microbiome {scorematchingad}

16s Microbiome Data for Soil-Transmitted Helminths

Description

The microbiome data contains paired DNA samples from before treatment and 21 months after treatment for helminth infections (Martin et al. 2019). This data was analysed by Martin et al. (2019) and a further subset was studied by Scealy and Wood (2023). The data are from a study into the effect of helminth infections on the course of malaria infections (ImmunoSPIN-Malaria) in the Nangapanda subdistrict, Indonesia (Wiria et al. 2010). As part of the study, some participants were given 400mg of albendazole every three months for 1.5 years, remaining participants were given a placebo (Wiria et al. 2010).

Usage

microbiome

Format

A dataframe with 300 rows (two rows per individual) and 31 columns:

IndividualID

An integer uniquely specifying the individual.

Year

The collection year for the sample. 2008 for before treatment. 2010 for after treatment.

Sex

1 if female, 0 otherwise.

Treatment

TRUE if individual given 400mg of albendazole every three months for 1.5 years, FALSE otherwise.

Age

Age at first sample.

ct_Al

A Helminth measurement: The qPCR cycle threshold (CT) for Ascaris lumbricoides (large roundworm). Ascaris lumbricoides can be considered present if the value is 30 or less.

ct_Na

A Helminth measurement: The qPCR cycle threshold (CT) for Necator americanus (a hookworm). Necator americanus can be considered present if the value is 30 or less.

ct_Ad

A Helminth measurement: The qPCR cycle threshold (CT) for Ancylostoma duodenale (a hookworm). Ancylostoma duodenale can be considered present if the value is 30 or less.

micr_Tt

A Helminth measurement: The presence of Trichuris trichiura as determined by microscopy. A value of TRUE means Trichuris trichiura was detected.

Helminth

A Helminth measurement: If any of the above helminths were detected then TRUE, otherwise FALSE.

Remaining columns

Count prevalence of 18 bacterial phyla and 2 unclassified columns.

Details

The measurements in the data come from stool samples before and after treatment. Gut microbiome prevalence was measured using 16s rRNA 454 sequencing (Martin et al. 2019). Helminth infections were detected by PCR or microscopy (Martin et al. 2019).

The subset studied by Scealy and Wood (2023) contained only the measurements from before treatment, and only those individuals with a helminth infection. These measurements can be obtained by running

microbiome[(microbiome$Year == 2008) & microbiome$Helminth, ]

Two further individuals (IndividualID of 2079 and 2280) were deemed outliers by Scealy and Wood (2023).

Modifications from the Source

The microbiome data was created from the file S1_Table.xlsx hosted on Nematode.net at ⁠http://nematode.net/Data/environmental_interaction/S1_Table.xlsx⁠ using the below code.

microbiome <- readxl::read_excel("S1_Table.xlsx",
  range = "A3:AE303") #avoids the genus data, keeping - only phyla
metacolnames <- readxl::read_excel("S1_Table.xlsx",
  range = "A2:J2", 
  col_names = FALSE)
colnames(microbiome)[1:ncol(metacolnames)] <- metacolnames[1, ]
colnames(microbiome)[2] <- "Year"
microbiome[, 11] <- (microbiome$ct_Al <= 30) | (microbiome$ct_Na <= 30) |
  (microbiome$ct_Ad <= 30) | (microbiome$ct_St <= 30) |
  (microbiome$micr_Tt == 1)
colnames(microbiome)[11] <- "Helminth"
microbiome <- microbiome |>
  dplyr::mutate(across(c(1,2,3,12:31), as.integer)) |>
  dplyr::mutate(micr_Tt = as.logical(micr_Tt),
                Treatment = as.logical(Treatment)) |>
  dplyr::rename(IndividualID = `Individual ID`)
microbiome <- as.data.frame(microbiome)

Source

⁠http://nematode.net/Data/environmental_interaction/S1_Table.xlsx⁠ from ⁠http://nematode.net⁠. S1_Table.xlsx was created by Dr. Bruce A Rosa for Martin et al. (2019). Permission to share this data was obtained from Dr. Bruce Rosa and Dr. Ivonne Martin.

References

Martin I, Uh H, Supali T, Mitreva M, Houwing-Duistermaat JJ (2019). “The mixed model for the analysis of a repeated-measurement multivariate count data.” Statistics in Medicine, 38(12), 2248–2268. doi:10.1002/sim.8101.

Scealy JL, Wood ATA (2023). “Score matching for compositional distributions.” Journal of the American Statistical Association, 118(543), 1811–1823. doi:10.1080/01621459.2021.2016422.

Wiria AE, Prasetyani MA, Hamid F, Wammes LJ, Lell B, Ariawan I, Uh HW, Wibowo H, Djuardi Y, Wahyuni S, Sutanto I, May L, Luty AJ, Verweij JJ, Sartono E, Yazdanbakhsh M, Supali T (2010). “Does treatment of intestinal helminth infections influence malaria?” BMC Infectious Diseases, 10, 77. doi:10.1186/1471-2334-10-77.