examples-R/inst/doc/Copy_Number_segments.html at master · isb-cgc/examples-R · GitHub

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<!DOCTYPE html>
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<head>
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<title>Copy Number segments (Broad SNP6)</title>

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<h1>Copy Number segments (Broad SNP6)</h1>

<p>The goal of this notebook is to introduce you to the Copy Number (CN) segments BigQuery table.
This table contains all available TCGA Level-3 copy number data produced by the Broad Institute using the Affymetrix Genome Wide SNP6 array, as of October 2015. (Actual archive dates range from April 2011 to October 2014.) The most recent archives (eg broad.mit.edu_UCEC.Genome_Wide_SNP_6.Level_3.143.2013.0) for each of the 33 tumor types was downloaded from the DCC, and data extracted from all files matching the pattern %_nocnv_hg19.seg.txt. Each of these segmentation files has six columns: Sample, Chromosome, Start, End, Num_Probes, and Segment_Mean. During ETL the sample identifer contained in the segmentation files was mapped to the TCGA aliquot barcode based on the SDRF file in the associated mage-tab archive.</p>

<p>In order to work with BigQuery, you need to import the bigrquery library and you need to know the name(s) of the table(s) you are going to be working with:</p>

<pre><code class="r">cnTable &lt;- &quot;[isb-cgc:tcga_201510_alpha.Copy_Number_segments]&quot;
</code></pre>

<p>From now on, we will refer to this table using this variable &#39;cnTable&#39;, but we could just as well explicitly give the table name each time.
Let&#39;s start by taking a look at the table schema:</p>

<pre><code class="r">querySql &lt;- paste(&quot;SELECT * FROM &quot;,cnTable,&quot; limit 1&quot;, sep=&quot;&quot;)
result &lt;- query_exec(querySql, project=project)
data.frame(Columns=colnames(result))
</code></pre>

<pre><code>##                 Columns
## 1    ParticipantBarcode
## 2         SampleBarcode
## 3  SampleTypeLetterCode
## 4        AliquotBarcode
## 5                 Study
## 6              Platform
## 7            Chromosome
## 8                 Start
## 9                   End
## 10           Num_Probes
## 11         Segment_Mean
</code></pre>

<p>Unlike most other molecular data types in which measurements are available for a common set of genes, CpG probes, or microRNAs, this data is produced using a data-driven approach for each aliquot independently. As a result, the number, sizes and positions of these segments can vary widely from one sample to another.</p>

<p>Each copy-number segment produced using the CBS (Circular Binary Segmentation) algorithm is described by the genomic extents of the segment (chromosome, start, and end), the number of SNP6 probes contained within that segment, and the estimated mean copy-number value for that segment. Each row in this table represents a single copy-number segment in a single sample.</p>

<p>The Segment_Mean is the base2 log(copynumber/2), centered at 0. Positive values represent amplifications (CN&gt;2), and negative values represent deletions (CN&lt;2). Although within each cell, the number of copies of a particular region of DNA must be an integer, these measurements are not single-cell measurements but are based on a heterogenous sample. If 50% of the cells have 2 copies of a particular DNA segment, and 50% of the cells have 3 copies, this will result in an estimated copy number value of 2.5, which becomes 1.32 after the log transformation.</p>

<p>Let&#39;s count up the number of unique patients, samples and aliquots mentioned in this table. We will do this by defining a very simple parameterized query. (Note that when using a variable for the table name in the FROM clause, you should not also use the square brackets that you usually would if you were specifying the table name as a string.)</p>

<pre><code class="r">for (x in c(&quot;ParticipantBarcode&quot;, &quot;SampleBarcode&quot;, &quot;AliquotBarcode&quot;)) {
  querySql &lt;- paste(&quot;SELECT COUNT(DISTINCT(&quot;,x,&quot;), 25000) AS n &quot;,
                    &quot;FROM &quot;,cnTable)
  result &lt;- query_exec(querySql, project=project)
  cat(x, &quot;: &quot;, result[[1]], &quot;\n&quot;)
}
</code></pre>

<pre><code>## ParticipantBarcode :  10953
## SampleBarcode :  21890
## AliquotBarcode :  22106
</code></pre>

<p>Unlike most other molecular data types, in addition to data being available from each tumor sample, data is also typically available from a matched &ldquo;blood normal&rdquo; sample. As we can see from the previous queries, there are roughly twice as many samples as there are patients (aka participants). The total number of rows in this table is ~2.5 million, and the average number of segments for each aliquot is ~116 (although the distribution is highly skewed as we will see shortly).</p>

<p>Let&#39;s count up the number of samples using the <code>SampleTypeLetterCode</code> field:</p>

<pre><code class="r">querySql &lt;- &quot;
SELECT
  SampleTypeLetterCode,
  COUNT(*) AS n
FROM (
  SELECT
    SampleTypeLetterCode,
    SampleBarcode
  FROM
    [isb-cgc:tcga_201510_alpha.Copy_Number_segments]
  GROUP BY
    SampleTypeLetterCode,
    SampleBarcode )
GROUP BY
  SampleTypeLetterCode
ORDER BY
  n DESC&quot;

query_exec(querySql, project=project)
</code></pre>

<pre><code>##   SampleTypeLetterCode     n
## 1                   TP 10254
## 2                   NB  8730
## 3                   NT  2247
## 4                   TM   392
## 5                   TB   191
## 6                   TR    59
## 7                  TAP     9
## 8                  NBC     4
## 9                  NBM     4
</code></pre>

<p>As shown in the results of this last query, most samples are primary tumor samples (TP), and in most cases the matched-normal sample is a &ldquo;normal blood&rdquo; (NB) sample, although many times it is a &ldquo;normal tissue&rdquo; (NT) sample. You can find a description for each of these sample type codes in the TCGA Code Tables Report.
In order to get a better feel for the data in this table, let&#39;s take a look at the range of values and the distributions of segment lengths, mean segment values, and number of probes contributing to each segment.</p>

<pre><code class="r">querySql &lt;- &quot;
SELECT
  MIN(Length) AS minLength,
  MAX(Length) AS maxLength,
  AVG(Length) AS avgLength,
  STDDEV(Length) AS stdLength,
  MIN(Num_Probes) AS minNumProbes,
  MAX(Num_Probes) AS maxNumProbes,
  AVG(Num_Probes) AS avgNumProbes,
  STDDEV(Num_Probes) AS stdNumProbes,
  MIN(Segment_Mean) AS minCN,
  MAX(Segment_Mean) AS maxCN,
  AVG(Segment_Mean) AS avgCN,
  STDDEV(Segment_Mean) AS stdCN,
FROM (
  SELECT
    Start,
    END,
    (END-Start+1) AS Length,
    Num_Probes,
    Segment_Mean
  FROM
    [isb-cgc:tcga_201510_alpha.Copy_Number_segments] )&quot;

result &lt;- query_exec(querySql, project=project)
t(result)
</code></pre>

<pre><code>##                          1
## minLength     1.000000e+00
## maxLength     2.445951e+08
## avgLength     2.474561e+07
## stdLength     4.408090e+07
## minNumProbes  1.000000e+00
## maxNumProbes  1.313610e+05
## avgNumProbes  1.332859e+04
## stdNumProbes  2.368022e+04
## minCN        -8.673300e+00
## maxCN         1.054680e+01
## avgCN        -2.617346e-01
## stdCN         9.941424e-01
</code></pre>

<p>Segment lengths range from just 1 bp all the way up to entire chromosome arms, and the range of segment mean values is from -8.7 to +10.5 (average = -0.26, standard deviation = 1.0)</p>

<p>Now we&#39;ll use ggplot2 to create some simple visualizations.</p>

<p>For the segment means, let&#39;s invert the log-transform and then bin the values to see what the distribution looks like:</p>

<pre><code class="r">querySql &lt;- &quot;
SELECT
  lin_bin,
  COUNT(*) AS n
FROM (
  SELECT
    Segment_Mean,
    (2.*POW(2,Segment_Mean)) AS lin_CN,
    INTEGER(((2.*POW(2,Segment_Mean))+0.50)/1.0) AS lin_bin
  FROM
    [isb-cgc:tcga_201510_alpha.Copy_Number_segments]
  WHERE
    ( (End-Start+1)&gt;1000 AND SampleTypeLetterCode=&#39;TP&#39; )
  )
GROUP BY
  lin_bin
HAVING
  ( n &gt; 2000 )
ORDER BY
  lin_bin ASC&quot;

result &lt;- query_exec(querySql, project=project)

ggplot(data=result, aes(x=factor(lin_bin), y=n)) +
geom_bar(stat=&quot;identity&quot;) + xlab(&quot;bin&quot;) + ylab(&quot;Count&quot;)
</code></pre>

<p><img 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" alt="plot of chunk cn_fig1"/></p>

<p>The histogram illustrates that the vast majority of the CN segments have a copy-number value near 2, as expected, with significant tails on either side representing deletions (left) and amplifications (right).</p>

<p>Let&#39;s take a look at the distribution of segment lengths now. First we&#39;ll use 1Kb bins and look at segments with lengths up to 1 Mb.</p>

<pre><code class="r">querySql &lt;- &quot;
SELECT
  bin,
  COUNT(*) AS n
FROM (
  SELECT
    (END-Start+1) AS segLength,
    INTEGER((END-Start+1)/1000) AS bin
  FROM
    [isb-cgc:tcga_201510_alpha.Copy_Number_segments]
  WHERE
    (END-Start+1)&lt;1000000 AND SampleTypeLetterCode=&#39;TP&#39; )
GROUP BY
  bin
ORDER BY
  bin ASC&quot;

result &lt;- query_exec(querySql, project=project)

ggplot(data=result, aes(x=factor(bin), y=n)) +
geom_bar(stat=&quot;identity&quot;) + xlab(&quot;bin&quot;) + ylab(&quot;Count&quot;)
</code></pre>

<p><img 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" alt="plot of chunk cn_fig2"/></p>

<p>As expected, shorter segment lengths dominate, and between 1Kb and 1Mb it appears that segment lengths follow a power-law distribution.</p>

<p>Let&#39;s have a closer look at the shorter segments, under 1Kb in length:</p>

<pre><code class="r">querySql &lt;- &quot;
SELECT
  bin,
  COUNT(*) AS n
FROM (
  SELECT
    (END-Start+1) AS segLength,
    INTEGER((END-Start+1)/1) AS bin
  FROM
    [isb-cgc:tcga_201510_alpha.Copy_Number_segments]
  WHERE
    (END-Start+1)&lt;1000 AND SampleTypeLetterCode=&#39;TP&#39;
  )
GROUP BY
  bin
ORDER BY
  bin ASC&quot;

result &lt;- query_exec(querySql, project=project)

qplot(x=log10(bin), y=log10(n), data=result, ylab=&quot;log number of segments&quot;, xlab=&quot;segment length in log kb&quot;)
</code></pre>

<p><img 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" alt="plot of chunk cn_fig3"/></p>

<p>At this finer scale, we see that the most comment segment length is ~15bp.</p>

<p>Let&#39;s go back and take another look at the medium-length CN segments and see what happens when we separate out the amplifications and deletions. We&#39;ll use queries similar to the getSLhist_1k query above, but add another WHERE clause to look at amplifications and deletions respectively.</p>

<pre><code class="r">querySql1 &lt;- &quot;
SELECT
  bin,
  COUNT(*) AS n
FROM (
  SELECT
    (END-Start+1) AS segLength,
    INTEGER((END-Start+1)/1000) AS bin
  FROM
    [isb-cgc:tcga_201510_alpha.Copy_Number_segments]
  WHERE
    (END-Start+1)&lt;1000000 AND SampleTypeLetterCode=&#39;TP&#39; AND Segment_Mean&lt;-0.7 )
GROUP BY
  bin
ORDER BY
  bin ASC
&quot;

querySql2 &lt;- &quot;
SELECT
  bin,
  COUNT(*) AS n
FROM (
  SELECT
    (END-Start+1) AS segLength,
    INTEGER((END-Start+1)/1000) AS bin
  FROM
    [isb-cgc:tcga_201510_alpha.Copy_Number_segments]
  WHERE
    (END-Start+1)&lt;1000000 AND SampleTypeLetterCode=&#39;TP&#39; AND Segment_Mean&gt;0.7 )
GROUP BY
  bin
ORDER BY
  bin ASC
&quot;

SLhistDel &lt;- query_exec(querySql1, project=project)
SLhistAmp &lt;- query_exec(querySql2, project=project)
SLhistDel$Type &lt;- &quot;Del&quot;
SLhistAmp$Type &lt;- &quot;Amp&quot;
SLhist &lt;- rbind(SLhistDel,SLhistAmp)
qplot(data=SLhist, x=log10(bin), y=log10(n), color=Type)
</code></pre>

<p><img 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" alt="plot of chunk cn_fig4"/></p>

<p>The amplification and deletion distributions are nearly identical and still seem to roughly follow a power-law distribution. We can also infer from this graph that a majority of the segments less than 10Kb in length are either amplifications or deletions, while ~90% of the segments of lengths &gt;100Kb are copy-number neutral.</p>

<p>Before we leave this dataset, let&#39;s look at how we might analyze the copy-number as it relates to a particular gene of interest. This next parameterized query looks for all copy-number segments overlapping a specific genomic region and computes some statistics after grouping by sample.</p>

<pre><code class="r">genes &lt;- c(&quot;EGFR&quot;, &quot;MYC&quot;, &quot;TP53&quot;)
geneChr &lt;- c(7, 8, 17)
geneStart &lt;- c(55086725, 128748315, 7571720)
geneStop &lt;- c(55275031, 128753680, 7590868)

allResults &lt;- data.frame()

for (i in 1:length(genes)) {
    querySql &lt;- paste(
    &quot;SELECT
      SampleBarcode,
      AVG(Segment_Mean) AS avgCN,
      MIN(Segment_Mean) AS minCN,
      MAX(Segment_Mean) AS maxCN,
    FROM &quot;,
      cnTable,
    &quot;WHERE
      ( SampleTypeLetterCode=&#39;TP&#39;
        AND Num_Probes &gt; 10
        AND Chromosome=\&#39;&quot;,geneChr[i],&quot;\&#39; &quot;,
        &quot;AND ( (Start&lt;&quot;, geneStart[i], &quot; AND End&gt; &quot;, geneStop[i], &quot;)&quot;,
           &quot;OR (Start&lt;&quot;, geneStop[i], &quot; AND End&gt; &quot;,geneStop[i], &quot;)&quot;,
           &quot;OR (Start&gt;&quot;, geneStart[i], &quot; AND End&lt; &quot;,geneStop[i], &quot;) ) )&quot;,
    &quot;GROUP BY
      SampleBarcode&quot;, sep=&quot;&quot;)

    result &lt;- query_exec(querySql, project=project)
    result$gene &lt;- genes[i]
    allResults &lt;- rbind(allResults, result)
}
ggplot(allResults, aes(avgCN, colour=gene)) + geom_freqpoly(aes(group = gene))
</code></pre>

<pre><code>## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
</code></pre>

<p><img 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" alt="plot of chunk cn_fig5"/></p>

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