Reading and Pre-Processing Microarrays

 Data processing of Placental Microarrays  Dr. Hugo A. Barrera Saldaña  Paper in Mol. Med  Search PubMed for Trevino V

Placenta 1 Placenta 2 mRNA Extraction Reference Pool Labelling Microarray Hybridization (by duplicates) Scanning & Data Processing Detection of Differentially Expressed Genes Validation and Analysis Green Red t-test  H 0 : µ = 0 p-values correction: False Discovery Rate Comparison With Known Tissue Specific Genes Image Analysis Within Normalization (per array) Between Normalization (all arrays) (controls) (Dr. Hugo Barrera)

a b cd Placenta/ReferenceControl/Control

(a) Microarray Experiment Ratio (log 2 ) Placenta (b) T1dbase T1 score 1 0 Lung Thalamus Amygdala Spinal Cord Testis Kidney Liver Pituitary Thyroid Cerebellum Hypothalamus Caudate Nucleus Exocrine Pancreas Lymph Node Frontal Cortex Stomach Breast Bone Marrow Pancreatic Islets Uterus Ovary Skin Heart Skeletal Muscle Prostate Thymus Salivary Gland Trachea Placenta 2 Replcate 2 Placenta 2 Replicate 1 Array: Placenta 1 Replicate 1 Placenta 1 Replicate 2

Data downloaded from URL: dyes, 2 slides per assay (each containing different probes, same sample in both slides, oligo or cDNA arrays ?). 48 grids, 24x24 spots 2..grd files contain the "initial grid" specification for the slides 3..adf files contain the "annotations" of the genes. 4.Files: 51,52,53,54,55,56. 5xa is the slide 1 and 5xb the slide 2 of each assay. 5.Some assays use the same rna sample (techincal replicates). See table in next slide. 6.One dye is Placental RNA and the other is a reference pool of different organs RNA GOALS: 1.Detect Differential Expressed Genes 2.Focus on Placental Specific Genes (growth hormone family?) Contact: Dr. Hugo A. Barrera Saldana (81) ext. 2871, 2872, 2587 (81) (particular), (mobile) Secretario de Investigacion, Regulacion y Vinculacion

SLIDES' SCANNINGS GROUPSLIDECY3 (GREEN)CY5(RED)COMMENTS 1a52 AVSampleControl 1b52 BVSampleControl 2a51 AVSampleControl RIGHT TOP GROUP 2b51 BVSampleControl RIGHT BOTTOM GROUP 3a56 AVControlMuestra 3b56 BVControlMuestra 4aA 54VControlMuestra 4bB 54VControlMuestra 5aA 55VControl LEFT TOP GROUP 5bB 55VControl LEFT BOTTOM GROUP 6aA 53VControl 6bB 53VControl Pending Questions: 1)Slides from group 1 and 2 should be 52 and 51, which is which? 2)Are the slides from Group 5 and 6 Control vs Control? 1)In which case we have only 2 independent samples 3)Group 5 should be slide 55, A and B, isn't?

 Download and use SpotFinder from TM4 Suite   Download Images (51.zip or 55.zip from  Read BOTH Images together using SpotFinder  Mark file 1 as "Cy3" = Green  Mark file 2 as "Cy5" = Red  Create Grid  Metarows = 12, Metacolumns = 4  Rows = 24, Columns = 24  Pixels = 450 (of the 24 x 24 spots)  Spacing = 18 (between metacolumns and metarows)  Adjust each of the 24 Grids to correct positions  Right mouse button in a grid  Right mouse button in a blank section to move all grids  Save the grid

 Use Gridding and Processing  Adjust (save grid first, in mac adjust doesn´t work well)  Process  Copy images  1 From the grid adjust  1 From the RI plot  1 From the data (figure)  2 From the QC view (A and B)  What does they represent?  Export to.mev file  Open.mev file in excel  Remove comment lines  Compute signal:  Signal A = Cy3 Green = MNA - MedBkgA = Media del spot A - Mediana del fondo B  Signal B = Cy5 Red = MNB - MedBkgB = Media del spot B - mediana del fondo B  Plot Signal A vs Signal B  Copy image in a word file  DO NOT SAVE THE modified.MEV FILE

 Upload.mev file to google groups identifying the Slide name and team  Next week, we will process all your uploaded data for processing

 UID  IA  IB  R  C  MR  Print-tip Normalization  MC  Print-tip Normalization  SR  SC  FlagA  FlagB  SA  SF  QC  QCA  QCB  BkgA  BkgB  SDA  SDB  SDBkgA  SDBkgB  MedA  MedB  MNA  Signal Ch. A = Cy3 [Green]  MNB  Signal Ch. B = Cy5 [Red]  MedBkgA  Background Ch. A  MedBkgB  Background Ch. B  X  Y  PValueA  PValueB

 Block  Print-tip Normalization  Column  Row  Name  ID  X  Y  Dia.  F635 Median  F635 Mean  F635 SD  B635 Median  B635 Mean  B635 SD  % > B SD  % > B SD  F635 % Sat.  F532 Median  F532 Mean  F532 SD  B532 Median  B532 Mean  B532 SD  % > B SD  % > B SD  F532 % Sat.  Ratio of Medians  Ratio of Means  Median of Ratios  Mean of Ratios  Ratios SD  Rgn Ratio  Rgn R²  F Pixels  B Pixels  Sum of Medians  Sum of Means  Log Ratio  Flags  Normalize  F1 Median - B1  F2 Median - B2  F1 Mean - B1  Signal - Background  F2 Mean - B2  Signal - Background  SNR 1  F1 Total Intensity  Index  "User Defined"

  MIDAS TM4

  Project  New  Read Data  Single Data File  Specify your.mev file  Oper  Normalization  LOWESS  Write Output  No virtual  Execution Reports  PDF

click, right-button, plot

 only ~ 9,000 data generated for 54a  Output is different Spotfinder+Midas Chipskipper + R (Bioconductor) This problem exemplify that the right software + right parameters is needed for each experiment (ChipSkipper was designed by the microarray slide provider).

51a.txt

51b.txt

56a.txt

56b.txt

52a.txt

Same Sample?? Same Image?? Same Scan?? 52b.txt

55A.txt controls

55B.txt controls

53A.txt controls

53B.txt controls

54a.txt

54b.txt

 2 independent samples  51a+52a, 54a+56a  51b, 54b+56b (52b has problems)  It seems that no bias is present per subgrid (not shown)  Raw values will be used (no-normalised)

g51a a bit different to g52a g52a seems to be more "noisy" 54a and 56a looks more correlated in both g and r (This is was computed normalizing each channel independently)

Averages = [Log(Cy3) + Log(Cy5)] / 2

M (ratios) = Log("Cy5" / "Cy3") = Log(Sample/Reference)

GENES SELECTED SLIDES A: (t-test vs mean=0) fdr <= 10% fold >= 2

 Lun pm  Juev 24

"AND" Maru Perla