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ノート/ハックメモhttp://pepper.is.sci.toho-u.ac.jp/pepper/index.php?%A5%CE%A1%BC%A5%C8%2F%A5%CF%A5%C3%A5%AF%A5%E1%A5%E2 |
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ノート/ハックメモhttp://pepper.is.sci.toho-u.ac.jp/pepper/index.php?%A5%CE%A1%BC%A5%C8%2F%A5%CF%A5%C3%A5%AF%A5%E1%A5%E2 |
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訪問者数 1145 最終更新 2019-07-22 (月) 10:03:35
＞＞2018年の分 （このページの前）
ムキムキで疲れにくい体ができる？らくらくできる体幹トレーニングがこれｗｗｗ 短期間で効率的にカロリー消費できるトレーニング10 健康にいい、適度な運動量ってどのくらい？ Physical activity guidelines for adults (UK National Health Service) 動画でレクチャー！ たった8回で効果大の脂肪燃焼エクササイズ
ゾンビに追われ運動！？やる気を引き出すアプリ：日経メディカル
2019-05-16 CentOS6のgccをアップデートする
2019-06-23 MacOSとHomebrewとpyenvで快適python環境を。 - Qiita
2019-06-20 ゲノム医学第1回
2019-07-03 Windows 10はレジストリの自動バックアップを停止している - GIGAZINE
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
axis=1)
df.iloc[0, 2] = np.nan
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
s = df.style.applymap(color_negative_red)
s.to_excel('pandasstyle.xlsx')
これで実行できた。
import pandas as pd
df = pd.read_excel('mytest.xlsx')
print(df)
Excelファイルはそのまま（文字コード変換とかcsvとか考える必要なし）でよい。xlrdライブラリをインストールしておく必要ありHISAT: a fast spliced aligner with low memory requirements. Kim D, Langmead B, Salzberg SL Nat Methods. 2015 Apr;12(4):357-60.を訳したものらしい。
for fieldname in p._fields:
print(getattr(p, fieldname) #if (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__) < 40900
#error "unsupported GCC version - see https://github.com/nlohmann/json#supported-compilers"
#endif
やることは
# yum install centos-release-scl # yum install scl-utils # yum install devtoolset-7-gcc devtoolset-7-gcc-c++ devtoolset-7-binutils # または全部のdevtoolset-7を入れるなら yum install devtoolset-7 # レベル6とか5も可加えて毎回使用を宣言
# scl enable devtoolset-7 bashなので、何時も使うならbash_profileの中で
$ cat /etc/profile.d/enabledevtoolset-7.sh #!/bin/bash source scl_source enable devtoolset-7
git clone https://github.com/tanghaibao/goatools.git --- まず全体をclone
./run.sh
select the test from below
1) Test enrichment 3) Test mapslim
2) Test plotting go terms 4) Test mapslim on assocation
1を選択する
go-basic.obo: fmt(1.2) rel(2019-04-17) 47,398 GO Terms
HMS:0:00:00.463984 128,975 annotations READ: data/association
Study: 276 vs. Population 33239
Propagating term counts to parents ..
53 GO IDs in assc. are not found in the GO-DAG: GO:0016566 GO:0005792 GO:0003702
GO:0000300 GO:0046853 GO:0016986 GO:0019204 GO:0016944 GO:0035300 GO:0006987
GO:0006800 GO:0000060 GO:0008471 GO:0003840 GO:0006496 GO:0019446 GO:0000059
GO:0019575 GO:0004428 GO:0008159 GO:0004437 GO:0016585 GO:0000903 GO:0022627
GO:0000072 GO:0005057 GO:0033903 GO:0030528 GO:0007108 GO:0005678 GO:0005625
GO:0042891 GO:0016563 GO:0010149 GO:0046909 GO:0048196 GO:0008565 GO:0000299
GO:0004086 GO:0000739 GO:0003715 GO:0008943 GO:0005624 GO:0003701 GO:0004702
GO:0004871 GO:0042156 GO:0004221 GO:0042991 GO:0022625 GO:0045750 GO:0017163
GO:0016564
96% 31,855 of 33,239 population items found in association
97% 269 of 276 study items found in association
100% 276 of 276 study items found in population(33239)
Calculating 6,076 uncorrected p-values using fisher
6,076 GO terms are associated with 31,847 of 33,239 population items
773 GO terms are associated with 269 of 276 study items
（中略）
# Generated by GOATOOLS v0.8.12 (2019-05-01)
# min_ratio=None pval=0.05
GO NS enrichment name ratio_in_study ratio_in_pop p_uncorrected
depth study_count p_bonferroni p_sidak p_holm p_fdr_bh study_items
....GO:0036211 BP e protein modification process 33/276 1725/33239
8.07e-06 5 33 0.049 0.0470.049 0.00981 AT1G13580, AT1G66610,
AT1G66860, AT1G66980, AT2G29220, AT2G41140, AT2G41970, AT2G42550, AT3G25540,
AT3G28600, AT3G45390, AT3G45420, AT3G46400, AT3G46410, AT3G46420, AT3G46930,
AT3G56760, AT3G57710, AT3G57720, AT3G58690, AT4G00960, AT4G11490, AT4G17483,
AT4G23220, AT4G23270, AT4G26840, AT5G37910, AT5G37930, AT5G38210, AT5G47340,
AT5G55160, AT5G59010, AT5G59650
（以下略）python scripts/find_enrichment.py data/study data/population data/association --outfile=goea_fdr_bh.xlsx,goea_fdr_bh.tsv \
--pval_field=fdr_bh
（上記と同じ表示結果）
Calculating 6,076 uncorrected p-values using fisher
6,076 GO terms are associated with 31,847 of 33,239 population items
773 GO terms are associated with 269 of 276 study items
5 GO terms found significant (< 0.05=alpha) ( 5 enriched + 0 purified): local bonferroni
110 study items associated with significant GO IDs (enriched)
0 study items associated with significant GO IDs (purified)
5 GO terms found significant (< 0.05=alpha) ( 5 enriched + 0 purified): local sidak
110 study items associated with significant GO IDs (enriched)
0 study items associated with significant GO IDs (purified)
5 GO terms found significant (< 0.05=alpha) ( 5 enriched + 0 purified): local holm
110 study items associated with significant GO IDs (enriched)
0 study items associated with significant GO IDs (purified)
17 GO terms found significant (< 0.05=alpha) ( 16 enriched + 1 purified): statsmodels fdr_bh
111 study items associated with significant GO IDs (enriched)
1 study items associated with significant GO IDs (purified)
245 of 6,076 results have uncorrected P-values <= 0.05=pval
17 items WROTE: goea_fdr_bh.xlsx
17 items WROTE: goea_fdr_bh.tsv
（ファイル goea_fdr_bh.xlsx、goea_fdr_bh.tsvを生成、xlsxを開いてみる）
$seqret AP012030.gb -outseq AP012030.gff3 -osformat gcc3 -feature
$samtools index SRR453566.sorted.bamこれを作っておいて、同じディレクトリ内にSRR453566.sorted.bamとSR453566.sorted.bam.bai がある状態にして、IGV.jsに対して
{
"name": "BAM",
"type": "alignment",
"format": "bam",
#"displayMode": "EXPANDED",
"url": "files/SRR453566.sorted.bam",
"indexed": True
}bgzip -c s288c.vcf > s288c.vcf.gz bcftools index s288c.vcf.gzこの結果できたs288c.vcf.csiをs288c.vcf.gzとおなじフォルダに置いて食わす。
#Converting other formats to GFF3 from BCBio import GFF from Bio import SeqIO in_file = "your_file.gb" out_file = "your_file.gff" in_handle = open(in_file) out_handle = open(out_file, "w") GFF.write(SeqIO.parse(in_handle, "genbank"), out_handle) in_handle.close() out_handle.close()
process = subprocess.run(['cal', '-A', '3'], stdout=subprocess.PIPE)
output = process.stdout.decode('utf-8')
# output = process.stdout.decode('cp932') # Windowsの場合https://www.uniprot.org/uniprot/?query=reviewed:yes+AND+yliB+AND+organism:Escherichia%20coli&columns=go-id&format=tab結果は
Gene ontology IDs GO:0006974; GO:0030288; GO:0042938; GO:0043190; GO:1904680
check_pd=[row for index, row in data.groupby("user_id")]
pandasstyle.png
goatools-goea_fdr_bh_xlsx.png
