smd data analysis tutorial april 7, 2009 catherine ball ([email protected]) janos demeter...

SMD Data Analysis Tutorial

April 7, 2009

Catherine Ball

([email protected])

Janos Demeter ([email protected])

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:[email protected]

You will learn…• How to use SMD Data Analysis Pipeline

– Gene Selection and Annotation– Data Filtering– Data Retrieval– Gene Filtering– Clustering and Image Generation

• How to use SMD’s data repository• How to use SMD’s implementation of

the GenePattern data analysis suite

Data Retrieval and Analysis

• Experiment names will be listed with feature extraction software indicated.

Gene Selection and Annotation

• Specify genes or clones

• Collapse data by SUID or LUID

• Determine UID column

• Choose biological annotation

• Label result set

Gene Selection: All genes

• Ten arrays• All genes• 8690 Biosequence IDs used

in cluster• Using all genes results in a

very long cluster!

Gene Selection: Specify Genes or Clones

• Use all genes or clones on an array• Select a Genelist from your loader.stanford.edu account• Enter a list of genes to select. The names should be

separated by two colons• Optionally include controls and empty spots.

Gene Selection: Genelists

• Ten arrays• 500-gene genelist• 380 Biosequence IDs used

for cluster• Using a genelist limits the

data analyzed to a subset of genes

Gene Selection: Retrieving and Collapsing Data

• Collapse or averaging occurs within each individual array. Multiple instances of the same entity will be combined as specified.

• Duplicated entities can be defined in three ways:• Biosequence ID is the identifier for the molecule in SMD.• Laboratory Unique ID is the identifier for the source of the

sample in the lab. • SPOT is a individual feature on a print.

Gene Selection: Collapse by SUID• Ten arrays• 500 gene genelist• Data retrieved by

Biosequence ID• 380 Biosequence IDs used

for cluster• duplicated spots will be

averaged

Gene Annotation: Biological Annotation

• The list includes all information stored within SMD for any gene from the organism in question. Not all genes will have all annotations.

• Annotations from a genelist (if one was selected) can be used to describe the genes.

Array Annotation: Name Choices

• Arrays (hybridizations) are identified in SMD by slide name (e.g., serial number) and experiment name, both unique.

• Agilent and Affymetrix data sets are further identified by a “result set” name – possibly more than one per hybridization, and not guaranteed to be unique.

SMD Data Analysis Tutorial• How to use SMD Data Analysis Pipeline




Data Filtering• Choose data column to

retrieve• Elect to invert reverse dye

replicates• Elect to filter by spot flag• Select spot criteria for

filtering• Define image presentation

options• Retrieve data in

background (not shown) - goes to repository

Data Filtering: Choose Data to Retrieve

• You can retrieve and cluster any numerical measurement from your data.

• Clustering doesn’t necessarily make sense for all fields.

• Default (and most appropriate) fields for clustering are log ratio (two-channel data) and signal or intensity (single-channel data).

Data Filtering: Selecting Filtering Criteria

• Each spot will be individually assessed as specified, prior to any averaging or collapse.

• Each filter can be made active and customized as desired.• Filters can be combined using logical operators (filter string),

defaulting to a logical AND.• Filters available will be appropriate to the feature extraction

software used.

Data Filtering: Default Spot Filters• Regression correlation measures pixel-by-pixel agreement

between the two channels.• Foreground/Background intensities are a simple measure of

signal to noise.• Absolute intensity cutoffs impose a minimum net signal.• “Failed” and “Is Contaminated” refer to the quality of the spot

material.• Equivalent defaults are presented for Agilent data.• Affymetrix data can be filtered on detection, detection p-value,

etc.• Any data, including biological annotations, can be used for

customized filters.

Spots with low regression correlation

Data Filtering: Regression Correlation• Ten arrays• 500 gene Genelist• Spot flag = 0• Regression correlation > 0.6• 380 Biosequence IDs used for filtering• Filtering away spots with low

regression correlation removes many spots

Data Filtering: Combinations of Filters• Ten arrays• 500-gene genelist• Regression correlation > 0.6• Net intensity in each channel >= 350• 371 Biosequence IDs selected for

clustering• This data set was formed by

selecting spots that are good quality (via the regression correlation) and good intensity in both channels

Data Filtering: Image Presentation Options

• Retrieve spot coordinates will allow you to see an assembled image of each array after clustering.

• Show all spots allows you to view the spots you filtered out (in addition to the ones that passed filtering) after clustering. This might slow down data retrieval.

Data Filtering: Retrieve Data in Background

• Long running data retrieval jobs can be submitted and you’ll be e-mailed with a progress report.

• Data sets will be saved to your data repository.

Data Retrieval

• General results and progress

• PreClustering (.pcl) file

• Data retrieval summary report

• Option to deposit data in repository

…….

Data Retrieval Summary

Gene Filtering

• Transform single-channel data

• Filter genes based on data distribution

• Data centering• Filter genes based on

data values• Filter genes and arrays

based on spot filter criteria

• Zero-transform data

Gene Filtering: Transformation

• Single-channel (e.g., Affymetrix) data only.• Adjust arrays for simple cross-array

comparison.• Log-transform data for clustering.

– May add a constant for variance stabilization– May replace non-positive values with very small

values

Gene Filtering: Data Distribution

• Rank will select genes whose retrieved value is in the top Nth percentile.

• Deviations selects those genes whose retrieved value has a value significantly above or below the mean.

Gene Filtering: Percentile Rank

• Ten arrays• 500-gene genelist• Regression correlation > 0.6• Net intensity in either channel >=

350• Rank > 95% in at least one array• Many data are removed, since

only those that were very intense in the yellow (red) channel are included.

Gene Filtering: Deviation from Mean Value

• Ten arrays, 500-gene genelist• Regression correlation > 0.6• Net intensity in either channel >= 350• Genes whose Log(Normalized

Red/Green) is more than one standard deviation from mean in at least one array

• This filter removes data that do not show significant variance from the mean – a good way to identify genes with potentially interesting behavior.

Gene Filtering: Centering Data

• Data can be centered at this stage. This transforms the data so that the mean value is equal to zero. Images and downloaded files will reflect this transformation.

• During clustering, data can be treated as if they were centered, but the values of the data are not affected.

• Gene centering is useful for common references.• Array centering amounts to renormalizing each array,

using the spots that pass the spot filter criteria.

Data Centering

• Centering sets the average value of a vector to zero.

• This results in a loss of information, but may reveal important patterns.

-1.5

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Array

log(ratio)

Data Centering

• Gene centering is useful when the actual value of the ratio is not important or is not meaningful (e.g., common reference).

• Centering is generally not appropriate when using a biologically meaningful control sample, such as a matched, untreated sample, or a zero timepoint.

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Array

log(ratio)

Data Transformation: Centering

• To illustrate how centering affects data, a small sample of data were duplicated. A constant was added to the second copy of each row

Uncentered Data, No Centering Metric During Clustering

Uncentered Data, Centering Metric During Clustering

Centered Data, No Centering Metric During Clustering

Centered Data, Centering Metric During Clustering

Gene Filtering: Center Genes• Ten arrays, 500-gene genelist• Regression correlation > 0.6• Net intensity in either channel >=

350• Genes centered • No effect on number of

biosequence IDs clustered, but data values are changed (centered data is displayed on left)

Centered Uncentered

Gene Filtering: Data Values

• Cutoff requires data to exceed a user-defined value in at least A arrays. Think hard before using this filter. Especially when data are centered, you could be losing important information.

Gene Filtering: Spot Filter Criteria

• Genes can be screened out if they do not meet the spot criteria a given percentage of the time, as specified by the user.

• Arrays can be similarly filtered out if they do not meet the spot filter criteria.

Spot Filtering vs. Gene FilteringSpot filters remove individual data points. That means there will be more missing (gray) data.

Gene filters remove the genes that do not meet the filter criteria often enough. This reduces the number of genes.

Gene Filtering: Zero Time Point Transformation

• Data can be transformed by subtracting one state of a series from all other data

Gene Filtering: Zero Time Point Transformation

Subtract the values

from the first time

point from all the other

time points

Gene Filtering: Results

• Download PreClustering files (.pcl)

• Go to GenePattern• Summary report• Deposit to repository• Another round of

filtering• Proceed to clustering

Gene Filtering: Data Retrieval Summary Report

Clustering and Image Generation

• Partitioning options• Clustering metric

selections• Correlated genes• Image generation

options

Clustering Algorithms

In microarray studies, we often use clustering algorithms to help us identify patterns in complex data.

For example, we can randomize the data used to represent this painting and see if clustering will help us visualize the pattern.

Clustering algorithms

The painting is “sliced” into rows which are then randomized.

Rows ordered by hierarchical clustering with nodes flipped to optimize ordering

Clustering algorithms

How do we compare expression profiles?

• Treat expression data for a gene as a multidimensional vector.

• Decide on a distance metric to compare the vectors.– Plenty to choose from…

• Pearson correlation, Euclidean Distance, Manhattan Distance etc.

Similar expression

• Crucial concept for understanding clustering

• Each gene is represented by a vector where coordinates are its values (log(ratio)) in each experiment

• x = log(ratio)expt1

• y = log(ratio)expt2

• z = log(ratio)expt3

• etc.

Expression Vectors

x

y

z

Clustering: Metric Selections

• Genes and arrays can be clustered.

• Pearson correlation treats vectors as if they were the same (unit) length.

• Euclidean distance will be affected by both the direction and the amplitude of the vectors.

• Distances are measured “between” expression vectors

• Distance metrics define the way we measure distances

• Many different ways to measure distance:• Euclidean distance• Pearson correlation coefficient(s)• Manhattan distance• Mutual information• Kendall’s Tau• etc.

• Each has different properties and can reveal different features of the data

Distance Metrics

Euclidean Distance

• The Euclidean distance metric detects similar vectors by identifying those that are closest in space. In this example, A and C are closest to one another.

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ARRAY 1

ARRAY 2Gene B

Gene A

Gene C

Pearson Correlation

• The Pearson correlation disregards the magnitude of the vectors but instead compares their directions. In this example, Gene A and Gene B have the same slope, so would be most similar to each other.

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ARRAY 1

ARRAY 2Gene B

Gene A

Gene C

Distance Metric: Pearson vs. Euclidean

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Array

Log Ratio

• By Euclidean distance, A and C are most similar.• By Pearson correlation, A and B are most similar.

A

B

C

Clustering: Tree Displays

• Clustered gene arrays are displayed adjacent to most similar arrays.

• The nodes of the trees indicate the members of an array and the degree of similarity to its neighbor.

Hierarchical Clustering

1. Calculate the distance between all genes. Find the smallest distance. If several pairs share the same similarity, use a predetermined rule to decide between alternatives.

2. Fuse the two selected clusters to produce a new cluster that now contains at least two objects. Calculate the distance between the new cluster and all other clusters.

3. Repeat steps 1 and 2 until only a single cluster remains.4. Draw a tree representing the results.

G1G6

G3

G5

G4

G2

G1

G6

G3

G5

G4

G2

Clustering: Array ClusteringNo Array Clustering

With Array Clustering

Clustering: Self Organizing Maps

• Map of n partitions, that is modeled on the expression data, where each partition in the map has an associated vector

• Genes are assigned to partitions of most similar genes

• Neighboring partitions are more similar to each other than they are to distant partitions

Clustering: Correlated Genes

• SMD can produce a file listing the best-correlated genes, for each gene retrieved.

Clustering: Visualization• Click on the image to get a

dynamic display.• Click on the TreeView button

for another dynamic option.• Click on one of the other

options to see static displays with or without the spot images.

• Download files (.cdt, .atr, .gtr, report) for use with other tools (e.g., TreeView).

• Add cluster or pre-clustering file to your repository

Clustering Display: Adjacent Cluster and Clustered Spot Images

Clustering Display: Hierarchical Cluster View

• Interactive view of cluster

• Link to GO term analysis (green nodes) to evaluate sub-clusters.

SMD Data Analysis• Using SMD Data Analysis Pipeline• Repository Tools

– SVD– Synthetic Gene Tool– kNNimpute

• GenePattern tools

SMD Help: File Formats

File Formats: Pre-clustering (PCL) File

UID is the Unique Identifier for the Spot/Reporter

NAME sequence label for the Spot/Reporter

GWEIGHT indicates the weight the Spot/Reporter is given in clustering

Names and orders of arrays (if arrays are not clustered)

EWEIGHT indicates the weight the Array/Experiment is given in clustering

Values are for each spot/reporter on each array (usually log ratios)

File Formats: Clustered Data Table (CDT) File

File Formats: Gene Cluster Text (GCT) File

File Formats: Class (CLS) File

Using Your Repository: PCL Deposits

View information about your repository entry

Download data

Delete the repository entry

Edit the entry

Cluster data

Filter data

Apply SVD to data

Apply “Synthetic Genes” to data

Estimate missing data with KNN impute

Use GenePattern tools

Using the Repository: CDT File Options

CDT files have a few other options

GeneXplorer

TreeView

Clustering with Proxy images

Clustering with Spotimages

Clustering with Proxy and Spot images

Viewing Repository Entries

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

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

SMD Data Analysis• Data Analysis Background

– Clustering algorithms– Data centering

• Using SMD Data Analysis Pipeline– Gene Selection and Annotation– Data Filtering– Data Retrieval– Gene Filtering– Clustering and Image Generation

• Repository Tools– SVD– Synthetic Gene Tool– kNNimpute


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

SVD:method

SVD Display in SMD




• Repository Tools– SVD– kNNimpute– Synthetic Gene Tool


KNNImpute: The Missing Values Problem

• Microarrays can have systematic or random missing values

• Some algorithms aren’t robust to missing values

• Large literature on parameter estimation exists

• What’s best to do for microarrays?

KNNimpute Algorithm• Idea: use genes with similar expression

profiles to estimate missing values

2 | 4 | 5 | 7 | 3 | 2

2 | | 5 | 7 | 3 | 1

3 | 5 | 6 | 7 | 3 | 2

Gene X

Gene B

Gene C

j

2 | 4 | 5 | 7 | 3 | 2

2 |4.3| 5 | 7 | 3 | 1

3 | 5 | 6 | 7 | 3 | 2

Gene X

Gene B

Gene C

j




• Repository Tools– SVD– kNNimpute– Synthetic Gene Tool


Synthetic Genes• Purpose:

average data based on arbitrary groupings of genes/probes

- for biological reasons

- for technical reasons• Can average data using:

- common genelists

- your own genelists

- annotations in pcl file• After averaging:

- a new row for the synthetic gene data

- Original data can be removed/included

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)

– Entrez Geneid (same as above, but for Entrez Geneid)

These lists are useful to collapse data by gene, rather than biosequenceid/luid.

They allow comparison of experiments between different platforms - oligo print to cDNA print or spotted arrays to Agilent arrays where the arrays don’t share common reporters. Also can be used to compare cDNA prints with h/meebo arrays

These synthetic gene lists are updated on a regular basis.

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

http://smd.stanford.edu/help/synthGenes.shtml








SMD Data Analysis• Using SMD Data Analysis Pipeline• Repository Tools

– SVD– Synthetic Gene Tool– kNNimpute


What is GenePattern?

Software package developed at Broad Institute (Jill P. Mesirov’s group):

http://www.broad.mit.edu/cancer/software/genepattern/

Reasons to choose this package:• Large number of microarray analysis

tools (>90)• Ability to create pipelines (reproducible

research)• Ease of adding new modules to existing

ones





How to find GP in SMD?From Data retrieval

From repository

Terms in GenePattern

• Module (Analysis/Visualization/Utility): program that does analysis, displays or executes some other transformation of a file

• Pipelines: chained modules - output from one -> input to next

• Suites: groupings of modules/pipelines• Jobs:

– execution of module/pipeline– persistent – results are deleted after one week

• Go to web-site for navigation

GenePattern comments

• SMD uses pcl• GenePattern uses gct (among others )• Converters gct -> pcl; pcl -> gct• Most tools in GenePattern need full dataset - Use

ImputeMissingValuesKNN first• Most default values are designed for Affymetrix data -

evaluate each option carefully (GeneCruiser)

Input/output files in GenePattern

• Called through specific pcl file• Files in your repository• Upload data from desktop• Any file that has a url• Module to get data directly from geo

• Clustering:– Hierarchical clustering/HierarchicalClusteringPCL– Self-organizing maps (SOM)– K-means clustering – Non-negative matrix factorization (NMF) (Brunet et al., 2004) is an alternative method for class

discovery. Rather than clustering genes, NMF detects context-dependent patterns of gene expression. Requires all positive values.

– Consensus clustering (Monti et al., 2003) runs a selected clustering algorithm against perturbations of the original data set. The result is a consensus matrix that assesses the stability of discovered clusters. Supported clustering methods: hierarchical clustering, K-means clustering, self-organizing maps (SOM), and non-negative matrix factorization (NMF).

• Clusters genes or samples, not both

– SubMap (Hoshida Y, et al. PLoS ONE 2(11): e1195, 2007 ) is an unsupervised method, which estimates the significance of an association between subclasses observed in two independent data sets. The subclass labels are predetermined as manually assigned phenotypes or by clustering prior to the application of the SubMap algorithm.

• Corresponding visualizers:– HierarchicalClusteringViewer– SOMClusterViewer– HeatMapViewer

– Etc…• Clustering result example

GenePattern modules by category: Clustering

• ComparativeMarkerSelection (similar to SAM)– ComparativeMarkerSelectionViewer:

Visualize and explore data produced by the method

– ExtractComparativeMarkerResults: extract data based on the analysis, create genelist

• Gene Set Enrichment Analysis (GSEA)– GSEALeadingEdgeViewer

GenePattern modules by category: Marker Selection

Goal: Given phenotypically distinct classes,distinct classes, find markers with distinct expression distinct expression patternspatterns (in different classes)

GenePattern modules : Marker Selection

ScoreScore

Measure of Measure of confidence/significanceconfidence/significance

Reject/Accept Reject/Accept criterioncriterion

?

YesYes

NoNo

Marker Marker candidatecandidate

ss

DatasetDataset

PhenotypePhenotype

Visualize result using ComparativeMarkerSelectionViewer

GenePattern modules: Marker Selection

GenePattern modules: GSEA

ScoreScore

Measure of Measure of confidence/significanceconfidence/significance

Reject/Accept Reject/Accept criterioncriterion

?YesYes

NoNoMarker Marker

candidatcandidateses

DatasetDataset

PhenotypePhenotype

• a method to determine whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states

• Very similar to comparative marker selection: sets rather than genes


Molecular Signatures DB http://www.broad.mit.edu/gsea/msigdb/index.jsp

Gene sets: groups of gene symbols

Gene sets are versioned

http://www.broad.mit.edu/gsea/msigdb/index.jsp






• Requirements:– Expression dataset– Class file:

– Chip file:


Number of slides

Number of classes


How it works:• Sorts rows based on how well a metric correlates with the class assignment (similar to marker selection tool)• Scores gene sets (using a scoring method) by walking down the ranked list of genes, increasing a running-

sum statistic when a gene is in the gene set and decreasing it when it is not.

• The enrichment score is the maximum (or minimum) value of the running sum.• Permutes labels to come up with FDR

• Output results can be viewed in web-browser

• Further analysis: GSEALeadingEdgeViewer


• Pipeline can be created from a path• by concatenating individual modules• From zip file• Pipelines can be exported• Pipelines can be private or public

Create pipeline/suite

• Smd curators/programmers can create/upload new modules

• If you have any programs you would like to share or use in smd, please let us know

Creating new modules

• Goal: Given phenotypically distinct classes, find a gene expression signature that accurately predicts class membership.

• Computational methodology: divide data into training and test sets

• Goal: – achieve high predictive power– Avoid over-fitting

GenePattern modules : Class Prediction

Expression DataExpression Data Known ClassesKnown Classes

Assess Gene-Class Correlation

Feature Selection

Assess Gene-Class Correlation

Feature Selection

Build ClassifierBuild Classifier

Test Classifier by Cross-Validation

Test Classifier by Cross-Validation

Evaluate Classifier on Independent Test SetEvaluate Classifier on Independent Test Set

Naïve Bayes/Large Bayes

Weighted Voting

k-Nearest Neighbors

Support Vector Machines

Logistic Regression

Classification Trees

LDA, QDA, …

• Simple example:Knn classifier,k=5, 2 genes, 2 classes


project samples in gene space

gene 1gene 1

gene

2ge

ne 2

class orange

class black



gene 1gene 1

gene

2ge

ne 2

class orange

class black

project unknown sample

?



gene 1gene 1

gene 2

gene 2

class orange

class black

"consult" 5 closest neighbors:- 3 black- 2 orange Distance measures:

• Euclidean distance• 1-Pearson correlation• KL divergence• …

?


• Evaluation on Evaluation on independent test set test set– Build the classifier on the train settrain set.– Assess prediction performance on test set.test set.

• Maximize generalization/Avoid overfitting.

• Performance measurePerformance measure

error rate=# of cases correctly classified

total # of cases

• K-nearest-neighbors (KNN) classifies an unknown sample by assigning it the phenotype label most frequently represented among the k nearest known samples (Golub and Slonim et al., 1999). In GenePattern, the user selects a weighting factor for the 'votes' of the nearest neighbors (unweighted: all votes are equal; weighted by the reciprocal of the rank of the neighbor's distance: the closest neighbor is given weight 1/1, next closest neighbor is given weight 1/2, and so on; or weighted by the reciprocal of the distance).

• Weighted Voting (Slonim et al., 2000) classifies an unknown sample using a simple weighted voting scheme. Each gene in the classifier 'votes' for the phenotype class of the unknown sample. A gene's vote is weighted by how closely its expression correlates with the differentiation between phenotype classes in the training data set.

• Support Vector Machines (SVM) is designed for multiple class classification (Rifkin et al., 2003). The algorithm creates a binary SVM classifier for each class by computing a maximal margin hyperplane that separates the given class from all other classes; that is, the hyperplane with maximal distance to the nearest data point. The binary classifiers are then combined into a multiclass classfier. For an unknown sample, the assigned class is the one with the largest margin.

• CART (Breiman et al., 1984) builds Classification And Regression Trees for predicting continuous dependent variables (regression) and categorical predictor variables (classification). It works by recursively splitting the feature space into a set of non-overlapping regions and then predicting the most likely value of the dependent variable within each region. A classification tree represents a set of nested if-then conditions that allows for the prediction of the value of the categorical dependent variable based on the observed values of the feature variables. A regression tree is similar but allows for the prediction of the value of a continuous dependent variable instead.


• ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) (Margolin, A., et al., BMC Bioinformatics, 2006. 7(Suppl 1): p. S7.) is an algorithm which reverse engineers a gene regulatory network from microarray gene expression data. It attemps to predict targets of select transcription factors from a microarray dataset.

• MINDY (Modulator Inference by Network Dynamics) algorithm computationally infers genes that modulate the activity of a transcription factor at post-transcriptional levels (Wang, et. al. ,2006). The algorithm uses mutual information (MI) to measure the mutual dependence of the transcription factor (TF) and its target gene to predict modulators of TF activity.

GenePattern modules: Pathway Analysis

• SurvivalCurve: Draws survival curve based on cls file

• SurvivalDifference: tests if there is a difference between two or more survival curves based on sample classes defined by genomic data. The log-rank test (Mantel-Haenszel test) and the generalized Wilcoxon test can be used.

GenePattern modules: Survival Analysis

QuickTime™ and a decompressor

are needed to see this picture.

• Many other modules:– Projection methods: PCA, NMF (Non-

negative Matrix Factorization)– Tools for snp analysis– Tools for proteomics data– Etc…

GenePattern modules

SMD: Office Hours

• Grant S201• Mondays 3 -

5 pm• Wednesdays

2 - 4 pm

SMD StaffGavin SherlockCo-Investigator

Catherine BallDirector

Janos DemeterComputational Biologist

Tatiparthy ReddyScientific Curator

Heng JinScientific Programmer

Patrick BrownCo-InvestigatorFarrell Wymore

Lead ProgrammerMichael NitzbergDatabase Administrator

Zac ZachariahSystems Administrator

Maria MaoSoftware Engineer

Jeremy HubbleScientific Programmer

smd data analysis tutorial april 7, 2009 catherine ball ([email protected]) janos demeter...

Documents

gene genelist data

data collapse

data column

gene annotation

smd data analysis tutorial

smds data repository

repository slide

affymetrix data sets