expressed) can be obtained and used to determine up- and down-regulation of genes. Fold changes of 1.5 and B-statistics >0 for probe sets were used as cut-offs. Item Comparison up-regulated down-regulated 1 2 VS VS VS 1 3 VS logfc Item 1 (Group2 vs Group1) AveExpr t P.Value adj.p.val B gene_id symbol CUST_1_PI E E AT1G66970 SVL2 CUST_10_PI E E AT2G01570 RGA;RGA1 CUST_10000_PI E E AT1G14920 GAI;RGA2

9 S Standard Data Analysis Report Agilent Gene Expression Service 8 Cluster Analysis The clustering is performed on the differentially expressed gene sets (426 genes). Hierarchical clustering using Euclidean distances for both samples (column, expressed as mean of replicates) and genes (probe sets) showing differential expression (B stat > 0, FC >1.5 ). The heat map diagram shows the result of the two-way hierarchical clustering of genes and samples. Each row represents an mrna and each column represents a sample. The mrna clustering tree is shown on the left, and the sample clustering tree appears at the top. The color scale shown at the top illustrates the relative expression level of a mrna: red color represents a high expression level; blue color represents a low expression level.

10 S Standard Data Analysis Report Agilent Gene Expression Service 9 Gene Ontology (GO) and Pathway Analysis Enrichment analysis of Gene Ontology terms was performed for every transcript DE at any of the time points. The GOstats package for R was used to compute the hyper geometric test for significance. Each list of DE transcripts at each time point was tested against the total list of transcripts in our analysis after filtering out transcripts acting as controls, transcripts showing little variation across samples, and transcripts without GO or pathway annotation. Enrichment in KEGG biological pathways was performed using WebGestalt (WEB-based GEne SeT AnaLysis Toolkit). Inclusion in KEGG is limited to the proteins/genes with defined roles in biological processes. There are 8431 genes that had KEGG annotations, and a total of 9728 had GO categories. The KEGG pathways are not entirely separate from one another. One example of this is the Glycolysis / Gluconeogenesis pathway, which is a constituent component in several other biological pathways, such as the Citrate Cycle pathway. Within the pathways of the KEGG database there are multiple references to other KEGG pathways. Statistical Enrichment Analysis List Hits - the number of genes annotated by the considered category or annotation cluster within the analyzed list of target genes List Total - the number of genes within the analyzed list of target genes having at least one annotation Population Hits - the number of genes, available on the entire microarray, annotated by the considered category or annotation cluster Population Total - the number of genes available on the entire microarray and having at least one annotation P-value - the significance p-value of the gene enrichment of the considered category or annotation cluster, calculated with hyper geometric test Term 1 (BP/CC/MF/KEGG*) List Hits List Total Pop. Hits Pop. Total P value ath00010:glycolysis / Gluconeogenesis < ath00020:citrate cycle (TCA cycle) < ath00030:pentose phosphate pathway <0.0001

11 S Standard Data Analysis Report Agilent Gene Expression Service 10 Graphs for GO and Pathway Analysis

12 S Standard Data Analysis Report Agilent Gene Expression Service 11 Advanced Analysis X Gene set: Group 2 Vs Group 1 Group 3 Vs Group 1 s diff_genes; Y Gene set: Group 4 Vs Group 1 Group 5 Vs Group 1 s diff_genes; Z Gene set: Group 7 Vs Group 6 Group 8 Vs Group 6 s diff_genes; X Y Z Probeid TargetID gene set 7 CUST_10018_PI contig CUST_10020_PI contig CUST_10037_PI contig CUST_10075_PI contig Category Term number_of_probes probes GO_BP GO: ~metabolic process 114 CUST_14331_PI , GO_BP GO: ~cellular process 64 CUST_3197_PI , GO_BP GO: ~primary metabolic process 64 CUST_13326_PI , GO_BP GO: ~cellular metabolic process 51 CUST_3197_PI ,

13 S Standard Data Analysis Report Agilent Gene Expression Service 12 VI. References 1. Agilent. Agilent Feature Extraction Reference Guide, R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria. 3. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004; 5(10): R Smyth GK. Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry RA, Huber W (eds). Bioinformatics and Computational Biology Solutions using R and Bioconductor. Springer: New York, 2005, pp Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004; 3: Article3. 6. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 1995; 57: