Deconvoluting pathogen infection RNAi screens with gespeR

In gespeR: Gene-Specific Phenotype EstimatoR

This file contains step-by-step instructions on how to obtain the data and deconvolute phenotypes from pathogen infection screens, as shown in gespeR: A statistical model for deconvoluting off-target-confounded RNA interference screens (Schmich et al., 2015). For full compatibility, please download the development version of gespeR at: https://github.com/fschmich/gespeR

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Downloading data

Phenotypic data

1. Go to https://pubchem.ncbi.nlm.nih.gov/assay/assay.cgi?aid=1117357&viewcode=X, replacing X with the viewcode provided (data is on hold until March 19, 2016).
2. At the top table, in the description field, click View All Data.
3. Scroll to the bottom of the page. Under Result Exports choose to save BioAssay as CSV file to /tmp/phenotypes.csv.
4. Download the mapping from PubChem to Vendor siRNA IDs from http://www.ncbi.nlm.nih.gov/pcsubstance/?term=%22InfectX+Consortium%22%5Bsourcename%5D. On the top right of the page, press Send To -> File -> Format: ID Map and save to /tmp/mapping.txt.

Target relation matrices

1. Go to https://www1.ethz.ch/bsse/cbg/software/gespeR.
2. Download the following siRNA-to-gene target relation matrices
   • Qiagen libraries to /tmp/QIAGEN.rds
   • Dharmacon library to /tmp/DHARMACON.rds
   • Validation library to /tmp/VALIDATION.rds
3. Alternatively, from within R, type

sapply(c("QIAGEN", "DHARMACON", "VALIDATION"), function(x) {
  download.file(url = sprintf("http://n.ethz.ch/~fschmich/gespeR/%s.rds", x),
                destfile = sprintf("/tmp/%s.rds", x), quiet = FALSE, mode = "wb")
})

Preprocessing data

Target relation matrices

require(Matrix)
require(gespeR)
# Construct TargetRelations objects for each library
Q <- TargetRelations("/tmp/QIAGEN.rds")
show(Q)

Phenotypic data

require(dplyr)
require(reshape2)
require(gespeR)
# Read phenotypes
phenotypes <- read.delim("/tmp/phenotypes.csv", 
                         sep = ",", stringsAsFactors = FALSE) %>% 
  tbl_df() %>%
  select(SID, GeneID = NCBI.Gene.ID, siRNASet = SIRNA_SET, contains("Infectivity")) %>% 
  melt(id.vars = c("SID", "siRNASet", "GeneID")) %>% 
  tbl_df() %>%
  select(SID = SID, GeneID, siRNASet, Pathogen = variable, Phenotype = value) %>%
  mutate(Pathogen = gsub("Infectivity_", "", Pathogen),
         SID = as.character(SID)) %>%
  filter(!is.na(Phenotype)) %>% # Artifact of how data is deposited in Pubchem
  arrange(SID)
head(phenotypes)

# Read ID mapping between SIDs and Vendor IDs
map <- read.delim("/tmp/mapping.txt", header = FALSE, stringsAsFactors = FALSE)
map <- map[seq(2, nrow(map), by = 2),]
map <- data.frame(t(sapply(map, function(x) {
  unlist(strsplit(x, split = "SID: | InfectX Consortium: "))[2:3]
})), stringsAsFactors = FALSE) %>% tbl_df()
rownames(map) <- NULL
colnames(map) <- c("SID", "VendorID")

# Map IDs
phenotypes <- left_join(phenotypes, map, by = "SID") %>% 
  tbl_df() %>% 
  select(SID, VendorID, siRNASet, GeneID, Pathogen, Phenotype)
head(phenotypes)

# Construct Phenotypes objects for each (Qiagen) library + pathogen combination
obs.ssp <- obs.gsp <- list()
phenotypes <- split(phenotypes, phenotypes$Pathogen)
for (pathogen in names(phenotypes)) {
  obs.ssp[[pathogen]] <- obs.gsp[[pathogen]] <- list()
  for (s in 1:4) {
    spl <- filter(phenotypes[[pathogen]], siRNASet == s, VendorID %in% Q@siRNAs)
    obs.ssp[[pathogen]][[s]] <- Phenotypes(phenotypes = Matrix(spl$Phenotype),
                                           ids = spl$VendorID, 
                                           pnames = c("Infectivity"),
                                           type = "SSP")
    spl.noNA <- filter(spl, !is.na(GeneID)) %>% 
      group_by(GeneID) %>% 
      summarise(Phenotype = mean(Phenotype, na.rm = TRUE)) %>% 
      filter(!is.nan(Phenotype))
    # We need gene-based Phenotypes objects for concordance evaluation
    obs.gsp[[pathogen]][[s]] <- Phenotypes(phenotypes = Matrix(spl.noNA$Phenotype), 
                                           ids = as.character(spl.noNA$GeneID), 
                                           pnames = c("Infectivity"),
                                           type = "GSP")
  }
}
show(obs.ssp$Bartonella[[1]])

Fitting gespeR models

require(gespeR)
# Fit gespeR models
ans.cv <- list()
for (pathogen in c("Bartonella", "Brucella", "Salmonella")) {
  ans.cv[[pathogen]] <- list()
  for (s in 1:4) {
    cat(sprintf("set: %d, pathogen: %s\n", s, pathogen))
    ges <- gespeR(phenotypes = obs.ssp[[pathogen]][[s]],
                                      target.relations = Q,
                                      mode = "cv",
                                      alpha = 0.5,
                                      ncores = 1)
    ans.cv[[pathogen]][[s]] <- unloadValues(ges, writeValues = FALSE)
  }
}

# Obtain gene-specific phenotypes (GSPs)
ges.gsp <- lapply(ans.cv, function(x) {
  lapply(x, gsp)
})

ges.gsp <- readRDS("~/gespeR_GSPs.rds")

Concordance evaluation

require(gespeR)
require(ggplot2)
# Function computes concordance between all pairs of phenotypes. Measures used
# are Spearman's correlation, rank-biased overlap and the Jaccard index.
# Observed phenotypes are cut to the same length as gespeR GSPs, respecting the
# proportion of negative and positive phenotypes, in order to guarantee fair
# comparison.
get.conc <- function(phen, cut = NULL) {
  min.overlap = 10
  rbo.k = 1000
  rbo.p = 1-1e-3
  rbo.mid <- 0
  cor.method = "spearman"
  uneven.lengths = TRUE
  if (!is.null(cut)) { # cut longer ranked lists to gespeR's lengths
    lapply(names(phen), function(x) {
      l <- lapply(cut[[x]], function(z) {
        ans <- as.data.frame(z)
        list(pos = length(which(ans$Infectivity > 0)), neg = length(which(ans$Infectivity < 0)))
      })
      phencut <- lapply(phen[[x]], function(y) {
          as.data.frame(y) %>% 
            tbl_df() %>%
            mutate(ID = as.character(ID)) %>%
            filter(!is.na(Infectivity)) %>%
            arrange(desc(Infectivity))
      })
      for (lib in 1:length(l)) {
        len <- l[[lib]]
        a <- nrow(phencut[[lib]]) - len$neg + 1
        b <- nrow(phencut[[lib]])
        phencut[[lib]] <- phencut[[lib]][c(1:len$pos, a:b),]
        phencut[[lib]] <- Phenotypes(phenotypes = Matrix(phencut[[lib]]$Infectivity),
                                     ids = phencut[[lib]]$ID, 
                                     pnames = c("Infectivity"),
                                     type = "SSP")
      }
      concordance(phencut, 
                  min.overlap = min.overlap, 
                  rbo.k = rbo.k, 
                  rbo.p = rbo.p,
                  cor.method = cor.method,
                  rbo.mid = rbo.mid,
                  uneven.lengths = uneven.lengths) %>% 
        data.frame %>% 
        select(-lisect) %>%
        melt(id.vars = c("test.pair", "phen")) %>%
        mutate(Method = "SSP", Pathogen = x) %>%
        select(-test.pair, Method, Pathogen, Measure = variable, value) %>%
        tbl_df()
    }) %>% do.call("rbind", .)
  } else {
    lapply(names(phen), function(x) {
      concordance(phen[[x]], 
                  min.overlap = min.overlap, 
                  rbo.k = rbo.k, 
                  rbo.p = rbo.p, 
                  cor.method = cor.method,
                  rbo.mid = rbo.mid,
                  uneven.lengths = uneven.lengths) %>% 
        data.frame %>% 
        select(-lisect) %>%
        melt(id.vars = c("test.pair", "phen")) %>%
        mutate(Method = "gespeR", Pathogen = x) %>%
        select(-test.pair, Method, Pathogen, Measure = variable, value) %>%
        tbl_df()
    }) %>% do.call("rbind", .)
  }
}
# Computation of concordance for gespeR GSPs and observed phenotypes
conc.gespeR <- get.conc(ges.gsp)
conc.obs <- get.conc(obs.gsp, cut = ges.gsp)

# Visualisation of concordance measures
dat <- rbind(conc.gespeR, conc.obs) %>% tbl_df() %>%
  mutate(Pathogen = factor(Pathogen, levels = c("Brucella", "Bartonella", "Salmonella"), 
                           labels = c("B. abortus", "B. henselae", "S. typhimurium")),
         Method = factor(Method, levels = c("gespeR", "SSP"), 
                         labels = c("gespeR", "Observed")))
dat$Measure <- factor(dat$Measure, 
                      levels = c("cor", "rbo.top", "rbo.bottom", "jaccard"), 
                      labels = c(expression(rho), expression(rbo["" %down% ""]),
                                 expression(rbo["" %up% ""]), expression("J")))
ggplot(data = dat, aes(x = Pathogen, y = value, colour = Method)) + 
      geom_boxplot(outlier.size = 0, width = 0.8) + 
      facet_grid(. ~ Measure, labeller = label_parsed) +
      xlab("") + ylab("") +
      scale_colour_manual("", values = c("#d7191c", "#525252"), drop = FALSE) +
      ylim(c(0, 1)) +
      theme_bw(base_size = 12, base_family = "Helvetica") + 
      theme(axis.text = element_text(size = rel(1.0)),
            axis.title = element_text(size = rel(1.0), face = "bold"),
            strip.text = element_text(size = rel(1.0), face = "bold"),
            axis.ticks = element_line(colour = "black"),
            legend.key = element_rect(colour = NA),
            legend.text = element_text(size = rel(1.0)),
            legend.title = element_text(size = rel(1.0), face = "bold"),
            panel.background = element_rect(fill = "white", colour = NA),
            panel.border = element_rect(fill = NA, colour = "grey50"),
            panel.grid.major = element_line(colour = "grey90", size = 0.2),
            panel.grid.minor = element_line(colour = "grey98", size = 0.5),
            strip.background = element_blank(),
            axis.text.x = element_text(angle = 45, hjust = 1))

Session information

sessionInfo()

gespeR documentation built on Nov. 8, 2020, 5:35 p.m.