CODEWITHSUNDEEP
r
user432797
user432797

Reputation: 603

Filter in rows of all columns with specific conditions

I'm still learning R, I have this dataset, it has 5 columns, first column is tracking_id, the next four columns have values of four groups.

First, I want to filter rows that have values equal or larger than 1, then I want to filter rows based on comparison of the last three columns ("CD44hi_CD69low_rep","CD44hi_CD69hi_CD103low_rep","CD44hi_CD69hi_CD103hi_rep") that are 8 folds higher or 4 folds lower compared to column ("CD44low_rep").

The output should have 5 columns, with values equal or larger than 1 that are 8 fold higher or 4 fold less of the last three column compared to second column.

I should get something like this:

enter image description here

To filter rows equal or larger than 1, I tried this:

df1 %>% select_if(is.numeric) %>%  filter_all(all_vars(. >= 1))

Then to filter 8 folds high or 4 fold less, I tried (thanks to @akrun):

nm1 <- c("CD44hi_CD69low_rep",  "CD44hi_CD69hi_CD103low_rep", 
         "CD44hi_CD69hi_CD103hi_rep")
i1 <- (rowSums(df1[nm1]  >= (df1$CD44low_rep * 8)) == 3) &
     (rowSums(df1[nm1]  <= (df1$CD44low_rep * 4)) == 3)

However, I'm getting no input.

I'm following these steps:

Analysis and graphic display of RNA-Seq data. A total of 9,085 genes for which
the maximum fragments per kilobase of exon per million mapped reads value in all
samples was ≥1.0 were subjected to further analyses. A principal component analysis
was performed using R (https://www.r-project.org/). Clustering was performed using
APCluster (an R Package for Affinity Propagation Clustering). The transcriptional
signatures of CD44hiCD69lo, CD44hiCD69hiCD103lo and CD44hiCD69hiCD103hi CD4+
T cells were defined with genes for which the expression was eightfold higher or
fourfold lower than that in CD44loCD69lo CD4+ T cells.
For the visualization of the co-regulation network, the 500 genes in the CD44hi
CD4+ T cell groups that showed the greatest variation compared with the naive
(CD44loCD69lo) CD4+ T cell group were subjected to further analyses. The first-
neighbor genes were determined using the following two criteria: (1) a correlation
of >0.8; and (2) a ratio of norm of 0.8–1.25. The network graph of 483 genes was
visualized using Cytoscape (http://www.cytoscape.org/).

The IDs that I'm interested in are:

values <- c('S100a10', 'Esm1', 'Itgb1', 'Anxa2', 'Hist1h1b', 
                                                'Il2rb', 'Lgals1', 'Mki67', 'Rora', 'S100a4', 
                                                'S100a6', 'Adam8', 'Areg', 'Bcl2l1', 'Calca', 
                                                'Capg', 'Ccr2', 'Cd44', 'Csda', 'Ehd1', 
                                                'Id2', 'Il10', 'Il1rl1', 'Il2ra', 'Lmna', 
                                                'Maf', 'Penk', 'Podnl1', 'Tiam1', 'Vim',
                                                'Ern1', 'Furin', 'Ifng', 'Igfbp7', 'Il13', 
                                                'Il4', 'Il5', 'Nrp1', 'Ptprs', 'Rbpj', 
                                                'Spry1', 'Tnfsf11', 'Vdr', 'Xcl1', 'Bmpr2', 
                                                'Csf1', 'Dst', 'Foxp3', 'Itgav', 'Itgb8', 
                                                'Lamc1', 'Myo1e', 'Pmaip1', 'Prdm1', 'Ptpn5', 
                                                'Ramp1', 'Sdc4')

After applying @RonakShah (thank you!), I get only 21 instead of 57:

library(dplyr)
df09 <- read.csv('https://raw.githubusercontent.com/learnseq/learning/main/dfpilot.csv')

filtertrial <- df09 %>% 
  #Keep rows where all the values are greater than 1
  filter(across(where(is.numeric), ~. >= 1)) %>%
  #Rows where any value is higher than 8 times CD44low_rep
  #Or 4 times less than CD44low_rep
  filter(Reduce(`|`, across(CD44hi_CD69low_rep:CD44hi_CD69hi_CD103hi_rep, 
         ~. >= CD44low_rep*8 | . <= CD44low_rep/4)))

values <- c('S100a10', 'Esm1', 'Itgb1', 'Anxa2', 'Hist1h1b', 
                                                'Il2rb', 'Lgals1', 'Mki67', 'Rora', 'S100a4', 
                                                'S100a6', 'Adam8', 'Areg', 'Bcl2l1', 'Calca', 
                                                'Capg', 'Ccr2', 'Cd44', 'Csda', 'Ehd1', 
                                                'Id2', 'Il10', 'Il1rl1', 'Il2ra', 'Lmna', 
                                                'Maf', 'Penk', 'Podnl1', 'Tiam1', 'Vim',
                                                'Ern1', 'Furin', 'Ifng', 'Igfbp7', 'Il13', 
                                                'Il4', 'Il5', 'Nrp1', 'Ptprs', 'Rbpj', 
                                                'Spry1', 'Tnfsf11', 'Vdr', 'Xcl1', 'Bmpr2', 
                                                'Csf1', 'Dst', 'Foxp3', 'Itgav', 'Itgb8', 
                                                'Lamc1', 'Myo1e', 'Pmaip1', 'Prdm1', 'Ptpn5', 
                                                'Ramp1', 'Sdc4')

#Make sure the sorting won't change by using match function and reverse it to get the right order as 
#shown in the original plot.
dfgll <- filtertrial %>% slice(match(rev(values), tracking_id))


dfgll

How to achieve this?

Upvotes: 0

Views: 544

Answers (1)

Ronak Shah
Ronak Shah

Reputation: 389325

You can try the following :

library(dplyr)
df <- read.csv('https://raw.githubusercontent.com/learnseq/learning/main/dfpilot.csv')

df %>% 
  #Keep rows where all the values are greater than 1
  filter(across(where(is.numeric), ~. > 1)) %>%
  #Rows where any value is higher than 8 times CD44low_rep
  #Or 4 times less than CD44low_rep
  filter(Reduce(`|`, across(CD44hi_CD69low_rep:CD44hi_CD69hi_CD103hi_rep, 
         ~. > CD44low_rep*8 | . < CD44low_rep/4))) -> result

head(result)  

#    tracking_id CD44low_rep CD44hi_CD69low_rep CD44hi_CD69hi_CD103low_rep
#1         42624        1.91               6.68                      17.50
#2 A930005H10Rik        9.41               4.63                       1.48
#3         Actn1       42.01              21.77                       1.71
#4       Adora2a        1.31               7.05                      15.51
#5         Ahnak       12.09             152.43                     362.87
#6        Als2cl       11.17               1.98                       1.01

#  CD44hi_CD69hi_CD103hi_rep
#1                     22.51
#2                      1.55
#3                      1.22
#4                     13.31
#5                    299.07
#6                      1.26

Upvotes: 1

Related Questions