README.md
In ABeav/vsgseqtools: Helpful Functions for VSG Sequencing Analysis
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)
#> # A tibble: 6 x 12
#> samplename mouse day tissue VSG Percent RPKM hit_VSG pct_id_vs_query
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 M13_D6_Ear M13 D6 Ear TRIN… 100 7.28e5 gnl|BL… 99.8
#> 2 M13_D6_Go… M13 D6 Gon TRIN… 100 9.64e5 gnl|BL… 99.7
#> 3 M13_D6_He… M13 D6 Heart TRIN… 100 7.28e5 gnl|BL… 99.8
#> 4 M13_D6_Lu… M13 D6 Lung TRIN… 100 7.28e5 gnl|BL… 99.8
#> 5 M13_D6_Su… M13 D6 Sub TRIN… 100 7.28e5 gnl|BL… 99.8
#> 6 M13_D6_bl… M13 D6 blood TRIN… 100 7.28e5 gnl|BL… 99.8
#> # … with 3 more variables: pct_id_vs_match <dbl>, match_VSG_length <dbl>,
#> # assembled_VSG_length <dbl>
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)
#> # A tibble: 285 x 6
#> mouse day tissue Percent cluster VSG
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 M13 D6 Ear 100 Cluster 72 TRINITY_DN478_c0_g1_i1_1_176_1568_M13_…
#> 2 M13 D6 Gon 100 Cluster 72 TRINITY_DN268_c0_g1_i1_1_44_1100_RC_M1…
#> 3 M13 D6 Heart 100 Cluster 72 TRINITY_DN77_c0_g1_i5_1_162_1554_M13_D…
#> 4 M13 D6 Lung 100 Cluster 72 TRINITY_DN12_c0_g1_i2_1_151_1543_M13_D…
#> 5 M13 D6 Sub 100 Cluster 72 TRINITY_DN236_c0_g1_i1_1_196_1588_M13_…
#> 6 M13 D6 blood 100 Cluster 72 TRINITY_DN0_c0_g2_i1_1_127_1519_RC_M13…
#> 7 M15 D10 Ear 100 Cluster 2 TRINITY_DN29_c0_g1_i1_1_62_1574_M15_D1…
#> 8 M15 D10 Gon 100 Cluster 2 TRINITY_DN243_c0_g1_i1_1_68_1580_M15_D…
#> 9 M15 D10 Heart 100 Cluster 2 TRINITY_DN4_c0_g1_i4_1_722_2234_M15_D1…
#> 10 M15 D10 Lung 100 Cluster 2 TRINITY_DN28_c0_g1_i1_1_68_1580_M15_D1…
#> # … with 275 more rows
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)
#> # A tibble: 52 x 4
#> mouse day tissue vsg_count
#> <chr> <chr> <chr> <int>
#> 1 M13 D6 blood 1
#> 2 M13 D6 Ear 1
#> 3 M13 D6 Gon 1
#> 4 M13 D6 Heart 1
#> 5 M13 D6 Lung 1
#> 6 M13 D6 Sub 1
#> 7 M15 D10 blood 23
#> 8 M15 D10 Ear 1
#> 9 M15 D10 Gon 1
#> 10 M15 D10 Heart 1
#> # … with 42 more rows
# 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)
#> # A tibble: 8 x 5
#> mouse day tissue Percent VSG
#> <chr> <chr> <chr> <dbl> <chr>
#> 1 M13 D6 blood 100 TRINITY_DN0_c0_g2_i1_1_127_1519_RC_M13_D6_blood
#> 2 M15 D6 blood 99.9 TRINITY_DN62_c0_g1_i1_1_85_1597_RC_M15_D6_blood
#> 3 M16 D6 blood 99.9 TRINITY_DN0_c0_g1_i1_1_80_1592_M16_D6_blood
#> 4 M23 D6 blood 99.9 TRINITY_DN0_c0_g1_i1_1_1803_3195_M23_D6_blood
#> 5 M39 D6 blood 91.5 TRINITY_DN0_c0_g1_i1_1_80_1592_M39_D6_blood
#> 6 M40 D6 blood 97.0 TRINITY_DN0_c0_g2_i1_1_191_1583_M40_D6_blood
#> 7 M42 D6 blood 99.9 TRINITY_DN0_c0_g1_i1_1_1722_3234_M42_D6_blood
#> 8 M45 D6 blood 96.0 TRINITY_DN0_c0_g1_i1_1_72_1464_RC_M45_D6_blood
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)
#> Joining, by = c("samplename", "VSG")
#> # A tibble: 14,820 x 3
#> samplename Percent VSG
#> <chr> <dbl> <chr>
#> 1 M13_D6_Ear 100 TRINITY_DN478_c0_g1_i1_1_176_1568_M13_D6_Ear
#> 2 M13_D6_Ear 0 TRINITY_DN268_c0_g1_i1_1_44_1100_RC_M13_D6_Gon_Fat
#> 3 M13_D6_Ear 0 TRINITY_DN77_c0_g1_i5_1_162_1554_M13_D6_Heart
#> 4 M13_D6_Ear 0 TRINITY_DN12_c0_g1_i2_1_151_1543_M13_D6_Lung
#> 5 M13_D6_Ear 0 TRINITY_DN236_c0_g1_i1_1_196_1588_M13_D6_Sub_Cu
#> 6 M13_D6_Ear 0 TRINITY_DN0_c0_g2_i1_1_127_1519_RC_M13_D6_blood
#> 7 M13_D6_Ear 0 TRINITY_DN29_c0_g1_i1_1_62_1574_M15_D10_Ear
#> 8 M13_D6_Ear 0 TRINITY_DN243_c0_g1_i1_1_68_1580_M15_D10_Gon_Fat
#> 9 M13_D6_Ear 0 TRINITY_DN4_c0_g1_i4_1_722_2234_M15_D10_Heart
#> 10 M13_D6_Ear 0 TRINITY_DN28_c0_g1_i1_1_68_1580_M15_D10_Lung
#> # … with 14,810 more rows
ABeav/vsgseqtools documentation built on Jan. 26, 2024, 7:22 p.m.
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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)
#> # A tibble: 6 x 12
#> samplename mouse day tissue VSG Percent RPKM hit_VSG pct_id_vs_query
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
#> 1 M13_D6_Ear M13 D6 Ear TRIN… 100 7.28e5 gnl|BL… 99.8
#> 2 M13_D6_Go… M13 D6 Gon TRIN… 100 9.64e5 gnl|BL… 99.7
#> 3 M13_D6_He… M13 D6 Heart TRIN… 100 7.28e5 gnl|BL… 99.8
#> 4 M13_D6_Lu… M13 D6 Lung TRIN… 100 7.28e5 gnl|BL… 99.8
#> 5 M13_D6_Su… M13 D6 Sub TRIN… 100 7.28e5 gnl|BL… 99.8
#> 6 M13_D6_bl… M13 D6 blood TRIN… 100 7.28e5 gnl|BL… 99.8
#> # … with 3 more variables: pct_id_vs_match <dbl>, match_VSG_length <dbl>,
#> # assembled_VSG_length <dbl>
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)
#> # A tibble: 285 x 6
#> mouse day tissue Percent cluster VSG
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 M13 D6 Ear 100 Cluster 72 TRINITY_DN478_c0_g1_i1_1_176_1568_M13_…
#> 2 M13 D6 Gon 100 Cluster 72 TRINITY_DN268_c0_g1_i1_1_44_1100_RC_M1…
#> 3 M13 D6 Heart 100 Cluster 72 TRINITY_DN77_c0_g1_i5_1_162_1554_M13_D…
#> 4 M13 D6 Lung 100 Cluster 72 TRINITY_DN12_c0_g1_i2_1_151_1543_M13_D…
#> 5 M13 D6 Sub 100 Cluster 72 TRINITY_DN236_c0_g1_i1_1_196_1588_M13_…
#> 6 M13 D6 blood 100 Cluster 72 TRINITY_DN0_c0_g2_i1_1_127_1519_RC_M13…
#> 7 M15 D10 Ear 100 Cluster 2 TRINITY_DN29_c0_g1_i1_1_62_1574_M15_D1…
#> 8 M15 D10 Gon 100 Cluster 2 TRINITY_DN243_c0_g1_i1_1_68_1580_M15_D…
#> 9 M15 D10 Heart 100 Cluster 2 TRINITY_DN4_c0_g1_i4_1_722_2234_M15_D1…
#> 10 M15 D10 Lung 100 Cluster 2 TRINITY_DN28_c0_g1_i1_1_68_1580_M15_D1…
#> # … with 275 more rows
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)
#> # A tibble: 52 x 4
#> mouse day tissue vsg_count
#> <chr> <chr> <chr> <int>
#> 1 M13 D6 blood 1
#> 2 M13 D6 Ear 1
#> 3 M13 D6 Gon 1
#> 4 M13 D6 Heart 1
#> 5 M13 D6 Lung 1
#> 6 M13 D6 Sub 1
#> 7 M15 D10 blood 23
#> 8 M15 D10 Ear 1
#> 9 M15 D10 Gon 1
#> 10 M15 D10 Heart 1
#> # … with 42 more rows
# 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)
#> # A tibble: 8 x 5
#> mouse day tissue Percent VSG
#> <chr> <chr> <chr> <dbl> <chr>
#> 1 M13 D6 blood 100 TRINITY_DN0_c0_g2_i1_1_127_1519_RC_M13_D6_blood
#> 2 M15 D6 blood 99.9 TRINITY_DN62_c0_g1_i1_1_85_1597_RC_M15_D6_blood
#> 3 M16 D6 blood 99.9 TRINITY_DN0_c0_g1_i1_1_80_1592_M16_D6_blood
#> 4 M23 D6 blood 99.9 TRINITY_DN0_c0_g1_i1_1_1803_3195_M23_D6_blood
#> 5 M39 D6 blood 91.5 TRINITY_DN0_c0_g1_i1_1_80_1592_M39_D6_blood
#> 6 M40 D6 blood 97.0 TRINITY_DN0_c0_g2_i1_1_191_1583_M40_D6_blood
#> 7 M42 D6 blood 99.9 TRINITY_DN0_c0_g1_i1_1_1722_3234_M42_D6_blood
#> 8 M45 D6 blood 96.0 TRINITY_DN0_c0_g1_i1_1_72_1464_RC_M45_D6_blood
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)
#> Joining, by = c("samplename", "VSG")
#> # A tibble: 14,820 x 3
#> samplename Percent VSG
#> <chr> <dbl> <chr>
#> 1 M13_D6_Ear 100 TRINITY_DN478_c0_g1_i1_1_176_1568_M13_D6_Ear
#> 2 M13_D6_Ear 0 TRINITY_DN268_c0_g1_i1_1_44_1100_RC_M13_D6_Gon_Fat
#> 3 M13_D6_Ear 0 TRINITY_DN77_c0_g1_i5_1_162_1554_M13_D6_Heart
#> 4 M13_D6_Ear 0 TRINITY_DN12_c0_g1_i2_1_151_1543_M13_D6_Lung
#> 5 M13_D6_Ear 0 TRINITY_DN236_c0_g1_i1_1_196_1588_M13_D6_Sub_Cu
#> 6 M13_D6_Ear 0 TRINITY_DN0_c0_g2_i1_1_127_1519_RC_M13_D6_blood
#> 7 M13_D6_Ear 0 TRINITY_DN29_c0_g1_i1_1_62_1574_M15_D10_Ear
#> 8 M13_D6_Ear 0 TRINITY_DN243_c0_g1_i1_1_68_1580_M15_D10_Gon_Fat
#> 9 M13_D6_Ear 0 TRINITY_DN4_c0_g1_i4_1_722_2234_M15_D10_Heart
#> 10 M13_D6_Ear 0 TRINITY_DN28_c0_g1_i1_1_68_1580_M15_D10_Lung
#> # … with 14,810 more rows
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