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Session ii g3 overview behavior science mmc
1. Theme: Transcriptional Program in the Response of Human
Fibroblasts to Serum.
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
Etienne.gnimpieba@usd.edu
2. Data manipulation Gene expression data analysis
OMIC World
DNA
E
DNA
mRNA
E
Degradation
Degradation
Translation
Transcription
Gene
Repression
S P
Catalyse
Genomics
Functional
Genomics
Transcriptomics
Proteomics
Metabolomics
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
3. Data manipulation Gene expression data analysis
OMIC World
GENOMICS
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
4. Data Manipulation Gene Expression Data Analysis
OMIC World
Genomics is the sub discipline of genetics devoted to the
mapping,
sequencing ,
and functional
analysis of genomics
Genomics can be said to have appeared in the 1980s, and took off in the 1990s
with the initiation of genome projects for several biological species.
The most important tools here are microarrays and bioinformatics
DNA microarrays allow for rapid measurement and visualization of differential
expression between genes at the whole genome scale. If technique implementation is
quite complicated, it’s principle is very easy. Here are described the major steps
involved in this process
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
5. Data Manipulation Gene Expression Data Analysis
Process
Biological question
Differentially expressed genes
Sample class prediction etc.
Testing
Biological verification
and interpretation
Microarray experiment
Estimation
Experimental design
Image analysis
Normalization
Clustering Discrimination
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
6. Data Manipulation Gene Expression Data Analysis
Process
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
7. Data Manipulation Gene Expression Data Analysis
Microarray Production Process
High density
filters(macroarrays)
Glass slides (microarrays) Oligonucleotides chips
Detail: Detail: Detail:
Size: 12cm x 8cm Size: 5,4cm x 0,9cm Size: 1,28cm x 1,28cm
•2400 clones by membrane
•radioactive labelling
•1 experimental condition by
membrane
•10000 clones by slide
•fluorescent labelling
•2 experimental conditions
by slide
•300000 oligonucleotides by
slide
•fluorescent labelling
•1 experimental condition by
slide
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
8. Data Manipulation Gene Expression Data Analysis
Microarray Production Process
• Frouin, V. & Gidrol, X. (2005)
• CBB group (Berlin)
• Transcriptome ENS (France) Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
Expression Profile Clustering:
Slide Scanning:
Target Preparation:
Hybridization:
9. Data Manipulation Gene Expression Data Analysis
Microarray Production Process
• Frouin, V. & Gidrol, X. (2005)
• CBB group (Berlin)
• Transcriptome ENS (France)
• Image analysis (genepix)
• Normalization (R)
• Pre-treatment
• Differential expression
• Clustering
• Data mining
• Annotation
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
10. Data Manipulation Gene Expression Data Analysis
Excel Used in Genomics
• Frouin, V. & Gidrol, X. (2005)
• CBB group (Berlin)
• Transcriptome ENS (France)
• How to select columns
• How to use functions
• How to anchor a cell value in a function
• How to copy the function result and not the
function itself
• How to sort data by columns
• How to search and replace
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
11. Data Manipulation Gene Expression Data Analysis
Excel Used in Genomics: Pre-Treatment
• Frouin, V. & Gidrol, X. (2005)
• CBB group (Berlin)
• Transcriptome ENS (France)
1. Open the file containing the experiment series (your expression matrix)
in Excel software, using the tabulation character as the column separator.
2. For one column (corresponding to one DNA microarray experiment),
calculate the mean value, using the MEAN Excel function. Verify that the
value obtained is equal to zero.
3. If it is not the case, remove from each experiment log2(Ratio) value the
corresponding mean value. Be careful, for missing values (empty cells),
replace empty contents by the NULL or NA string, in order to avoid
introducing a zero value in Excel calculation in this cell. Indeed, a
missing value is different from a true null one!
4. Once this operation has been done, verify that the final mean value is
equal to zero, this in order to avoid errors with Excel handling. Be careful,
with decimal separator handling in Excel version (dot or coma)!
Centering and Scaling Data
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
12. Data Manipulation Gene Expression Data Analysis
Excel Used in Genomics : Differential Expression Analysis (1)
• Frouin, V. & Gidrol, X. (2005)
• CBB group (Berlin)
• Transcriptome ENS (France)
Significance Analysis of Microarrays (SAM):
SAM is an Excel macro freely available for academics on the web. The use of SAM in Excel spreadsheet
makes this tool easier to use for most of microarray users. Using SAM implies several modifications in
your data file:
The ratio or intensity values in the Excel sheet must not contain any comas but only points as
decimal separator.
The header line depends on the type of analysis you want to perform. You can refer to SAM
manual for more information. So you must duplicate your header if you don’t want to loose the
experiment information (see image below).
Two annotation columns are available. SAM always references its calculation to the line number
in the departure sheet.
SAM (Significance Analysis of Microarray), Excel macro allowing to search for differentially expressed
genes using a bootstrapping method. Website: http://www-stat.stanford.edu/~tibs/SAM/
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
13. Data Manipulation Gene Expression Data Analysis
Excel Used in Genomics : Differential Expression Analysis (2)
• Frouin, V. & Gidrol, X. (2005)
• CBB group (Berlin)
• Transcriptome ENS (France)
When the SAM macro is launched in the tool bar (“SAM”), a setting window appears. For further
information on the various options you can choose, the best is to refer to the SAM manual. However,
the first important things to do is to indicate if the data source has been transformed in log2 or not,
then, as data bootstrapping uses a random generator, you need to initialize it several times by
creating a various number of seeds.
Once all the chosen iterations have been done, SAM displays a plot representing each gene thanks to
its score in the real distribution compared to the random distributions. Therefore, the differentially
expressed genes are the ones moving away from the 45° slope line.
First, display the delta table. This table indicates for each delta value, the number of putative
differentially expressed genes, the significant genes, and the number of false positive genes
estimated using the False Discovery Rate (FDR). The user fixes the delta value according to the
number of false positive or significant genes he wants to obtain.
To choose the delta value, get back to the SAM plot sheet and display the “SAM plot controller” by
clicking on the SAM macro button.
The SAM Plot Controller window lets you fix the delta value you want: “Manually Enter Delta”. Then if
you select the “List Significant Genes” button, SAM displays the list of differentially expressed genes
in the “SAM output” sheet according to the delta value you chose.
This sheet summarizes the selected parameters and gives you the list of induced and repressed
genes.
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
14. Data Manipulation Gene Expression Data Analysis
GEPAS: Gene Expression Pattern Analysis Suite
• Frouin, V. & Gidrol, X. (2005)
• CBB group (Berlin)
• Transcriptome ENS (France)
Verify the availability of the data file in your folder name
FibroGEPAS.txt
Open the dataset for description
Open GEPAS portal on
http://www.transcriptome.ens.fr/gepas/index.html
Click on “Tools”
Preprocessing
- Preprocess DNA array data files: log-transformation,
replicate handling, missing value imputation, filtering and
normalization
- Filtering
Viewing
Clustering
Differential expression
Classification
Data mining
Etienne Z. Gnimpieba
BRIN WS 2013
Mount Marty College – June 24th 2013
16. • Gene Expression Measurement
• Microarray Process
• Gene Expression Data Stores
• Data Mining / Querying
• Data Analysis
• Example: ATP13A2 Profile in Stress
Conditions
17. Gene Expression Measurement
Gene
expression
technologies
Microarray
process
Gene expression
data stores
Data mining /
quering (pb-
query-
extraction-load-
store-pretreat)
Data analysis
(Question-
Answer,
descriptive,
predictive,
modeling)
Example:
ATP13A2 profile
in stress
conditions
Higher-plex techniques:
SAGE
DNA microarray
Tiling array
RNA-Seq
NGS
Low-to-mid-plex techniques:
Reporter gene
Northern blot
Western blot
Fluorescent in situ hybridization
Reverse transcription PCR
18. Database
Microarray
Experiment
Sets
Sample
Profiles
Date Reported
ArrayExpress at EBI 24,838 708,914 October 28, 2011
ArrayTrack™ 1,622 50,953 February 11, 2012
caArray at NCI 41 1,741 November 15, 2006
Gene Expression Omnibus - NCBI 25,859 641,770 October 28, 2011
Genevestigator database 2,500 65,000 January 2012
MUSC database ~45 555 April 1, 2007
Stanford Microarray database 82,542 Not reported October 23, 2011
UNC Microarray database ~31 2,093 April 1, 2007
UNC modENCODE Microarray
database
~6 180 July 17, 2009
UPenn RAD database ~100 ~2,500 September 1, 2007
UPSC-BASE ~100 Not reported November 15, 2007
SAGE GEO
GUDMAP (421) MGI
BIOGPS
Gene
expression
technologies
Microarray
process
Gene expression
data stores
Data mining /
quering (pb-
query-
extraction-load-
store-pretreat)
Data analysis
(Question-
Answer,
descriptive,
predictive,
modeling)
Example:
ATP13A2 profile
in stress
conditions
Gene Expression Measurement
19. Data Mining / Querying
• Problem specification
• Query
• Extraction
• Storage
• Load
• Pretreat / prepare for analysis
Gene
expression
technologies
Microarray
process
Gene expression
data stores
Data mining /
quering (pb-
query-
extraction-load-
store-pretreat)
Data analysis
(Question-
Answer,
descriptive,
predictive,
modeling)
Example:
ATP13A2 profile
in stress
conditions
20. Data Analysis
• Question-Answer
– Experimental condition profile: group comparison
– Annotation profile: systems biological involved
– Clustering profile: co-regulation
– Time course profile: time variation
– …
• Descriptive
– Boxplot (SD, MEAN, MEDIAN, )
– Scatter plot
• Predictive / inference (clustering)
• Modeling (machine learning, simulation)
Gene
expression
technologies
Microarray
process
Gene expression
data stores
Data mining /
quering (pb-
query-
extraction-load-
store-pretreat)
Data analysis
(Question-
Answer,
descriptive,
predictive,
modeling)
Example:
ATP13A2 profile
in stress
conditions
21. • 3 Questions
– What is the right dataset (experimental
condition)?
– Is dataset is ready for analysis (quality)?
– What is the expression profile for a given gene?
– Significant differential expression in groups
comparison
• Tools
– ArrayExpress (EBI)
– Boxplot
– GEO2R (LIMMA, profile graph,)
Gene
expression
technologies
Microarray
process
Gene expression
data stores
Data mining /
quering (pb-
query-
extraction-load-
store-pretreat)
Data analysis
(Question-
Answer,
descriptive,
predictive,
modeling)
Example:
ATP13A2 profile
in stress
conditions
Data Analysis
22. Boxplot
Gene
expression
technologies
Microarray
process
Gene expression
data stores
Data mining /
quering (pb-
query-
extraction-load-
store-pretreat)
Data analysis
(Question-
Answer,
descriptive,
predictive,
modeling)
Example:
ATP13A2 profile
in stress
conditions
Data Analysis
23. Example: ATP13A2 Profile
in Stress Conditions
• Specification: ATP13A2 profile in stress
conditions
• Data querying:
– GEO
– Array Express
– Gene Atlas
• Data analysis:
– Online: GEO2R, Genospace, …
– Desktop: R, ArrayTrack, …
Gene
expression
technologies
Microarray
process
Gene expression
data stores
Data mining /
quering (pb-
query-
extraction-load-
store-pretreat)
Data analysis
(Question-
Answer,
descriptive,
predictive,
modeling)
Example:
ATP13A2 profile
in stress
conditions
24. Resolution Process
Context
Specification & Aims
Lab #2
Preprocessing
Viewing
Clustering
Differential expression
Classification
Data mining
24
Statement of problem / Case study:
The temporal program of gene expression during a model physiological response of human cells, the response of fibroblasts to serum, was explored with a
complementary DNA microarray representing about 8600 different human genes. Genes could be clustered into groups on the basis of their temporal patterns of expression in
this program. Many features of the transcriptional program appeared to be related to the physiology of wound repair, suggesting that fibroblasts play a larger and richer role in
this complex multicellular response than had previously been appreciated.
Gene Expression Data Analysis
16 Vishwanath R. Iyer, Scince, 1999
Conclusion: ?
Aim:
The purpose of this lab is to initiate on gene expression data analysis process.
We simulated the application on “Transcriptional Program in the Response of
Human Fibroblasts to Serum” . Now we can understand how a researcher can
come to identify a significant expressed gene from microarray dataset.
T1. Gene expression overview
T2. Excel used in Genomics
Objective: used of basic excel functionalities to solve some gene
expression data analysis needs
Acquired skills
- Gene expression data overview
- Excel Used for genomics
- Microarray data analysis using GEPAS
T1.1. Review of genomics place in OMIC- world
T1.2. Microarray data technics and process
T1.3. Data analysis cycle and tools
T2.1. Colum manipulation, functions used, anchor, copy with
function, sort data, search and replace
T2.2. Experiment comparison: Data pre-treatment
T1.3. Differential expressed gene from replicate experiments (SAM)
T2. GEPAS: Gene expression analysis pattern suite
Objective: used of the GEPAS suite to apply the whole microarray data
analyzing process on fibroblast data.
http://www.transcriptome.ens.fr/gepas/index.html
Expression Profile Clustering:
Slide Scanning:
Target Preparation:
Hybridization:
Editor's Notes
During this lab, we have:A brief review Lab’s templateGenome exploration practice…
DNA fragments amplified by PCR technique are spotted on a microscopic glass slide coated with polylysine prior to spotting process. The polylysine coating goal is to ensure DNA fixation through electrostatic interactions. PCR fragments are in our case the expressed part (ORF) of the 6200 Saccharomyces cerevisae genes (baker yeast). Slide preparation is achieved by blocking the polylysine not fixed to DNA in order to avoid target binding. Prior to hybridisation, DNA is denatured to obtained a single strand DNA on the microarray, this will allow the probe to bind to the complementary strand from the target. Apart from glass slide microarray other types of chips exist
Target preparation:RNA are extracted from two yeast cultures from which we want to compare expression level. Messengers RNA are then transformed in cDNA by reverse transcription. On this stage, DNA from the first culture with a green dye, whereas DNA from the second culture is labelled with a red dye.Hybridisation:Green labelledcDNA and red labelled ones are mixed together (call the target) and put on the matrix of spotted single strand DNA (call the probe). The chip is then incubated one night at 60 degrees. At this temperature, a DNA strand that encounter the complementary strand and match together to create a double strand DNA. The fluorescent DNA will then hybridise on the spotted onesSlide scanning:A laser excites each spot and the fluorescent emission gather through a photo-multiplicator (PMT) coupled to a confocal microscope. We obtained two images where grey scales represent fluorescent intensities read. If we replace grey scales by green scales for the first image and red scales for the second one, we obtained by superimposing the two images one image composed of spots going from green ones (where only DNA from the first condition is fixed) to red (where only DNA from the second condition is fixed) passing through the yellow colour (where DNA from the two conditions are fixed on equal amount).Data analysis:We have now two microarray images from which we have to calculate the number of DNA molecules in each experimental condition. To dos o, we measure the signal amount in the green dye emission wavelength and the signal amount in the red dye emission wavelength. Then we normalise these amount according to various parameters (yeast amount in each culture condition, emission power of each dye, …). We suppose that the amount of fluorescent DNA fixed is proportional to the mRNA amount present in each cell at the beginning and we calculate the red/green fluorescence ratio. If this ratio is greater than 1 (red on the image), the gene expression is greater in the second experimental condition, if this ration is smaller than 1 (green on the image), the gene expression is greater in the first condition. We can visualize these differences in expression using software as the one developed in the laboratory call ArrayPlot (cf below image). This software allows from the intensities list of spot to display the red intensities of each spot as a function of the green intensities.Expression profile clustering:Then we can try to gather genes that share the same expression profile on several experiments. This clustering can be done gradually as for phylogenetic analysis, which consist in calculating similarity criteria between expression profiles and gather the most similar ones. We can also use more complex techniques as principal component analysis or neuronal networks.At the end hierarchical clustering is usually displayed as a matrix where each column represent one experiment and each row a gene. Ratios are displayed thanks to a colour scale going from green (repressed genes) to red (induced genes).
Once you have your normalized data file, open it with Excel. You can filter out weak intensity spots (eliminate the weakest intensities in both channels) keep spot with ratio greater than 1 or lower than –1. Remember we are working with log2(ratio) so log2(2)=1. This method called “fold change” is the one used at the beginning of microarray analysis and is still useful if you do not have enough replicates to apply statistical treatments.The “fold change” method lack accuracy regarding the significant threshold to be fixed. That’s the reason why it is useful to apply a statistical method able to take into account intensity variations and most of all, the variability among experiments.Significance Analysis of Microarrays (SAM):SAM is an Excel macro freely available for academics on the web. The use of SAM in Excel spreadsheet makes this tool easier to use for most of microarray users. Using SAM implies several modifications in your data file:The ratio or intensity values in the Excel sheet must not contain any comas but only points as decimal separator.The header line depends on the type of analysis you want to perform. You can refer to SAM manual for more information. So you must duplicate your header if you don’t want to loose the experiment information (see image below).Two annotation columns are available. SAM always references its calculation to the line number in the departure sheet.Before launching the macro, it is necessary to select the data precisely because SAM rejects lines with too much missing values (such as empty lines).
Once you have your normalized data file, open it with Excel. You can filter out weak intensity spots (eliminate the weakest intensities in both channels) keep spot with ratio greater than 1 or lower than –1. Remember we are working with log2(ratio) so log2(2)=1. This method called “fold change” is the one used at the beginning of microarray analysis and is still useful if you do not have enough replicates to apply statistical treatments.The “fold change” method lack accuracy regarding the significant threshold to be fixed. That’s the reason why it is useful to apply a statistical method able to take into account intensity variations and most of all, the variability among experiments.Significance Analysis of Microarrays (SAM):SAM is an Excel macro freely available for academics on the web. The use of SAM in Excel spreadsheet makes this tool easier to use for most of microarray users. Using SAM implies several modifications in your data file:The ratio or intensity values in the Excel sheet must not contain any comas but only points as decimal separator.The header line depends on the type of analysis you want to perform. You can refer to SAM manual for more information. So you must duplicate your header if you don’t want to loose the experiment information (see image below).Two annotation columns are available. SAM always references its calculation to the line number in the departure sheet.Before launching the macro, it is necessary to select the data precisely because SAM rejects lines with too much missing values (such as empty lines).
I can not say that I'm into Statistician 20 min. I give you just a few items to give rapid analysis of microarray.
The following experimental techniques are used to measure gene expression and are listed in roughly chronological order, starting with the older, more established technologies. They are divided into two groups based on their degree of multiplexity.
ArrayTrack™ provides an integrated solution for managing, analyzing, and interpreting microarray gene expression data. Specifically, ArrayTrack™ is MIAME (Minimum Information About A Microarray Experiment)-supportive for storing both microarray data and experiment parameters associated with a pharmacogenomics or toxicogenomics study. Many statistical and visualization tools are available with ArrayTrack™ which provides a rich collection of functional information about genes, proteins, and pathways for biological interpretation. The primary emphasis of ArrayTrack™ is the direct linking of analysis results with functional information to facilitate the interaction between the choice of analysis methods and the biological relevance of analysis results. Using ArrayTrack™, users can easily select a statistical method applied to stored microarray data to determine a list of differentially expressed genes. The gene list can then be directly linked to pathways and gene ontology for functional analysis.
Boxplots are useful for determining where the majority of the data lies