Tile Performance

Counts per tile. Dashed red line indicates the 10% 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.

  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"))

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.

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.

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.

  ShortRead:::.plotTileCounts(readCnt[readCnt$type=="read",])

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.

Often, quality and count show an inverse relation. HiSeq: columns are upper swaths 1 and 2, and lower swaths 1 and 2, respectively.

  qscore <- perTile[["medianReadQualityScore"]]
  ShortRead:::.plotTileQualityScore(qscore[qscore$type=="read",])