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IMAP: Integrated Microbiome Analysis Pipelines: End-to-End Practical User Guides Using Integrated Approaches

3 Exploring Sample Metadata

3.1 Load required libraries 

library(tidyverse)
knitr::opts_chunk$set(comment = NA)

3.2 Read size

The size of the reads can tell us how big is the dataset and can help to estimate the computational needs.

Top five smallest project by read size

read_csv("results/read_size_asc.csv", show_col_types = FALSE) %>% 
head(5)
# A tibble: 5 × 3
  run        bioproject   bases
  <chr>      <chr>        <dbl>
1 SRR3725509 PRJNA322554    581
2 SRR901080  PRJNA208226  89298
3 SRR3725412 PRJNA322554 100541
4 SRR901106  PRJNA208226 114181
5 SRR3725402 PRJNA322554 117824

Top five biggest project by read size

read_csv("results/read_size_desc.csv", show_col_types = FALSE) %>% 
head(5)
# A tibble: 5 × 3
  run         bioproject      bases
  <chr>       <chr>           <dbl>
1 SRR10245303 PRJNA477349 434919320
2 SRR10245294 PRJNA477349 382616209
3 SRR10245352 PRJNA477349 316807915
4 SRR10245293 PRJNA477349 274398756
5 SRR10245330 PRJNA477349 265408834

3.3 Compare read size by BioProject


3.4 Read size by variables within a project