Difference between revisions of "Team:Evry/Software/Pipeline"

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<section class="page-section" id="deg-step">
 
<section class="page-section" id="deg-step">
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<h2>Diffeential expression analysis</h2>
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<h2>Differential expression analysis</h2>
 
<p class="text-justify text-muted"><strong>What we produced: </strong>script for differential expression analysis, table with read counts (tab separated format, 7 columns, ENSG ids).</p>
 
<p class="text-justify text-muted"><strong>What we produced: </strong>script for differential expression analysis, table with read counts (tab separated format, 7 columns, ENSG ids).</p>
  
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with reads counts.</p>
 
with reads counts.</p>
 
<br>
 
<br>
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<pre>ensembl_id melanocyte_1 melanocyte_2 melanome_1 melanome_2
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<pre>ensembl_id melanocyte_1 melanocyte_2 melanoma_1 melanoma_2
 
ENSG00000000003 1964 2409 2328 2451
 
ENSG00000000003 1964 2409 2328 2451
 
ENSG00000000005 0 2 10 12
 
ENSG00000000005 0 2 10 12
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ENSG00000001084 11429 13795 3549 3279</pre>
 
ENSG00000001084 11429 13795 3549 3279</pre>
 
<p class="text-center"><strong>Figure 2:</strong> Example input format for DE analysis.</p>
 
<p class="text-center"><strong>Figure 2:</strong> Example input format for DE analysis.</p>
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<p class="text-justify">We tested two designs, as illustrated in the tables below: normal cells vs cancerous cells (4 samples), cancerous cells vs cancerous drug treated (4 samples).</p>
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<br>
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<div class="row">
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<div class="col-md-4 col-md-offset-1">
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<table class="table table-hover table-striped">
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<thead>
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  <tr>
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    <th>Sample name</th>
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    <th>Condition</th>
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  </tr>
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</thead>
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<tbody>
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<tr>
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<td>melanocyte_1</td>
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<td>M</td>
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  </tr>
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<tr>
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<td>melanocyte_2</td>
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<td>M</td>
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  </tr>
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<tr>
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<td>melanoma_1</td>
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<td>C</td>
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  </tr>
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<tr>
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<td>melanoma_2</td>
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<td>C</td>
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  </tr>
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    </tbody>
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</table>
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</div>
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<div class="col-md-4 col-md-offset-2">
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<table class="table table-hover table-striped">
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<thead>
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  <tr>
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    <th>Sample name</th>
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    <th>Condition</th>
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  </tr>
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</thead>
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<tbody>
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<tr>
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<td>melanoma_1</td>
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<td>C</td>
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  </tr>
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<tr>
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<td>melanoma_2</td>
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<td>C</td>
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  </tr>
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<tr>
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<td>melanoma_drug_1</td>
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<td>D</td>
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  </tr>
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<tr>
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<td>melanoma_drug_2</td>
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<td>D</td>
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  </tr>
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    </tbody>
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</table>
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</div>
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<p class="text-center"><strong>Table 1 and 2: </strong>tested designs.</p>
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</div>
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 +
<h3>Visual exploration of the samples</h3>
 +
<p class="text-justify">Prior  to  checking  distances  between  our  samples,  we  applied  a  regularized-logarithm
 +
transformation (rlog) to stabilise the variance across the mean. The effects of the transformation are shown in the figure below.</p>
  
 +
<img src="https://static.igem.org/mediawiki/2015/0/02/Rlog_transformation_plot.png" class="img-responsive" style="margin: 0 auto; "/>
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<p class="text-center"><strong>Figure 3:</strong> Effect of the regularized-logarithm
 +
transformation on 'melanocyte_1' and 'melanocyte_2' samples.</p>
 
</section>
 
</section>
  

Revision as of 00:20, 21 November 2015

   

Pipeline

All the information presented on this page (quality-control, differential expression analysis, data visualisation, variant discovery) is also available as a PDF file.



Data processing and quality control

What we produced: FASTQ files (if we don't have them), FASTQC reports, BAM and SAM files.

Figure 1: schematic overview of the pipeline for RNA-seq data analysis.

Differential expression analysis

What we produced: script for differential expression analysis, table with read counts (tab separated format, 7 columns, ENSG ids).

RNA-seq data can be difficult to interpret (especially in terms of differential expression quantitation). Thus, we decided to adopt a simple method for the analysis, based on counting, for each gene and for each sample, the number of available reads and then testing for significant differences between two experimental conditions or groups.

We wrote an R script that automatically creates a PDF file (in the current directory) with all the figures necessary for visual inspection and result interpretation. The input is a tab separated file with reads counts.


ensembl_id	melanocyte_1	melanocyte_2	melanoma_1	melanoma_2
ENSG00000000003	1964	2409	2328	2451
ENSG00000000005	0	2	10	12
ENSG00000000419	15122	19592	38225	36654
ENSG00000000457	12129	14893	7483	7812
ENSG00000000460	21930	25575	13123	13840
ENSG00000000938	48	58	26	42
ENSG00000000971	125	229	124	236
ENSG00000001036	11611	14125	14067	13518
ENSG00000001084	11429	13795	3549	3279

Figure 2: Example input format for DE analysis.

We tested two designs, as illustrated in the tables below: normal cells vs cancerous cells (4 samples), cancerous cells vs cancerous drug treated (4 samples).


Sample name Condition
melanocyte_1 M
melanocyte_2 M
melanoma_1 C
melanoma_2 C
Sample name Condition
melanoma_1 C
melanoma_2 C
melanoma_drug_1 D
melanoma_drug_2 D

Table 1 and 2: tested designs.

Visual exploration of the samples

Prior to checking distances between our samples, we applied a regularized-logarithm transformation (rlog) to stabilise the variance across the mean. The effects of the transformation are shown in the figure below.

Figure 3: Effect of the regularized-logarithm transformation on 'melanocyte_1' and 'melanocyte_2' samples.

After idenfication of genes that are both overexpressed and mutated in tumor samples, we want to know if good candidate antigens can be predicted. Read more about the prediction step.

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