In ABeav/vsgseqtools: Helpful Functions for VSG Sequencing Analysis

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vsgseqtools

A collection of functions that helps analyze and tidy up the output from VSG sequencing. The purpose of this package is to be used specifically for analyzing data produced from the VSGSeqPipeline.

Installation

library(devtools)
devtools::install_github("ABeav/vsgseqtools")

Examples

There are several helpful tools in this package. Below is an example of a few tools that help read in and analyze VSGSeqPipeline results.

library(tidyverse) # The tidyverse is required
library(vsgseqtools)

## This function accurately reads in the results from the raw VSGSeqPipeline output
results <- vsg_read("mock_data/mock_RESULTS.txt")
head(results)

The vsg_rename() function allows you to add a column for the clusters produced by cd-hit in the VSGSeqpipeline. However, you do need to provide a reference that has each VSG name and its associated cluster name (I do this using this script MatchVSGs.py).

vsg_rename(results, "mock_data/cluster_reference_table.txt") %>%
  select(mouse, day, tissue, Percent, cluster, VSG)

Often when analyzing VSG expression, we count the total number of VSGs in a sample. The vsg_count function helps streamline this with one line of code.

results %>%
  vsg_count(mouse, day, tissue) 

# This can also be plotted directly after

results %>%
  vsg_count(mouse, day, tissue) %>%
  ggplot(aes(x = tissue, y = vsg_count)) + 
  facet_wrap("day") +
  geom_boxplot() +
  geom_dotplot(binaxis='y', stackdir= "center" , dotsize=.6, position=position_dodge(.75)) +
  xlab("") +
  ylab("Number of VSGs") +
  theme(axis.text = element_text(angle = 30,hjust = 1, size = 16, color = 'black'))

It can be helpful to look at the most highly expressed VSG in each sample as well. I created a function, vsg_max, to help pull the maximum expressed VSG out easily. This is particularly useful to find the starting VSG expressed in an infection on day 6.

results %>%
  filter(day == "D6", tissue == "blood") %>%
  vsg_max(mouse) %>% 
  select(mouse, day, tissue, Percent, VSG)

The last function I wanted to highlight is little more niche. vsg_expand is helpful if you want to create a data frame that has includes all VSGs found and represents them in each sample. VSGs that were not found in a sample are represented by having a Percent of 0, meaning 0% of parasites expressed that VSG.

 vsg_expand(results, "samplename", "VSG") %>%
  select(samplename, Percent, VSG)

ABeav/vsgseqtools documentation built on Jan. 26, 2024, 7:22 p.m.