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AFGC Damares C Monte Carnegie Institution of Washington,

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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
AFGC Damares C Monte Carnegie Institution of Washington, Plant Biology Department Stanford, CA

2 DNA Microarray Technology
AFGC Cy5: ~650 nm Cy3: ~550 nm Walk through exact method of hybridizing and making probe for microarrays. Discuss the CY Dyes used and current scanning technology. No differential expression Induced Repressed

3 Data Analysis Acquisition - QC Input/Storage/Retrieval
Analysis/Pattern Recognition Visualization Interpretation/Annotation Publication/Repository SMD Biologist

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 Images Adjust 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 columns Spot width and height Column and Row spacing Resolution (Xres and Yres) Tip spacing Create grid


8 What gets flagged? SMD Overlay Cy3 Cy5 Dust speck
Saturated in both channels Doughnut Saturated on outside & in the middle doughnut Saturated in one channel Streak Dust/Background/saturation/something coming into spot



11 Mean Intensity plots Broad 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 Control Clustering Stanford Microarray Database - SMD Data Selection Quality Control Complete Data Table (cdt) XML Download






20 Comparison plot This 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 level Very few high level Log 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.

25 Frequency of intensity levels

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.








34 Clustering and Image Generation Page


36 Spot Data Page




40 Things to be Considered When Analyzing Microarray Data
1. Clones corresponding to spots with intensities of at least 350 in both channels and ratios 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








48 Acknowledgements Shauna Somerville Katrina Ramonell Lorne Rose
Bi-Huei Ho Sue Thayer Shu-Hsing Wu

49 Acknowledgements Shauna Somerville
Bench team Katrina Ramonell Lorne Rose Bi-Huei Ho Sue Thayer Shu-Hsing Wu Bioinformatics team David Finkelstein Jeremy Gollub Fredrik Sterky Rob Ewing Lalitha Subramanian Mira Kaloper Todd Richmond Mike Cherry



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