SLAFEEL: R scripts and reformatted data analyzed by Alamil et al. (2019)

SLAFEEL: Statistical Learning Approach For Estimating Epidemiological Links from deep sequencing data

This archive contains R scripts for running analyses proposed by Alamil et al. (2019; Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases), namely
- functions.R that contains R functions required for computations,
- influenza.R, ebola.R and potyvirus.R where the analyses are implemented for each case study, and
- influenza-format-genomic-data.R giving an example of how to format data to be used in the statistical learning approach.

This archive also contains the reformatted data analyzed by Alamil et al. (2019). The datasets that are provided concern swine influenza virus (reformatted from Murcia et al., 2012), Ebola virus (reformatted from Gire et al., 2014) and a wild salsify potyvirus. Two rds files are provided for swine influenza, the first one for the naive chain, the second one for the vaccinated chain. Ebola rds files are compressed into the archive ebolaRDS.zip. rds files can be loaded in the R statistical software with the command "readRDS(filename)", which returns a list. The list contains a "readme" item describing the contents of the list, as well as a "host.table" item providing metadata about host units and a "set.of.sequences" item providing sequencing data formatted in numeric matrices.

Murcia PR, Hughes J, Battista P, Lloyd L, Baillie GJ, Ramirez-Gonzalez RH, et al. Evolution of an Eurasian avian-like influenza virus in naive and vaccinated pigs. PLoS Pathogens. 2012;8(5):e1002730.

Gire SK, Goba A, Andersen KG, Sealfon RS, Park DJ, Kanneh L, et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science. 2014;345:1369–1372

Funded by the ANR - Project name: SMITID (2016-2020) - Grant number: ANR-16-CE35-0006