新闻媒体-太阳成tyc7111cc

发稿时间：2020-06-19来源：天昊生物

天昊生信团自4月份以来，陆续推出一系列的r基础教程，很多老师也展开r的学习。目前收到部分老师反馈如何对一组数据进行列求和。之所以提出这个问题，是因为excel数据中样品是无规则排列，即使行排序也不能将同一组的样品靠在一起；或者样品数量较多，分组较多，需要多次输入求和函数。这里，我们提供了一个r的统计教程，帮您解决这方面的问题。

一读取数据

1.读取绝对丰度数据

in [1]:

df <- read.table('genus.taxon.abundance.xls',header = t, row.names = 1, sep = 't',check.names = false,quote = '',fill = t)

head(df,10)

out[1]:

c-1  c-2  c-3  c-4  c-5  c-6  c-7  m-1  m-2  m-3  ...  m-5  m-6  m-7  m-8  abundance  superkingdom  phylum  class  order  family

no_rank  32729  43885  36532  29335  21815  26879  41869  31030  29862  33787  ...  43164  30980  21853  21792  471140  -  -  -  -  -

lactobacillus  24438  24449  3760  18204  28795  4537  12831  24450  17354  26211  ...  30850  19648  21548  18263  291188  bacteria  firmicutes  bacilli  lactobacillales  lactobacillaceae

unassigned  25317  21725  13405  19347  14697  16676  19162  15819  22083  21072  ...  12032  7750  33000  26360  279560  bacteria  bacteroidetes  bacteroidia  bacteroidales  porphyromonadaceae

alistipes  5720  6196  18812  14525  19759  21996  10696  4411  5670  3663  ...  5744  6580  5415  5571  146280  bacteria  bacteroidetes  bacteroidia  bacteroidales  rikenellaceae

parabacteroides  6092  2683  8164  10246  10619  8742  7885  4493  12209  2423  ...  5628  4943  6382  11633  118324  bacteria  bacteroidetes  bacteroidia  bacteroidales  porphyromonadaceae

bacteroides  9803  3257  7719  5919  8039  6959  3502  6514  8694  3840  ...  7839  4368  11986  15711  116935  bacteria  bacteroidetes  bacteroidia  bacteroidales  bacteroidaceae

barnesiella  2982  1414  2341  9184  3707  6875  3812  3183  2773  5250  ...  3128  6584  3764  6680  64267  bacteria  bacteroidetes  bacteroidia  bacteroidales  porphyromonadaceae

eisenbergiella  680  2283  4357  1743  1251  6988  3731  1832  2204  1598  ...  1193  649  3466  4671  45890  bacteria  firmicutes  clostridia  clostridiales  lachnospiraceae

oscillibacter  653  714  4704  1569  2210  4680  4303  2748  4912  800  ...  2424  729  1287  2486  37738  bacteria  firmicutes  clostridia  clostridiales  ruminococcaceae

turicibacter  1373  2366  20  1405  56  6  2091  5060  9  9229  ...  1  1  4360  1693  27673  bacteria  firmicutes  erysipelotrichia  erysipelotrichales  erysipelotrichaceae

2.读取分组数据

in [2]:

info = read.table('sample_groups.xls',header = f,col.names = c('sample','group'))head(info)

out[2]:

sample  group

c-1  control

c-2  control

c-3  control

c-4  control

c-5  control

c-6  control

3.检查样品是否正确

in [3]:

info$sample

out[3]:

c-1 c-2 c-3 c-4 c-5 c-6 c-7 m-1 m-2 m-3 m-4 m-5 m-6 m-7 m-8

4.根据分组文件，从丰度数据中提取样品的丰度信息

in [4]:

df = df[as.character(info$sample)]head(df)

out[4]:

c-1  c-2  c-3  c-4  c-5  c-6  c-7  m-1  m-2  m-3  m-4  m-5  m-6  m-7  m-8

no_rank  32729  43885  36532  29335  21815  26879  41869  31030  29862  33787  25628  43164  30980  21853  21792

lactobacillus  24438  24449  3760  18204  28795  4537  12831  24450  17354  26211  15850  30850  19648  21548  18263

unassigned  25317  21725  13405  19347  14697  16676  19162  15819  22083  21072  11115  12032  7750  33000  26360

alistipes  5720  6196  18812  14525  19759  21996  10696  4411  5670  3663  11522  5744  6580  5415  5571

parabacteroides  6092  2683  8164  10246  10619  8742  7885  4493  12209  2423  16182  5628  4943  6382  11633

bacteroides  9803  3257  7719  5919  8039  6959  3502  6514  8694  3840  12785  7839  4368  11986  15711

5.检查分组信息

in [5]:

info$group

out[5]:

control control control control control control control nnk-bap nnk-bap nnk-bap nnk-bap nnk-bap nnk-bap nnk-bap nnk-bap

分组信息发现，仅有2个分组，组名分别是control、nnk-bap

二计算该组的丰度之和、均值、方差、标准差

查看control组包含哪些样品

in [6]:

info[info$group=='control',1]

out[6]:

c-1 c-2 c-3 c-4 c-5 c-6 c-7

apply函数计算control组的和、均值、方差、标准差

in [7]:

df['control_sum']  = apply(df[as.character(info[info$group=='control',1])],1,sum)

df['control_mean']  = apply(df[as.character(info[info$group=='control',1])],1,mean)

df['control_var']  = apply(df[as.character(info[info$group=='control',1])],1,var)

df['control_sd']  = apply(df[as.character(info[info$group=='control',1])],1,sd)

head(df)

out[7]:

c-1  c-2  c-3  c-4  c-5  c-6  c-7  m-1  m-2  m-3  m-4  m-5  m-6  m-7  m-8  control_sum  control_mean  control_var  control_sd

no_rank  32729  43885  36532  29335  21815  26879  41869  31030  29862  33787  25628  43164  30980  21853  21792  233044  33292.000  64182849  8011.420

lactobacillus  24438  24449  3760  18204  28795  4537  12831  24450  17354  26211  15850  30850  19648  21548  18263  117014  16716.286  99804027  9990.197

unassigned  25317  21725  13405  19347  14697  16676  19162  15819  22083  21072  11115  12032  7750  33000  26360  130329  18618.429  16946400  4116.601

alistipes  5720  6196  18812  14525  19759  21996  10696  4411  5670  3663  11522  5744  6580  5415  5571  97704  13957.714  43482964  6594.161

parabacteroides  6092  2683  8164  10246  10619  8742  7885  4493  12209  2423  16182  5628  4943  6382  11633  54431  7775.857  7342272  2709.663

bacteroides  9803  3257  7719  5919  8039  6959  3502  6514  8694  3840  12785  7839  4368  11986  15711  45198  6456.857  5800759  2408.477

apply函数计算nnk-bap组的和、均值、方差、标准差

in [8]:

df['nnk-bap_sum']  = apply(df[as.character(info[info$group=='nnk-bap',1])],1,sum)

df['nnk-bap_mean']  = apply(df[as.character(info[info$group=='nnk-bap',1])],1,mean)

df['nnk-bap_var']  = apply(df[as.character(info[info$group=='nnk-bap',1])],1,var)

df['nnk-bap_sd']  = apply(df[as.character(info[info$group=='nnk-bap',1])],1,sd)

head(df)

out[8]:

c-1  c-2  c-3  c-4  c-5  c-6  c-7  m-1  m-2  m-3  ...  m-7  m-8  control_sum  control_mean  control_var  control_sd  nnk-bap_sum  nnk-bap_mean  nnk-bap_var  nnk-bap_sd

no_rank  32729  43885  36532  29335  21815  26879  41869  31030  29862  33787  ...  21853  21792  233044  33292.000  64182849  8011.420  238096  29762.000  48868388  6990.593

lactobacillus  24438  24449  3760  18204  28795  4537  12831  24450  17354  26211  ...  21548  18263  117014  16716.286  99804027  9990.197  174174  21771.750  25821419  5081.478

unassigned  25317  21725  13405  19347  14697  16676  19162  15819  22083  21072  ...  33000  26360  130329  18618.429  16946400  4116.601  149231  18653.875  72916680  8539.126

alistipes  5720  6196  18812  14525  19759  21996  10696  4411  5670  3663  ...  5415  5571  97704  13957.714  43482964  6594.161  48576  6072.000  5639229  2374.706

parabacteroides  6092  2683  8164  10246  10619  8742  7885  4493  12209  2423  ...  6382  11633  54431  7775.857  7342272  2709.663  63893  7986.625  22692800  4763.696

bacteroides  9803  3257  7719  5919  8039  6959  3502  6514  8694  3840  ...  11986  15711  45198  6456.857  5800759  2408.477  71737  8967.125  17710582  4208.394

三按列计算相对丰度

in [9]:

df_relative = apply(df,2,function(d) d/sum(d))

head(df_relative)

out[9]:

c-1  c-2  c-3  c-4  c-5  c-6  c-7  m-1  m-2  m-3  ...  m-7  m-8  control_sum  control_mean  control_var  control_sd  nnk-bap_sum  nnk-bap_mean  nnk-bap_var  nnk-bap_sd

no_rank  0.13115102  0.17585513  0.14639033  0.11755065  0.08741665  0.10770901  0.16777666  0.12434282  0.11966244  0.135390620  ...  0.08756892  0.08732449  0.13340707  0.13340707  0.24318010  0.14420668  0.11926172  0.11926172  0.19457417  0.10579967

lactobacillus  0.09792749  0.09797157  0.01506700  0.07294672  0.11538677  0.01818058  0.05141614  0.09797557  0.06954062  0.105032218  ...  0.08634673  0.07318314  0.06698518  0.06698518  0.37814391  0.17982493  0.08724334  0.08724334  0.10281045  0.07690602

unassigned  0.10144980  0.08705600  0.05371626  0.07752693  0.05889354  0.06682375  0.07678560  0.06338959  0.08849058  0.084439315  ...  0.13223697  0.10562929  0.07460741  0.07460741  0.06420761  0.07409938  0.07474945  0.07474945  0.29032475  0.12923607

alistipes  0.02292107  0.02482849  0.07538309  0.05820430  0.07917789  0.08814195  0.04286081  0.01767567  0.02272072  0.014678304  ...  0.02169888  0.02232400  0.05593109  0.05593109  0.16475105  0.11869583  0.02433160  0.02433160  0.02245313  0.03594017

parabacteroides  0.02441175  0.01075127  0.03271462  0.04105758  0.04255225  0.03503078  0.03159662  0.01800426  0.04892367  0.009709399  ...  0.02557383  0.04661554  0.03115927  0.03115927  0.02781887  0.04877431  0.03200385  0.03200385  0.09035356  0.07209653

bacteroides  0.03928239  0.01305139  0.03093143  0.02371850  0.03221373  0.02788597  0.01403315  0.02610278  0.03483843  0.015387575  ...  0.04803007  0.06295682  0.02587379  0.02587379  0.02197829  0.04335292  0.03593289  0.03593289  0.07051638  0.06369227

四写入文件进行保存

in [10]:

write.csv(df_relative,'genus.taxon.relativeabundance.cvs')

往期相关链接：

1、r基础篇

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2、r进阶

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3、数据提交

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4、表达谱分析

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5、医学数据分析

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