Need help with peak signal detection in Perl

Answer 1

You need to decide how local you want the peaks to be. The approach here finds peaks and troughs within broad regions of the data.

use strict;
use warnings;

my @data = (
    5.7, 5.3, 8.2, 16.5, 34.2, 58.8, 75.4, 75, 65.9, 62.6,
    58.6, 66.4, 71.4, 53.5, 40.5, 26.8, 14.2, 8.6, 5.9, 7.7,
    14.9, 30.5, 49.9, 69.1, 75.3, 69.8, 58.8, 57.2, 56.3, 67.1,
    69, 45.1, 27.6, 13.4, 8, 5,
);

# Determine mean. Or use Statistics::Descriptive.
my $sum;
$sum += $_ for @data;
my $mean = $sum / @data;

# Make a pass over the data to find contiguous runs of values
# that are either less than or greater than the mean. Also
# keep track of the mins and maxes within those groups.
my $group = -1;
my $gt_mean_prev = '';
my @mins_maxs;
my $i = -1;

for my $d (@data){
    $i ++;
    my $gt_mean = $d > $mean ? 1 : 0;

    unless ($gt_mean eq $gt_mean_prev){
        $gt_mean_prev = $gt_mean;
        $group ++;
        $mins_maxs[$group] = $d;
    }

    if ($gt_mean){
        $mins_maxs[$group] = $d if $d > $mins_maxs[$group];
    }
    else {
        $mins_maxs[$group] = $d if $d < $mins_maxs[$group];
    }

    $d = {
        i       => $i,
        val     => $d,
        group   => $group,
        gt_mean => $gt_mean,
    };
}

# A fun picture.
for my $d (@data){
    printf
        "%6.1f  %2d  %1s  %1d  %3s  %s\n",
        $d->{val},
        $d->{i},
        $d->{gt_mean} ? '+' : '-',
        $d->{group},
        $d->{val} == $mins_maxs[$d->{group}] ? '==>' : '',
        '.' x ($d->{val} / 2),
    ;

}

Output:

   5.7   0  -  0       ..
   5.3   1  -  0  ==>  ..
   8.2   2  -  0       ....
  16.5   3  -  0       ........
  34.2   4  -  0       .................
  58.8   5  +  1       .............................
  75.4   6  +  1  ==>  .....................................
  75.0   7  +  1       .....................................
  65.9   8  +  1       ................................
  62.6   9  +  1       ...............................
  58.6  10  +  1       .............................
  66.4  11  +  1       .................................
  71.4  12  +  1       ...................................
  53.5  13  +  1       ..........................
  40.5  14  -  2       ....................
  26.8  15  -  2       .............
  14.2  16  -  2       .......
   8.6  17  -  2       ....
   5.9  18  -  2  ==>  ..
   7.7  19  -  2       ...
  14.9  20  -  2       .......
  30.5  21  -  2       ...............
  49.9  22  +  3       ........................
  69.1  23  +  3       ..................................
  75.3  24  +  3  ==>  .....................................
  69.8  25  +  3       ..................................
  58.8  26  +  3       .............................
  57.2  27  +  3       ............................
  56.3  28  +  3       ............................
  67.1  29  +  3       .................................
  69.0  30  +  3       ..................................
  45.1  31  +  3       ......................
  27.6  32  -  4       .............
  13.4  33  -  4       ......
   8.0  34  -  4       ....
   5.0  35  -  4  ==>  ..

Answer 2

my @data = ...;

# filter out sequential duplicate values
my @orig_index = 0;
my @deduped = $data[0];
for my $index ( 1..$#data ) {
    if ( $data[$index] != $data[$index-1] ) {
        push @deduped, $data[$index];
        push @orig_index, $index;
    }
}

# add a sentinel (works for both ends)
push @deduped, -9**9**9;

my @local_maxima_indexes;
for my $index ( 0..$#deduped-1 ) {
    if ( $deduped[$index] > $deduped[$index-1] && $deduped[$index] > $deduped[$index+1] ) {
        push @local_maxima_indexes, $orig_index[$index];
    }
}

Note that this considers the first value a local maximum, and also the values 71.4 and 69. I'm not sure how you are distinguishing which ones you want included.

Answer 3

Do you have a control data set? If so, I'd recommend normalizing your data using say, a simple log ratio between yeast intensities and control images.

You could then use the perl port of ChiPOTle to grab the significant peaks, which sounds way more robust than searching local/global maxima, etc.

ChiPOTle "is a peak-finding algorithm used to analyze ChIP-chip microarray data", but I've used it successfully in many other applications (like ChIP-seq, which admittedly is closer to its original purpose than in your case).

The resulting log(yeast/control) negative values would be used to build a Gaussian background model for significance estimation. The algorithm then uses the false-discovery rate for multiple testing correction.

Here's the original paper.