Presentation on theme: "AFGC Damares C Monte Carnegie Institution of Washington,"— Presentation transcript:
1 DNA Microarray Data Acquisition and Analysis - Introduction to Stanford Microarray Database AFGCDamares C MonteCarnegie Institution of Washington,Plant Biology DepartmentStanford, CA
2 DNA Microarray Technology AFGCCy5: ~650 nmCy3: ~550 nmWalk through exact method of hybridizing and making probe for microarrays. Discuss the CY Dyes used and current scanning technology.No differential expressionInducedRepressed
3 Data Analysis Acquisition - QC Input/Storage/Retrieval Analysis/Pattern RecognitionVisualizationInterpretation/AnnotationPublication/RepositorySMDBiologist
4 Fluorescence Intensities Extraction To extract data from a microarray by accurately identifying the location of each of the spots.Data extracted on: Fluorescence intensities, background intensities, fluorescence intensities ratios.
5 Load ImagesAdjust Gain (brightness of image) and Normalization (balance between images in red and green)
6 Create Grids Create new grid Number of grids to create Numbers of rows and columnsSpot width and heightColumn and Row spacingResolution (Xres and Yres)Tip spacingCreate grid
8 What gets flagged? SMD Overlay Cy3 Cy5 Dust speck Saturated in both channelsDoughnutSaturated on outside & in the middle doughnutSaturated in one channelStreakDust/Background/saturation/something coming into spot
11 Mean Intensity plotsBroad distribution pattern with curved or flattened arms. Common to have gap in the center. (Crab Claw)Concentrated cluster of spots in a linear or fan pattern with clearly distinguished outliers
13 Median signal to background (How strong the signal is compared to background) Mean of median background (Determines how high is the background)Median signal to noise (Confidence to quantify peak signal to background)(F635Med - B635Med)/B635SD
14 Data Analysis Flow New Scan Gene Pix Clustering Quality ControlClusteringStanford Microarray Database - SMDData SelectionQuality ControlComplete Data Table (cdt)XMLDownload
20 Comparison plotThis plot can be effectively used to get a measure of the overall consistency between two duplicated or reversibly duplicated experiments. It compares the log2(RAT2N) values of the two experiments and calculates the regression line, which optimally should be close to one. Future functions will include filters and plotting of multiple experiments.
23 Why do we use log space? Exponential distribution of intensities Most genes at low levelVery few high levelLog scale approx. normal distribution??
24 Frequency of intensity levels This plot allows the user to plot the frequency of spots within certain intensity intervals for the red and green channel respectively. It is possible to use normalized or non-normalized intensity values and linear or logarithmic scales. This graph will make it easier to determine background levels for filtering when using other analysis tools. The two examples below give a good view of what normalization does to the data.
26 Distribution of ratios This plot draws the distribution of the logarithm (base 2) of the red/green ratios [log2(RAT2N)]. It gives an immediate overview of the range of expression and the normality of the distribution.
40 Things to be Considered When Analyzing Microarray Data 1. Clones corresponding to spots with intensities of at least 350 in both channels andratios of 2.0 / 0.5 are worth further investigation.2. For genes of interest, check that the spot(s) is acceptable.3. Check for consistency among replicate values.4. Validate important results obtained from the microarray data by an independent test or method.@pilot