I'm working with genomic data in this format:
chr start end lengthabs_summit pileup X.log10.pvalue. fold_enrichment X.log10.qvalue. name
chr1 29017 29389 373 29358 31 28.59002 11.7551 24.95703 7_peak_2
chr1 569886 569978 93 569924 1334 334.59555 3.66639 329.13641 7_peak_13
chr1 713775 714591 817 714238 63 57.55214 14.98049 53.18887 7_peak_16
chr1 1009170 1009766 597 1009354 57 29.6026 6.49704 25.93788 7_peak_38
chr1 1013682 1014753 1072 1014285 45 22.68048 6.00323 19.24049 7_peak_39
chr1 1051283 1052033 751 1051691 49 34.32018 9.31181 30.51424 7_peak_43
chr1 1071957 1072489 533 1072064 36 20.45083 6.56582 17.09022 7_peak_46
chr1 1079500 1080408 909 1079994 36 21.25546 6.87813 17.8657 7_peak_47
chr1 1085553 1085793 241 1085681 32 20.59002 7.39226 17.22433 7_peak_48
chr1 1092859 1093875 1017 1092953 55 32.86424 7.69885 29.10045 7_peak_49
chr1 1098076 1098442 367 1098157 51 25.19468 6.00704 21.67023 7_peak_50
chr1 1167340 1167771 432 1167457 46 34.94157 10.2791 31.11741 7_peak_57
chr1 1310568 1311013 446 1310739 75 61.06957 12.93319 56.63967 7_peak_73
chr1 1334658 1335005 348 1334903 41 32.4828 10.54771 28.73031 7_peak_74
chr1 1368673 1368922 250 1368819 39 20.83713 6.22806 17.46213 7_peak_77
chr1 1407006 1407170 165 1407136 29 23.68931 9.70474 20.21472 7_peak_81
chr1 1446997 1447660 664 1447477 35 25.84261 9.0858 22.29687 7_peak_83
chr1 1550552 1551647 1096 1550765 42 27.55648 8.18824 23.95619 7_peak_87
chr1 1562564 1563038 475 1562809 45 27.52078 7.59892 23.92145 7_peak_88
chr1 1623807 1625030 1224 1624276 59 40.35566 9.39971 36.38159 7_peak_96
chr1 1655573 1656140 568 1655902 44 38.03923 12.27166 34.12801 7_peak_98
chr1 1677697 1678421 725 1677814 46 30.71495 8.58012 27.01606 7_peak_101
chr1 1690209 1690798 590 1690462 55 37.97549 9.38048 34.06614 7_peak_102
chr1 1850605 1851273 669 1850915 58 30.82379 6.7014 27.12157 7_peak_108
chr1 1981599 1982178 580 1981750 44 29.74246 8.62567 26.07388 7_peak_109
chr1 2121014 2121503 490 2121181 44 25.97852 7.22808 22.42829 7_peak_115
chr1 2130779 2131029 251 2130922 57 30.68925 6.78891 26.99122 7_peak_118
chr1 2158733 2159503 771 2159309 52 35.02846 8.9443 31.2017 7_peak_123
chr1 2322758 2323284 527 2323118 47 34.27391 9.75263 30.46929 7_peak_129
chr1 2343877 2344464 588 2344122 45 23.81217 6.35414 20.33326 7_peak_131
chr1 2457479 2458104 626 2457738 41 27.63569 8.43239 24.03328 7_peak_136
chr1 2507171 2507610 440 2507387 40 22.07389 6.50842 18.65457 7_peak_141
chr1 2517776 2518527 752 2517982 79 54.66531 10.1156 50.35995 7_peak_144
chr1 3104749 3105340 592 3105042 39 26.23199 8.29302 22.67383 7_peak_168
chr1 3339907 3340297 391 3340051 61 47.4887 11.4835 43.33681 7_peak_183
chr1 3541145 3541844 700 3541432 33 22.2239 7.90376 18.79962 7_peak_194
chr1 3712982 3713209 228 3713146 25 21.03679 9.46547 17.65467 7_peak_204
chr1 3773318 3774375 1058 3773903 71 64.18323 15.20667 59.69656 7_peak_206
chr1 3816748 3818236 1489 3817402 58 40.40163 9.61359 36.42624 7_peak_210
chr1 6052087 6052758 672 6052606 55 44.57815 11.90162 40.49594 7_peak_218
chr1 6086130 6086460 331 6086283 26 21.8022 9.58904 18.39271 7_peak_220
chr1 6259449 6259894 446 6259711 48 42.85861 13.27342 38.81911 7_peak_223
chr1 6453259 6454267 1009 6453833 36 25.9895 8.89626 22.43882 7_peak_236
chr1 6639866 6640271 406 6640031 44 35.75193 11.19049 31.90473 7_peak_243
chr1 6673060 6674146 1087 6673629 61 46.69005 11.1878 42.55659 7_peak_248
chr1 6844434 6845552 1119 6845378 72 58.2036 12.66662 53.8277 7_peak_252
chr1 6882651 6882812 162 6882746 21 22.99598 11.9154 19.54511 7_peak_255
chr1 7325838 7326444 607 7326032 32 24.22423 9.10923 20.73225 7_peak_258
chr1 7338199 7338451 253 7338410 23 20.28285 9.65393 16.92857 7_peak_259
chr1 7843899 7844833 935 7844068 50 38.84025 10.87309 34.90662 7_peak_266
chr1 7945594 7945913 320 7945805 37 40.12659 16.04772 36.15866 7_peak_267
chr1 8013883 8014418 536 8014328 29 24.7682 10.29467 21.25742 7_peak_269
chr1 8021299 8021991 693 8021619 78 76.90004 18.15693 72.21448 7_peak_270
chr1 8763179 8763705 527 8763447 45 41.29927 13.54395 37.30036 7_peak_297
chr1 8877609 8877845 237 8877792 24 20.69754 9.58204 17.32788 7_peak_299
chr1 9222907 9223400 494 9223017 44 30.50605 8.92885 26.81356 7_peak_310
chr1 9294465 9295131 667 9294997 34 23.79729 8.38562 20.31876 7_peak_316
chr1 9488859 9489215 357 9489096 33 35.37181 14.91643 31.53497 7_peak_323
chr1 9599244 9600007 764 9599346 38 30.08358 10.27689 26.40452 7_peak_325
Where the important columns are chr, start and end. For each chromosome, I want to make bins every 100kb, bin each row into one bin depending on the start position and then count the number of occurrences in each bin to compare the distribution between samples.
I'm having trouble defining the bins. I've seen that "cut" is very used for this, but since I don't have defined cutting points and it varies in each chromosome I'm not sure is the appropriate command.
bin_size = 100000
for (x in levels(df$chr)) { # For each chromosome
number_groups = max(df$end)/bin_size # Number of bins
# How to use cut here?
}
You can use aggregate for this. Using the data from thisisg:
aggregate(end ~ chr + start%/%100000, data=test, FUN=length)
## chr start%/%1e+05 end
## 1 chr1 0 1
## 2 chr1 5 1
## 3 chr1 7 1
## 4 chr1 10 8
...
The names then can be changed in the result. end is the count here, as that is the name on the left side of the ~ in the formula. Any column would do, as we're simply counting the number of elements with length.