删除行，如果特定列具有与子字符串向量匹配的值（字符串）。

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英文:

Remove rows if a specific column has values (strings) that match a vector of substrings

问题

The redundant.gs vector contains substrings that might match with the gs_name column in the all.gene.sets dataframe. I want to remove all such matches.

  1. redundant.gs <- c("ANDERSON_BLOOD_CN54GP140_ADJUVANTED", "BUCASAS_PBMC_FLUARIX_FLUVIRIN")
  2. gene.sets <- all.gene.sets[!(all.gene.sets$gs_name %like% redundant.gs),]

数据：

  1. all.gene.sets <- tibble::tribble(
  2.   ~gs_cat, ~gs_subcat,    ~gs_name,
  3.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN",
  4.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN",
  5.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN",
  6.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN",
  7.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN",
  8.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP",
  9.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP",
  10.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP",
  11.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP",
  12.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP",
  13.   "C7",    "VAX",         "ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP",
  14.   "C7",    "VAX",         "BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER",
  15.   "C7",    "VAX",         "BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER",
  16.   "C7",    "VAX",         "BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER",
  17.   "C7",    "VAX",         "BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER",
  18.   "C7",    "VAX",         "BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER",
  19.   "C7",    "IMMUNESIGDB", "GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN",
  20.   "C7",    "IMMUNESIGDB", "GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN",
  21.   "C7",    "IMMUNESIGDB", "GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN",
  22.   "C7",    "IMMUNESIGDB", "GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN",
  23.   "C7",    "IMMUNESIGDB", "GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN",
  24. )
英文:

The redundant.gs vector contains substrings that might match with the gs_name column in the all.gene.sets dataframe. I want to remove all such matches.
My code below only removes rows that match the first substring ANDERSON_BLOOD_CN54GP140_ADJUVANTED but not the second or subsequent substrings.

  1. redundant.gs &lt;- c(&quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED&quot;, &quot;BUCASAS_PBMC_FLUARIX_FLUVIRIN&quot;)
  2. gene.sets &lt;- all.gene.sets[!(all.gene.sets$gs_name %like% redundant.gs),]

Data:

  1. all.gene.sets &lt;- tibble::tribble(
  2.   ~gs_cat, ~gs_subcat,    ~gs_name,
  3.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN&quot;,
  4.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN&quot;,
  5.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN&quot;,
  6.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN&quot;,
  7.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_DN&quot;,
  8.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP&quot;,
  9.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP&quot;,
  10.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP&quot;,
  11.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP&quot;,
  12.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP&quot;,
  13.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;ANDERSON_BLOOD_CN54GP140_ADJUVANTED_WITH_GLA_AF_AGE_18_45YO_1DY_UP&quot;,
  14.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER&quot;,
  15.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER&quot;,
  16.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER&quot;,
  17.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER&quot;,
  18.   &quot;C7&quot;,    &quot;VAX&quot;,         &quot;BUCASAS_PBMC_FLUARIX_FLUVIRIN_CAUCASIAN_MALE_AGE_18_40YO_HIGH_RESPONDERS_1DY_3DY_POSITIVE_PREDICTIVE_OF_TITER&quot;,
  19.   &quot;C7&quot;,    &quot;IMMUNESIGDB&quot;, &quot;GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN&quot;,
  20.   &quot;C7&quot;,    &quot;IMMUNESIGDB&quot;, &quot;GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN&quot;,
  21.   &quot;C7&quot;,    &quot;IMMUNESIGDB&quot;, &quot;GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN&quot;,
  22.   &quot;C7&quot;,    &quot;IMMUNESIGDB&quot;, &quot;GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN&quot;,
  23.   &quot;C7&quot;,    &quot;IMMUNESIGDB&quot;, &quot;GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN&quot;,
  24. )

答案1

得分: 2

把redundant.gs合并成一个以&quot;|&quot;分隔的字符串，然后用作grepl()中的模式：

  1. keep.rows &lt;- !grepl(
  2.   paste(redundant.gs, collapse = &quot;|&quot;),
  3.   all.gene.sets$gs_name
  4. )
  6. all.gene.sets[keep.rows, ]
  1.    gs_cat   gs_subcat                                            gs_name
  2. 17     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  3. 18     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  4. 19     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  5. 20     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  6. 21     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
英文:

Collapse redundant.gs into a single string separated by &quot;|&quot;, then use as a pattern in grepl():

  1. keep.rows &lt;- !grepl(
  2.   paste(redundant.gs, collapse = &quot;|&quot;),
  3.   all.gene.sets$gs_name
  4. )
  6. all.gene.sets[keep.rows, ]
  1.    gs_cat   gs_subcat                                            gs_name
  2. 17     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  3. 18     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  4. 19     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  5. 20     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  6. 21     C7 IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN

答案2

得分: 1

翻译后的内容如下：

使用 str_detect

  1. library(dplyr)
  2. library(stringr)
  3. all.gene.sets %>%
  4.   filter(str_detect(gs_name, str_c(redundant.gs, collapse = "|"), negate = TRUE))

输出

  1. # 一个表格: 5 × 3
  2.   gs_cat gs_subcat   gs_name                                           
  3.   <chr>  <chr>       <chr>                                             
  4. 1 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  5. 2 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  6. 3 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  7. 4 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  8. 5 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
英文:

With str_detect

  1. library(dplyr)
  2. library(stringr)
  3. all.gene.sets %&gt;%
  4.   filter(str_detect(gs_name, str_c(redundant.gs, collapse = &quot;|&quot;), negate = TRUE))

-output

  1. # A tibble: 5 &#215; 3
  2.   gs_cat gs_subcat   gs_name                                           
  3.   &lt;chr&gt;  &lt;chr&gt;       &lt;chr&gt;                                             
  4. 1 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  5. 2 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  6. 3 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  7. 4 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  8. 5 C7     IMMUNESIGDB GSE21063_3H_VS_16H_ANTI_IGM_STIM_NFATC1_KOBCELL_DN
  11. </details>

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  • 本文由 发表于 2023年3月23日 09:32:49
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