SMD Data Quality Assessment and Repository Tools Tutorial

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SMD Data Quality Assessment and Repository Tools
Tutorial

• November 10, 2007
• Catherine Ball
• Janos Demeter

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SMD Getting Help

• Click on the Help menu
• Tool-specific links will be listed at the top.
• Use the SMD help index to look for specific
  subjects
• Send e-mail to
• array_at_genome.stanford.edu

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Quality Assessment and Repository Tools Tutorial

• Quality Assessment Tools
• Ratios on Array
• HEEBO/MEEBO plots
• Graphing tool
• Q-score
• Repository
• Repository
• SVD
• Synthetic Gene Tool
• kNNimpute

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SMD Data Repository Help

• How to use the tool
• Limitations of file sizes
• Sharing data
• Options
• Links to help for analysis methods, data file
  formats, data retrieval and clustering

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SMD Help File Formats
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File Formats Pre-clustering (PCL) File
Names and orders of arrays (if arrays are not
clustered)
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File Formats Clustering Design Tree (CDT) File
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SMD Data Repository

• What is the SMD Data Repository?
• What is the repository?
• Using the repository to save or upload data
• Using the repository to share data
• Using the repository to analyze data
• Options for PCL files via the repository
• View
• Data
• Delete
• Edit
• Cluster
• Filter
• SVD
• Synthetic Genes
• KNN Impute
• Options for CDT files via the repository
• GeneXplorer
• TreeView
• View Clusters, spots

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What is the SMD Repository?

• A method to save data sets to prevent repeatedly
  performing the same data retrieval
• A method to share processed data with others
• A way SMD can provide you with access to new
  and/or computationally-intensive tools

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Accessing the SMD Data Repository
Here!
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SMD Data Repository
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Uploading files to Repository

• If uploading clustered data, enter CDT files
• If uploading pre-clustering data, enter PCL
  files
• Choose an organism
• Give a unique name to your data set
• Provide a useful description to your data set

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Using Your Repository CDT Deposits

• View cluster using GeneXplorer or TreeView
• View cluster images
• View retrieval and clustering report
• Download files
• Assign access

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Using Your Repository PCL Deposits
Apply Synthetic Genes to data
Edit the entry
Filter data
Estimate missing data with KNN impute
Download data
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Using the Repository CDT File Options
CDT files have a few other options
GeneXplorer
Clustering with Proxy and Spot images
TreeView
Clustering with Spotimages
Clustering with Proxy images
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Viewing Repository Entries

• Name
• Organism
• Number of genes
• Number of arrays
• Size of file
• Date uploaded
• Description
• Data retrieval summary

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Downloading Repository Entries

• Downloading puts file(s) into a folder labeled
  with your SMD user name onto your computers
  desktop

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Deleting Repository Entries

• Details about your repository entry
• Asks you to confirm before deleting!

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Editing Entries -- How to Share!

• Change repository entry name
• Change description
• Add access to repository entry to a GROUP
• Add access to a repository entry to a SMD USER

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Filtering Data in Repository Entries

• If your repository entry is a PCL file, you can
  re-enter the SMD filtering pipeline

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SVD Singular Value Decomposition

• The goal of SVD is to find a set of patterns that
  describe the greatest amount of variance in a
  dataset
• SVD determines unique orthogonal (or
  uncorrelated) gene and corresponding array
  expression patterns (i.e. "eigengenes" and
  "eigenarrays," respectively) in the data
• Patterns might be correlated with biological
  processes OR might be correlated with technical
  artifacts

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SVD The Concept (easy version)

• Lets imagine we have a three-dimensional cigar,
  as shown in A
• We can represent this in one dimension, by
  looking at its lengthwise shadow (B)
• Looking at its cross-wise shadow (C), we get an
  orthogonal view of the cigar that tells us more
  about the three-dimensional object than B alone.

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SVD Missing Data Estimation

• Some algorithms (such as SVD) cannot operate with
  missing data
• You can use this simple method or you can use
  KNNImpute to estimate missing data

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SVD Display in SMD
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SVD Raster Display

• Each row represents an eigengene -- an
  orthogonal representation of the genes in the
  dataset
• The topmost eigengene contributes the most to the
  data set

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SVD View Projection

• Clicking on a row in the Raster Display brings
  you the Projection View
• You can select genes that have high and low
  contributions from an eigengene and download them
  in a PCL file
• In this way, you might use SVD to help classify
  subtypes

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SVD Eigenexpression

• Each bar show the probability of expression of
  each eigengene
• You can compare the probabilities to see which
  eigengenes contribute more to the overall view
  of the data

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SVD Plot selected eigengenes

• You can plot as many or as few eigengenes as you
  like
• This plot gives you an easy-to-understand view of
  the behavior of each eigengene

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Synthetic Genes

• Purpose
• average data based on arbitrary groupings of
  genes
• - for biological reasons
• - for technical reasons
• Can average data using
• - common genelists
• - your own genelists
• After averaging
• - a new row for the synthetic gene data
• - Original data can be removed/included

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Synthetic Genes

• Common lists available (only mouse and human
  data)
• Unigene (all clones/oligos that report on a given
  Unigene id will be averaged and shown as the
  Unigene id)
• LocusLink (same as above, but for LocusLink id)
• These lists are useful to collapse data by gene,
  rather than suid/luid.
• They allow comparison of experiments between
  different platforms - oligo print to cDNA print
  or spotted arrays to Agilent arrays where the
  arrays dont share common suids. Also can be used
  to compare cDNA prints with h/meebo arrays
• These synthetic gene lists are updated on a
  regular basis.

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Synthetic Genes

• Other common synthetic gene lists
• chromosome arms
• cytobands
• 5 Mb tiles based on GoldenPath mappings
• Tissue types
• tumor types
• processes
• Additional lists see
• http//smd.stanford.edu/help/synthGenes.shtml

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Synthetic Genes

• You can use your own genelists
• 1 genelist for each synthetic gene
• Name of the genelist is the synthetic genes name

• - tab-delimited text file
• File must have header (NAME, WEIGHT)
• NAME contains cloneid
• WEIGHT can be -1 to 1 (weight of clone
• during averaging)
• - Can have comment lines (start with )

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Synthetic Genes

• Tool only works on pcl files in repository
• During data retrieval the include UIDs option
  should not be used
• After collapsing, file can be downloaded, added
  to your repository, and/or clustered
• Currently works only for human and mouse data

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Synthetic Genes/Merge PCL Files

• Related tool Merge PCL Files
• On main page (lists menu -gt all programs) under
  tools section
• Can be used to combine 2 pcl files from different
  sources into a single pcl file.
• Cloneids that belong to the same gene can be
  combined into single row (based on a translation
  file provided).

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Synthetic Genes/Merge PCL Files
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Synthetic Genes/Merge PCL Files

• Same experiments in the pcl files can be averaged
• Averaging method can be mean/median
• Translation file
• Tab-delimited text file
• First column desired final identifier
• Second column desired final annotation
• Third and subsequent columns identifiers (first
  column of a pcl file) in the pcl files that
  should be collapsed to the identifier in the
  first column.
• Data for identifiers not included in the
  translation file will not be collapsed

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KNNImpute The Missing Values Problem

• Microarrays can have systematic or random missing
  values
• Some algorithms arent robust to missing values
• Large literature on parameter estimation exists
• Whats best to do for microarrays?

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Why Estimate Missing Values?

 
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KNNimpute Algorithm

• Idea use genes with similar expression profiles
  to estimate missing values

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Clustering Cluster Image

• Scale is indicated on the color bar
• Gene names are at the right
• Tree generated by hierarchical clustering is at
  the left

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Clustering Display Clustered Spot Images

• Spot images can also be viewed in a clustered
  image
• This can give you a visual impression of the data
  that are the basis of your analysis

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Clustering Display Adjacent Cluster and
Clustered Spot Images
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GENEXPLORER
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TREEVIEW
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SMD Getting Help

• Click on the Help menu
• Tool-specific links will be listed at the top.
• Use the SMD help index to look for specific
  subjects
• Send e-mail to
• array_at_genome.stanford.edu

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Quality Assessment and Repository Tutorial

• Quality assessment tools
• Ratios on Array
• H/Meebo plots
• Graphing tool
• Q-score
• Repository
• Repository
• SVD
• Synthetic Gene Tool
• kNNimpute

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Ratios on Array Tool

• Accessible from the display data -gt view data
  pages
• Ratios on array

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Ratios on Array Tool

• Quick visualization of log-ratio distribution on
  the slide
• Color assignments are based on log-ratio values
  and also intensity
• Can visualize normalized or non-normalized
  log-ratios
• PLUS ANOVA analysis to detect spatial bias
  (print-tip or plate)

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Ratios on Array Tool

• Not normalized vs. normalized (loess intensity,
  print-tip)

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Ratios on Array Tool

• One way ANOVA to test dependence of log-ratios on
  print-tip and printing plate
• F-statistic is given for the hypothesis no bias
  in data
• In the example, normalization significantly
  improved print-tip bias

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HEEBO/MEEBO plots
Single experiment
Batch access

• HEEBO/MEEBO quality assessment graphs from
  BioConductor package
• If you used doping controls on the slide, the
  graphs are automatically generated during
  experiment loading
• Accessible from
• For single experiment display data -gt view data
  pages
• For batch from main page, under tools
• You can create new graphs or look at existing
  ones
• Help page http//smd.stanford.edu/help/arrayQuali
  ty.shtml

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HEEBO/MEEBO plots

• Can be used for a gpr file uploaded from desktop
  - print has to be present in SMD and oligo_ids in
  the id/name column
• In batch for a result set list on
  loader.stanford.edu
• If called for a specific experiment, the values
  are already filled in.
• Normalization options available from limma. Note
  this normalization will NOT change data stored in
  SMD, only used for generating graphs
• Background subtraction methods - same story as
  normalization
• Job is placed in the job-queue - email is sent
  with link

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HEEBO/MEEBO plots

• Can be used for a gpr file uploaded from desktop
  - print has to be present in SMD and oligo_ids in
  the id/name column
• In batch for a result set list on
  loader.stanford.edu
• If called for a specific experiment, the values
  are already filled in.
• Normalization options available from limma. Note
  this normalization will NOT change data stored in
  SMD, only used for generating graphs
• Background subtraction methods - same story as
  normalization
• Job is placed in the job-queue - email is sent
  with link

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HEEBO/MEEBO plots

• Can be used for a gpr file uploaded from desktop
  - print has to be present in SMD and oligo_ids in
  the id/name column
• In batch for a result set list on
  loader.stanford.edu
• If called for a specific experiment, the values
  are already filled in.
• Normalization options available from limma. Note
  this normalization will NOT change data stored in
  SMD, only used for generating graphs
• Background subtraction methods - same story as
  normalization
• Job is placed in the job-queue - email is sent
  with link

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HEEBO/MEEBO plots
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HEEBO/MEEBO diagnostics

• MA-plots before and after normalization
• A 1/2(log2(Cy5) log2(Cy3))
• M log2(Cy5 / Cy3)
• Loess lines are shown for sectors if print-tip
  normalization was selected
• Distribution should be centered around M0, with
  no intensity dependence

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HEEBO/MEEBO diagnostics

• Distribution of ranked log-ratios (M-values) on
  slide, before and after normalization
• Spatial distribution of non-normalized A-values

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HEEBO/MEEBO diagnostics

• Histograms of signal-to-noise ratios for Cy5
  (upper) and Cy3 (lower) channels
• Histogram for all probes (probe) and curves for
  subgroups (doping, negative, positive controls
  and actual probes)

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HEEBO/MEEBO diagnostics

• Box-plots for groups of reporters (colors same as
  on previous)
• A-values without background subtraction
• Normalized M-values for positive/negative
  controls (should be around 0 for type 1
  experiment)

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HEEBO/MEEBO doping controls

• Amount of doping control (DC) vs. observed
  fluorescence intensity
• Expected sigmoid curve
• Additional graphs for individual DCs

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HEEBO/MEEBO doping controls

• non-normalized Cy5 vs. Cy3 signal intensity (log2
  scale) (background corrected if selected)
• DCs with same ratio should fit line parallel to
  diagonal
• Log-ratio increases from top left to bottom right

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HEEBO/MEEBO doping controls

• Observed vs. expected log-ratios (normalized and
  bg corrected) for each doping control group
• Ratios should be aligned on the diagonal
• Graphs for individual doping controls as well

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HEEBO/MEEBO mismatch and tiling controls

• Mismatch and tiling probes are used for 2 tests
• Assess integrity of sample (degradation) - tiling
  probes
• Degree of cross-hybridization - mismatch probes
• Mutations are anchored (at the extremities) or
  distributed (along transcript)
• Calculated binding energies vs. normalized (i.e.
  divided by median of corresponding wild type
  probes) raw intensities

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HEEBO/MEEBO mismatch and tiling controls

• Percent mismatch vs. log2 intensity for anchored
  (blue) and distributed (green) probes
• Wild-type probe on left (red box-plot) and
  negative controls on the right (red box-plot)
• Right axis fraction of all A-values

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HEEBO/MEEBO mismatch and tiling controls

• Tiling probes were designed along the transcript
• Non-normalized signal intensities (Cy5 and Cy3)
  vs. probes distance from 3-end
• Quick drop in signal indicates problem in sample
  (degradation/ivt)

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Graphing Tool
or
histograms

• Can be accessed directly from display data page
  or from view data page.
• It allows you to create graphs of any two data
  columns in linear or log space
• Can be applied for individual experiment or in
  batch for experiment set
• Interactive tool

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Graphing Tool

• In batch mode (for experiment set) it can be
  configured to work on a subset of the experiments
  in the set.

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Graphing Tool

• Can create scatter plots or histograms
• Can transform data to log space
• Wide selection of data columns to choose from
• Combine with data filter to look at distribution
  of subset of the data

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Graphing Tool

• Can create scatter plots or histograms
• Can transform data to log space
• Wide selection of data columns to choose from
• Combine with data filter to look at distribution
  of subset of the data

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Graphing Tool

• Can create scatter plots or histograms
• Can transform data to log space
• Wide selection of data columns to choose from
• Combine with data filter to look at distribution
  of subset of the data

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Graphing Tool Filter selection

• Data filters should be customized for the data
  retrieved.
• Graphing tool helps in filter selection and
  finding a cut-off value

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Graphing Tool Filter selection

• Plot filter field (here regression correlation)
  against test field (log ratio).
• Log ratios should center around 0.
• Here, the log ratios appear to diverge below a
  regression correlation of about 0.4 - 0.6.

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Graphing Tool Filter selection

• Foreground / Background (log scale) plotted
  against log-ratio
• Data should center around a log ratio of zero
• Impose cutoff at 2.0 (linear, 0.3 log10) to
  eliminate flare at low relative intensity.

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Graphing Tool Filter selection

• As intensity decreases, the log(ratio) tends to
  scatter
• Spots with low intensities might seem falsely
  significant
• A cut-off value of 250 (28) is suggested for
  Ch2 normalized net

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Q-score Tool

• Tool to use for filter and cut-off value
  selection
• Currently usable for cDNA slides (uses UNIGENE
  clusterid), for human and mouse (will be extended
  to HEEBO/MEEBO arrays)
• Still in experimental stage
• Simple idea pool reporters that belong to same
  gene, calculate their spread and combine values
  for each gene into score for whole array gt
  Q-score
• Filtering that removes bad quality spots should
  decrease spread of measurements for genes, hence
  improve (decrease) Q-score

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Q-score Tool

• Works in batch for a group of slides (from same
  print) in a result set list
• Requires a genelist that specifies which
  reporters to use. Common genelists for human and
  mouse clusterids
• Filters and their ranges need to be defined.
• Log-ratio mean/median is used to calculate
  Q-score
• Run from the job-queue, email is sent to user
  with the link

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Q-score Tool

• Output is a set of graphs showing the fraction of
  reporters not filtered out and the corresponding
  Q-scores at increasingly stringent filter values
• Cut-off values (if found) saved in a new result
  set list for data retrieval

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SMD Office Hours

• Grant S201
• Mondays 3 - 5 pm
• Wednesdays 2 - 4 pm

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SMD Staff
Gavin Sherlock Co-Investigator
Catherine Ball Director
Patrick Brown Co-Investigator
Farrell Wymore Lead Programmer
Michael Nitzberg Database Administrator
Catherine Beauheim Scientific Programmer
Zac Zachariah Systems Administrator
Janos Demeter Computational Biologist
Heng Jin Scientific Programmer
Takashi Kido Visiting Scholar
Don Maier Senior Software Engineer