Creating graphs and tables

[1]:
from itpseq import DataSet
[2]:
data = DataSet('tetracenomycin/')
data
[2]:
DataSet(data_path=PosixPath('tetracenomycin'),
        reference=Sample(noa:[1, 2, 3]),
        samples=[Sample(noa:[1, 2, 3]),
                 Sample(tcx:[1, 2, 3], ref: noa)],
        )
[3]:
data.itoeprint('shades')
[3]:
<Axes: ylabel='Inverse-toeprint length'>
../_images/notebooks_itpseq_tutorial_3_1.png
[4]:
data.itp_len_plot()
[4]:
<Axes: xlabel='Distance from the start site [nt]', ylabel='Number of reads (per million)'>
../_images/notebooks_itpseq_tutorial_4_1.png
[5]:
data.itp_len_plot(row='sample')
[5]:
<seaborn.axisgrid.FacetGrid at 0x7fba3413ad20>
../_images/notebooks_itpseq_tutorial_5_1.png
[6]:
tcx = data.samples['tcx']
tcx
[6]:
Sample(tcx:[1, 2, 3], ref: noa)
[7]:
tcx.hmap_grid();
../_images/notebooks_itpseq_tutorial_7_0.png
[8]:
tcx.DE(pos='E:P')
[8]:
baseMean log2FoldChange lfcSE stat pvalue padj log10pvalue log10padj rank qrank
QK 2388.608072 0.778417 0.072558 10.728162 7.507494e-27 3.153148e-24 26.124505 23.501256 20.335762 1.000000
PP 10164.907418 -0.411741 0.068681 -5.995011 2.034721e-09 4.272914e-07 8.691495 6.369276 3.578649 0.997619
KK 1925.860181 -0.408997 0.069592 -5.877081 4.175644e-09 5.845902e-07 8.379277 6.233149 3.427097 0.995238
TP 7266.661404 -0.278591 0.052503 -5.306210 1.119279e-07 1.175243e-05 6.951061 4.929872 1.936501 0.992857
EP 4319.078286 -0.305248 0.062449 -4.887962 1.018851e-06 7.131956e-05 5.991889 4.146791 1.829012 0.990476
... ... ... ... ... ... ... ... ... ... ...
SR 5592.632930 0.001314 0.049817 0.026377 9.789566e-01 9.883697e-01 0.009237 0.005081 0.000012 0.011905
IS 2505.613096 0.001021 0.051881 0.019688 9.842921e-01 9.913733e-01 0.006876 0.003763 0.000007 0.009524
HE 1255.730106 0.001114 0.070616 0.015768 9.874192e-01 9.921437e-01 0.005498 0.003425 0.000006 0.007143
TD 2411.075474 -0.000446 0.056517 -0.007890 9.937051e-01 9.940898e-01 0.002742 0.002574 0.000001 0.004762
DR 2082.675944 0.000412 0.055557 0.007407 9.940898e-01 9.940898e-01 0.002574 0.002574 0.000001 0.002381

420 rows × 10 columns

[9]:
tcx.get_counts_ratio('E:P').sort_values(by='ratio', ascending=False)
[9]:
noa.1 noa.2 noa.3 tcx.1 tcx.2 tcx.3 noa tcx ratio
QK 2014 1521 2079 3136 2888 2542 842.488457 1611.717538 1.913044
WM 90 57 77 113 89 58 33.060815 48.453429 1.465585
MF 190 135 165 221 163 173 72.748074 102.287249 1.406048
YY 1186 820 954 1284 976 1051 437.336974 609.545873 1.393767
MY 301 207 261 321 241 267 113.992118 152.365211 1.336629
... ... ... ... ... ... ... ... ... ...
TP 9366 6710 9416 7210 5702 5833 3812.390930 3466.791459 0.909348
EP 5583 4044 5639 3961 3528 3480 2284.621888 2052.122386 0.898233
m 1226086 770986 450057 740503 282179 690250 348210.205720 295241.808969 0.847884
KK 2747 1764 2555 1914 1282 1554 1047.897432 862.782356 0.823346
PP 12927 9580 14639 9281 7575 7989 5594.774388 4605.187585 0.823123

422 rows × 9 columns

[10]:
tcx.volcano(pos='E:P')
[10]:
<Axes: xlabel='log2FoldChange', ylabel='log10pvalue'>
../_images/notebooks_itpseq_tutorial_10_1.png