Help Session Lesson 5

All solutions should use the pipe.

Import the file "./data/filtlowabund_scaledcounts_airways.txt" and save to an object named sc. Create a subset data frame from sc that only includes the columns sample, cell, dex, transcript, and counts_scaled and only rows that include the treatment "untrt" and the transcripts "ACTN1" and "ANAPC4"?

Solution}

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

sc<-read_delim("../data/filtlowabund_scaledcounts_airways.txt")

## Rows: 127408 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (11): feature, SampleName, cell, dex, albut, Run, Experiment, Sample, Bi...
## dbl  (6): sample, counts, avgLength, TMM, multiplier, counts_scaled
## lgl  (1): .abundant
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

cnames<-c('sample', 'cell', 'dex', 'transcript', 'counts_scaled')
sc<-sc %>% select(all_of(cnames)) %>% filter(dex == "untrt" & (transcript %in% c("ACTN1","ANAPC4") ))

Using dexp ("./data/diffexp_results_edger_airways.txt") create a data frame containing the top 5 differentially expressed genes and save to an object named top5. Top genes in this case will have the smallest FDR corrected p-value and an absolute value of the log fold change greater than 2. See dplyr::slice().

Solution}

dexp<-read_delim("../data/diffexp_results_edger_airways.txt")

## Rows: 15926 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (4): feature, albut, transcript, ref_genome
## dbl (5): logFC, logCPM, F, PValue, FDR
## lgl (1): .abundant
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

top5<- dexp %>% dplyr::filter(abs(logFC) > 2) %>% arrange(FDR) %>% slice_head(n=5)

Filter sc to contain only the top 5 differentially expressed genes.

Solution}

sc %>% dplyr::filter(transcript %in%  top5$transcript)

## # A tibble: 0 × 5
## # ℹ 5 variables: sample <dbl>, cell <chr>, dex <chr>, transcript <chr>,
## #   counts_scaled <dbl>

Select only columns of type character from sc.

Solution}

sc %>% select(where(is.character))

## # A tibble: 8 × 3
##   cell    dex   transcript
##   <chr>   <chr> <chr>     
## 1 N61311  untrt ANAPC4    
## 2 N61311  untrt ACTN1     
## 3 N052611 untrt ANAPC4    
## 4 N052611 untrt ACTN1     
## 5 N080611 untrt ANAPC4    
## 6 N080611 untrt ACTN1     
## 7 N061011 untrt ANAPC4    
## 8 N061011 untrt ACTN1

Select all columns from dexp except .abundant and PValue. Keep only rows with FDR less than or equal to 0.01.

Solution}

dexp %>% select(-c(.abundant,PValue)) %>% filter(FDR <= 0.01)

## # A tibble: 2,763 × 8
##    feature         albut transcript ref_genome  logFC logCPM     F       FDR
##    <chr>           <chr> <chr>      <chr>       <dbl>  <dbl> <dbl>     <dbl>
##  1 ENSG00000000003 untrt TSPAN6     hg38       -0.390  5.06   32.8 0.00283  
##  2 ENSG00000000971 untrt CFH        hg38        0.417  8.09   29.3 0.00376  
##  3 ENSG00000001167 untrt NFYA       hg38       -0.509  4.13   44.9 0.00126  
##  4 ENSG00000002834 untrt LASP1      hg38        0.388  8.39   22.7 0.00722  
##  5 ENSG00000003096 untrt KLHL13     hg38       -0.949  4.16   84.8 0.000234 
##  6 ENSG00000003402 untrt CFLAR      hg38        1.18   6.90  130.  0.0000800
##  7 ENSG00000003987 untrt MTMR7      hg38        0.993  0.341  24.7 0.00585  
##  8 ENSG00000004059 untrt ARF5       hg38        0.358  5.84   30.9 0.00328  
##  9 ENSG00000004487 untrt KDM1A      hg38       -0.308  5.86   23.5 0.00663  
## 10 ENSG00000004700 untrt RECQL      hg38        0.360  5.60   22.7 0.00721  
## # ℹ 2,753 more rows

Import the file "./data/airway_rawcount.csv". Use the function rename() to rename the first column. Use the pipe from magrittr to import and rename successively without intermediate steps or function nesting. Save to an object named acount.

Solution}

acount<-read_csv("../data/airway_rawcount.csv") %>%
  dplyr::rename("Feature" = "...1")

## New names:
## Rows: 64102 Columns: 9
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (8): SRR1039508, SRR1039509, SRR1039512, SRR1039513, SRR1039516,
## SRR1039...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`

Use filter on the object acount to keep only genes that had a count greater than 10 in at least one sample
Solution}
```
bcount<- acount %>% 
  filter(if_any(where(is.numeric), ~.> 10))
```
Challenge Question: Filter genes from acount that had a total count less than ten across all samples. Hint: Use column_to_rownames and look up rowSums().
Solution}
```
f_acount<-acount %>% column_to_rownames("Feature") %>% filter(rowSums(.) > 10)
```