Could not find function "Infer.SourceContribution"

[kbazany]

Hi there,

I am trying to run FEAST for the first time and I got this error message:

Error in Infer.SourceContribution(source = sources, sinks = t(sinks), : could not find function "Infer.SourceContribution"

How do I go about getting this function? Thank you so much!

[liashenhav]

Can you share the code you're running?

[kbazany]

Thank you for getting back to me so quickly! I tried with some of my own data and with the demo data and got the same error message.

Thank you!

---

From: liashenhav @.> Sent: Thursday, November 3, 2022 12:37 PM To: cozygene/FEAST @.> Cc: Bazany,Kate @.>; Author @.> Subject: Re: [cozygene/FEAST] Could not find function "Infer.SourceContribution" (Issue #45)

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Can you share the code you're running?

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[liashenhav]

Happy to help, but can only do that using the code you're running. Please paste it here so we could try to recapitulate your error.

Thanks!

[kbazany]

Sorry about that! It didn't attach!

FEAST Source Tracker Analysis on 16S amplicon data

for sugarbeet drought project

Installing Packages

Packages <- c("Rcpp", "RcppArmadillo", "vegan", "dplyr", "reshape2", "gridExtra", "ggplot2", "ggthemes") install.packages(Packages) lapply(Packages, library, character.only = TRUE)

Loading packages

library(Rcpp) library(RcppArmadillo) library(vegan) library(dplyr) library(reshape2) library(gridExtra) library(ggplot2) library(ggthemes)

library(tidyverse) library(mctoolsr) library(cozygene) library(FEAST)

Installing FEAST

devtools::install_github("cozygene/FEAST")

library(FEAST)

Preparing Data

otutabtax <- read_delim("~/Desktop/R/MiCROPe/zotutab_16S_merged.txt", delim = "\t")

remove host reads (consider sample type and amplicon)

otutabtax$host <- 0 otuhost <- otutabtax %>% mutate(host = case_when( grepl("Rickettsiales", taxonomy) ~ "remove", grepl("Chloroplast", taxonomy) ~ "remove", TRUE ~ "keep")) otufilt <- filter(.data = otuhost, host == "keep")

write

mcotu2 <- otufilt %>% select(-host)

write files

write_delim(mcotu2, file = "mcotu2.txt", delim = "\t")

Input Data

input <- load_taxa_table("mcotu2.txt", "~/Desktop/R/MiCROPe/16S_Meta.txt")

rarefy

input_rar = single_rarefy(input, 5000)

filter out by treatment

input_rar <- filter_data(input_rar, "Treatment", filter_vals = "Drought") input_rar <- filter_data(input_rar, "Species", filter_vals = "Sugarbeet")

o16S_meta <- input_rar$map_loaded o16S_otu <- input_rar$data_loaded

put metadata and otu table in FEAST format

dim(o16S_meta) dim(o16S_otu)

otu_feast <- o16S_otu %>% rownames_to_column()

meta_feast <- o16S_meta %>% rownames_to_column(var = "SampleID") %>% mutate(SourceSink = case_when( endsWith(Sample, "Roots") ~ "Sink", endsWith(Sample, "Leaves") ~ "Sink", endsWith(Sample, "Soil") ~ "Source", endsWith(Sample, "Bulk") ~ "Source")) %>% mutate(id = case_when( endsWith(Site, "CO1") ~ 1, endsWith(Site, "CO3") ~ 2, endsWith(Site, "CO8") ~ 3, endsWith(Site, "CO10") ~ 4, endsWith(Site, "NE4") ~ 5, endsWith(Site, "NE7") ~ 6, endsWith(Site, "MTA") ~ 7, endsWith(Site, "MTB") ~ 8)) %>% mutate(Env = paste(Sample, id)) %>% select(SampleID, Env, SourceSink, id)

Note -- there may be an issue as it seems that there can only be one sink

per id. This isn't possible because I don't have enough bulk samples

I may need to do plant by plant

write files

write_delim(meta_feast, file =
"~/Desktop/R/MiCROPe/feast_results/meta_feast_16S_irrigated_corn_bulksoil_rootsleaves.txt", delim = "\t") write_delim(otu_feast, file = "~/Desktop/R/MiCROPe/feast_results/otu_feast_16S_irrigated_corn_bulksoil_rootsleaves.txt", delim = "\t")

go back in excel and delete the col heading for the OTUs

Read files

metadata <- Load_metadata(metadata_path = "~/Desktop/R/MiCROPe/feast_results/meta_feast_16S_irrigated_corn_bulksoil_rootsleaves.txt") otus <- Load_CountMatrix(CountMatrix_path = "~/Desktop/R/MiCROPe/feast_results/otu_feast_16S_irrigated_corn_bulksoil_rootsleaves.txt")

dir_path <- "~/Desktop/R/MiCROPe/feast_results/" outfile <- "16S_irrigated_corn_bulksoil_rootsleaves"

Run

FEAST_output <- FEAST(C = otus, metadata = metadata, different_sources_flag = 1, dir_path = dir_path, outfile = outfile)

FEAST <- function(C, metadata, EM_iterations = 1000, COVERAGE = NULL ,different_sources_flag, dir_path, outfile){

1. Parse metadata and check it has the correct hearer (i.e., Env, SourceSink, id)

if(sum(colnames(metadata)=='Env')==0) stop("The metadata file must contain an 'Env' column naming the source environment for each sample.") if(sum(colnames(metadata)=='SourceSink')==0) stop("The metadata file must contain a 'SourceSink' column indicating 'source' or 'sink' for each sample.") if(sum(colnames(metadata)=='id')==0) stop("The metadata file must contain an 'id' column matching the source environments for each sink sample.") sink_ids <- grep("sink",as.character(metadata$SourceSink),ignore.case=TRUE,value=FALSE) source_ids <- grep("source",as.character(metadata$SourceSink),ignore.case=TRUE,value=FALSE) metadata$SourceSink[sink_ids] = "Sink" metadata$SourceSink[source_ids] = "Source"

2. Enforce integer data

if(sum(as.integer(C) != as.numeric(C)) > 0){ stop('Data must be integral. Consider using "ceiling(datatable)" or ceiling(1000*datatable) to convert floating-point data to integers.') }

3. Extract only those samples in common between the two tables

common.sample.ids <- intersect(rownames(metadata), rownames(C)) C <- C[common.sample.ids,] metadata <- metadata[common.sample.ids,]

Double-check that the metadata file and otu table

had overlapping samples

if(length(common.sample.ids) <= 1) { message <- paste(sprintf('Error: there are %d sample ids in common '), 'between the metadata file and data table') stop(message) }

4. Extract number of sources and sinks from metadata

all_sinks_sampleID <- unique(rownames(metadata)[which(metadata$SourceSink == 'Sink')]) all_sources_sampleID <- unique(rownames(metadata)[which(metadata$SourceSink == 'Source')]) num_sinks <- length(all_sinks_sampleID) num_sources <- length(all_sources_sampleID) Ids <- na.omit(unique(metadata$id))

5. Extract environment information from metadata

envs <- paste0(metadata$Env, "_", rownames(metadata)) envs_sources <- paste0(all_sourcessampleID, "", metadata$Env[rownames(metadata) %in% all_sources_sampleID]) envs_sink <- paste0(all_sinkssampleID, "", metadata$Env[rownames(metadata) %in% all_sinks_sampleID])

proportions_mat <- matrix(NA, ncol = (num_sources+1), nrow = num_sinks) idx.unknown <- num_sources+1

Quantify the sources' contribution for each sink sample separately

for(it in 1:num_sinks){

if(it%%10==0 || it == num_sinks)
  print(paste0("Calculating mixinig proportions for sink ", it))

###6. Assign ids to sinks and their corresponding sources - for every sink (test.id) match its sources (train.ix)
if(different_sources_flag == 1){
  train.ix <- which(metadata$SourceSink=='Source' & metadata$id == Ids[it])
  test.ix <- which(metadata$SourceSink=='Sink' & metadata$id == Ids[it])
} else {
  train.ix <- which(metadata$SourceSink=='Source')
  test.ix <- which(metadata$SourceSink=='Sink' & metadata$id == Ids[it])
}

###7. Set the number of sources per sink
num_sources <- length(train.ix)

###8. Set defualt COVERAGE
if(is.null(COVERAGE))
  COVERAGE <-  min(rowSums(C[c(train.ix, test.ix),]))

if(COVERAGE > 0){

  ###9. Define sources and sinks
  if(length(train.ix) == 1)
    sources <- as.matrix(FEAST_rarefy(t(as.matrix(C[train.ix,])), COVERAGE))

  if(length(train.ix) > 1)
    sources <- as.matrix(FEAST_rarefy(C[train.ix,], COVERAGE))

  sinks <- as.matrix(FEAST_rarefy(t(as.matrix(C[test.ix,])), COVERAGE))

  ###10. Estimate source proportions for each sink
  FEAST_output<-Infer.SourceContribution(source=sources, sinks = t(sinks), env = envs[train.ix], em_itr = EM_iterations, COVERAGE = COVERAGE)

  idx.sources <- which(all_sources_sampleID %in% rownames(metadata)[train.ix])
  proportions_mat[it,c(idx.sources, idx.unknown)] <- FEAST_output$data_prop[,1]

} else{

  cat(sprintf('Error: the sequencing depth in one sink or source sample in Id %d is zero. No proportions were generated\n', it))

}

}

proportions_mat <- data.frame(proportions_mat) colnames(proportions_mat) <- c(envs_sources, "Unknown") rownames(proportions_mat) <- envs_sink

proportions_mat[is.na(proportions_mat)] <- 999

setwd(dir_path) write.table(proportions_mat, file = paste0(outfile,"_source_contributions_matrix.txt"), sep = "\t") return(list = c(proportions_mat, FEAST_output))

}

Running with my data

FEAST_output <- FEAST(C = otus, metadata = metadata, different_sources_flag = 1, dir_path = dir_path, outfile = outfile)

Trying with Demo Data

metadata <- Load_metadata(metadata_path = "https://raw.githubusercontent.com/cozygene/FEAST/master/Data_files/metadata_example_multi.txt") otus <- Load_CountMatrix(CountMatrix_path = "https://raw.githubusercontent.com/cozygene/FEAST/master/Data_files/otu_example_multi.txt")

FEAST_output <- FEAST(C = otus, metadata = metadata, different_sources_flag = 1, dir_path = "~/FEAST/Data_files/", outfile="demo")

PlotSourceContribution(SinkNames = rownames(FEAST_output)[c(5:8)], SourceNames = colnames(FEAST_output), dir_path = "~/FEAST/Data_files/", mixing_proportions = FEAST_output, Plottitle = "Test",Same_sources_flag = 0, N = 4)

[kbazany]

Hi, Sorry about that but I think I found it! I reran all of the function codes and now the demo data seems to be running. Thank you!

[liashenhav]

It looks like you're not running the FEAST function and it's parameters. You are running the code the composes this function (as you pasted above, you divide it to 8 sections)... That's the reason you're getting this error.... did you try running FEAST(C = otus, metadata = metadata, different_sources_flag = 1, dir_path = "~/FEAST/Data_files/", outfile="demo")?