Minería de Datos

ANALISIS DE UN SET DE DATOS

! Visualization Techniques ! Combined Graph !   Charts and Pies ! Search for specific functions

Data Mining on the DAG

ü  When working with large datasets, annotation results need to be summarized

ü  The DAG provides visualization of annotation data within its biological context

ü  In Blast2GO --> Combined Graph Function

Combined Graph

Each term has a number of sequences associated

Nodes can be coloured to indicate relevance

Each term is displayed around its biological context

Node shape to differentiate between direct and indirect annotation

Combined Graph

Different GO branches Reduces nodes by number of annotate sequences

Criterion for highlighting and filtering nodes

Node data to be displayed

Let's paint the DAG of the dataset analized yesterday (1000 sequences)

Too many nodes!!!

Combined Graph

Need way to find relevant information

Accumulated by node (Sequence Count)

3 1

4

5

1 3

1

Incomming information (Node Score)

3 1

2.4

2.5

1

1 3

Node Information Content

Node score We compute a node score that reflects the

amount of direct information at the node

3 1

2.4

2.5

1

1 3

Node score

GO2 3

GO1 1

GO2 2.4

GO4 2.5

1

1 3

NodeScore (GO1) = 1 * 0.6 0 = 1 NodeScore (GO2) = 3 * 0.6 0 = 3

dist=0 dist=0

dist=1 dist=1

NodeScore (GO3) = 1 * 0.6 1 + 3 * 0.61 = 0.6 + 1.8 = 2.4

dist=0

dist=2 dist=2

NodeScore (GO4) = 1 * 0.6 2 + 3 * 0.62 + 1 * 0.60 = 0.36 + 1.08 + 1 = 2.5

α = 0.6

Node score vs Annotation score

3 1

2.4

2.5

1

1 3

GO1 1 seq

GO2 1 child

GO3 50

hit1

hit2 hit3

ROOT

GO4 55

GO1 1 child

GO2 52

GO1 60

Annotation Score:

-  In annotation context

-  Relates to Blast results of ONE sequence

Node Score:

-  In data-mining context

-  Relates to analysis of a GROUP of sequences

DO NOT MIX-UP !!!!!

AS = max{%sim * ECw]}+ (#TPR_GOs-1) * GOw

Filtered Graph

Direct annotations

Transition nodes

# Filtered Nodes

Compacting Graphs by GOSlim

Show node content

Saving Options

Save as picture and as txt

Graph Charts

Graph Charts

•  Sequence Distribution/GO as Multilevel-Pie (#score or #seq cutoff)

•  Sequence Distribution/GO as Bar-Chart

•  Sequence Distribution/GO as Level-Pie (level selection)

Multilevel vs. GO-Slim Chart

GO-Slim: Handy to summarize functional content

Multi-level Pie with a sequence filter of 20

Use DAG to analyze a function

How many sequences are annotated to the function “photosynthesis”?

Option 1: Find in the GO graph à direct & indirect annotation Option 2: Find through the Select function. Two sub options

Option 2.1. Direct annotation (use GOid or description) Option 2.2. Direct&indirect (use GOid and “include GO parents”)

DAG can be used to make queries on general concepts without direct annotations

Example: analyze a specific function

Find a function on the graph

search export

Example: analyze a specific function

Select all sequences annotated to this function and its descendents

Example: analyze a specific function

Locate these sequences

Example: analyze a specific function

Explore the annotation diversity of a given function within the graph

Exporting the sequence table you can see all Sequences annotated to a given function (GO)

Conclusions ü  DAGs are interesting for browsing functional

annotation but can be too large ü  With filtering and pruning options you can create

more navigable DAGs ü  Pies are good to compact information: try out levels ü  GO-Slim compacts to more equivalent terms than

filtering the GO ü  You can use the DAG to query on general terms

Minería de Datos

ANALISIS DE VARIOS SETS DE DATOS

! Functional Enrichment ! Enriched Graphs !   Meta-analysis

Biosynthesis 54% Biosynthesis 18%

Sporulation 18% Sporulation 27%

One Gene List (A) The other list (B)

Are this two groups of genes carrying out

different biological roles?

Enrichment Analysis

Are these differences statistically significant?

???

???

Interpretation of a large list of genes: which are relevant functions?

Biosynthesis 54% Biosynthesis 18%

Sporulation 18% Sporulation 27%

One Gene List (A) The other list (B)

9 5 No biosynthesis

2 6 Biosynthesis

B A

Fisher's Exact Test

Contingency table

p-value for biosynthesis < 0.05 8 9 No sporulation

3 2 Sporulation

B A

p-value for sporulation > 0.05

Multiple testing correction

We do this for all GO term of our dataset!!!

Many tests => Many false positive => We need correction!

FDR control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons. In a list of rejected hypotheses, FDR controls the expected proportion of incorrectly rejected null hypotheses.

FWER control: The familywise error rate is the probability of making one or more false discoveries among all the hypotheses when performing multiple pairwise tests.

(more conservative)

8 9 No GO 3 2 GO B A

Test-set Ref-set

Fisher’s Exact Test in Blast2GO

Three files: ! Blast2GO project with annotations (.dat/.annot) ! One txt file with IDs: Test-set (.txt) ! Other txt file with IDs: Ref-set (.txt)

Different types of comparisons

●  Compare one condition against another

●  Remove Common Ids ●  Test and Ref-Set are

interchangeable

Set 1

Set 2

Common IDs

●  Compare a subset against the total

●  Gossip default setting

●  Test and Ref-Set are NOT interchangeable

Test- Set

Ref- Set

Common IDs

Test- Set

Ref- Set

Common IDs

FET in Blast2GO ●  Two-Tailed test not only identifies over but also

under represented functions. ●  If no Ref-Set is chosen all annotations are

used as reference

Enrichment Results

●  Result table with link outs to sequence lists

Most specific terms

Retains only the lowest, most specific enriched term per GO branch

Enriched Graph View enriched terms data as DAG graphs!

reduce

=> To draw all nodes, set filter to 1

Bar-Chart ●  Export enriched terms as chart!

=> Filter results

% of sequences in Ref group

% of sequences in Test group

If Test > Ref = over-expressed

If Ref > Test = under-expressed

Meta-analysis in Blast2GO

Sequence_1 GO:0005792 Sequence_1 GO:0006412 Sequence_1 GO:0003735 Sequence_2 GO:0016705 Sequence_2 GO:0005840 Sequence_2 GO:0005506

Treatment_1 GO:0005792 Treatment_1 GO:0006412 Treatment_1 GO:0003735

Annotation Result (.annot) Enrichment Result

ó Equivalent formats

Treatment_1 GO:0005792 Treatment_1 GO:0006412 Treatment_1 GO:0003735 Treatment_2 GO:0016705 Treatment_2 GO:0005840 Treatment_2 GO:0005506

By joining different functional enrichment results we can create and annotation file of conditions that capture their functional profile

Enrichment Result (.annot)

Meta-analysis in Blast2GO

Use seq names to see treatments

Use color by SeqCount

FIND SIMILARITIES BETWEEN TREATMENTS

Meta-analysis in Blast2GO DISPLAY FUNCTIONAL DISSIMILARITIES ON DAG

Use second column number for color

Ejercicios: Minería de Datos