r-bioc-shortread 1.36.0-1 / usr / lib / R / site-library / ShortRead / template / 5000-PerTile.html

<h2>Tile Performance</h2>
<a name="Tile-Performance"></a>

<p>
  Counts per tile. Dashed red line indicates the 10&#37; of tiles with
  fewest reads. An approximately uniform distribution suggests
  consistent read representation in each tile. Distinct separation of
  'good' versus poor quality tiles might suggest systematic failure,
  e.g., of many tiles in a lane, or excessive variability (e.g., due
  to unintended differences in sample DNA concentration) in read
  number per lane.
</p> 
<pre>
  perTile <- qa[["perTile"]]
  readCnt <- perTile[["readCounts"]]
  cnts <- readCnt[readCnt$type=="read", "count"]
  histogram(cnts, breaks=40, xlab="Reads per tile",
            panel=function(x, ...) {
            panel.abline(v=quantile(x, .1),
                col="red", lty=2)
                panel.histogram(x, ...)
            }, col="white"))
</pre>
@PER_TILE_HISTOGRAM@

<p>
  Spatial counts per tile. Divisions on the color scale are quantized,
  so that the range of counts per tile is divided into 10 equal
  increments. Parenthetic numbers on the scale represent the break
  points of the quantized values. Because the scale is quantized, some
  tiles will necessarily have `few' reads and other necessarily `many'
  reads.
</p>

<p>
  Consistent differences in read number per lane will result in some
  lanes being primarily one color, other lanes primarily another
  color.  Genome Analyzer data typically have greatest read counts in
  the center column of each lane. There are usually consistent
  gradients from `top' to `bottom' of each column.
</p>

<p>
  Low count numbers in the same tile across runs of the same flow cell
  may indicate instrumentation issues. HiSeq: columns are upper swaths 1 and 2,
  and lower swaths 1 and 2, respectively.
</p>

<pre>
  ShortRead:::.plotTileCounts(readCnt[readCnt$type=="read",])
</pre>
@PER_TILE_COUNT_FIGURE@
 
<p>
Median read quality score per tile. Divisions on the color scale are
quantized, so that the range of average quality scores per tile is
divided into 10 equal increments. Parenthetic numbers on the scale
represent the break points of the quantized values.
</p>

<p>
  Often, quality and count show an inverse relation. HiSeq: columns
  are upper swaths 1 and 2, and lower swaths 1 and 2, respectively.
</p>
<pre>
  qscore <- perTile[["medianReadQualityScore"]]
  ShortRead:::.plotTileQualityScore(qscore[qscore$type=="read",])
</pre>
@PER_TILE_QUALITY_FIGURE@