Error: no applicable method for `RunTSNE` applied to an object of class "try-error"
1
0
Entering edit mode
moldach ▴ 20
@moldach-8829
Last seen 4.5 years ago
Canada/Montreal/Douglas Mental Health I…

I'm running into issues trying to catch errors thrown from Seurat::RunTSNE() inside of a function. When users input a small dataset we've noticed that this function will fail with the error:

Error in .checktsneparams(nrow(X), dims = dims, perplexity = perplexity, : perplexity is too large for the number of samples

To handle this condition I would like to run an alternative function and give the user a warning.

Typically one does so using try() or tryCatch(), for example:

exception_handling <- function() {
  tryCatch(
    expr = {
      message(RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE))
      message("Successfully executed the RunTSNE call.")
    },
    error = function(e) {
      message("Caught an error!")
      print(e)
    },
    warning = function(w) {
      message("Caught a warning!")
      print(w)
    },
    finally = {
      message("Reduce perplexity")
      warning_msg <- "Lowering perplexity to the lowest recommendation: 5."
      print(warning_msg)

      # Works with some functions
      lmfit <- lm(mpg ~ wt, mtcars)
      return(lmfit)
    }
  )
}

The output from exception_handling() is:

Call:
lm(formula = mpg ~ wt, data = mtcars)

Coefficients:
(Intercept)           wt  
     37.285       -5.344 

However, I run into an error when trying this with Seurat::RunTSEN():

exception_handling2 <- function(x) {
  tryCatch(
    expr = {
      message(RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE))
      message("Successfully executed the RunTSNE call.")
    },
    error = function(e) {
      message("Caught an error!")
      print(e)
    },
    warning = function(w) {
      message("Caught a warning!")
      print(w)
    },
    finally = {
      message("Reduce perplexity")
      warning_msg <- "Lowering perplexity to the lowest recommendation: 5."
      print(warning_msg)

      # Doesn't work for some function?
      dataSet_tSNE <- RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE, perplexity = 5)
      return(dataSet_tSNE)
    }
  )
}

Error in UseMethod(generic = "RunTSNE", object = object) : no applicable method for RunTSNE applied to an object of class "try-error"

Ultimately, I think I want to run a while-loop that reduces the number of perplexity until the function runs successfully (but not sure if this is the optimal way to do it)....

Maybe something like this:

dataSet <- try(RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE))
  while (class(dataSet) == "try-error") {
          cat("Caught an error relating to 'perplexity', reducing the number from default = 50")
          for (i in 49:5) {
                  dataSet <- RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE, perplexity = i)
                  }
          }
Seurat scRNA debugging exceptions unit-test • 2.0k views
ADD COMMENT
0
Entering edit mode
@martin-morgan-1513
Last seen 4 months ago
United States

Seurat isn't a Bioconductor package and your example isn't reproducible. However, for your use of tryCatch()...

An 'error' stops a calculation, whereas a 'warning' should be handled and the calculation continued. One usually uses tryCatch() for errors, but withCallingHandlers() for warnings.

Since the finally clause is always run, if one runs

i <- 0
tryCatch({
    i <- i + 1
    if (i == 1L)
        stop("oops")
}, finally = {
    i = i + 1
})

then the result is i = 2. Changing the initial value to i = 1, so the error doesn't occur, then result is i = 3. So you don't want to 'recover' in the finally= clause; a typical use might be to close a file connection that you opened in expr and that needs to be closed whether an error occurs or not.

If one wanted to try a value and then provide a 'recovery' value in case of error, one might write something like

f <- function(i) {
    tryCatch({
        if (i == 2)
            stop("oops")
        i
    }, error = function(e) {
        warning(e)                      # record error as warning
        NA                              # return NA
    })
}

f() returns its argument, unless an error occurs in which case it returns NA. It works as

> sapply(1:5, f)

[1]  1 NA  3  4  5
Warning message:
In doTryCatch(return(expr), name, parentenv, handler) : oops

This seems to be close to what you would like to do -- try a value of RunTSNE(), and if it fails recover in some way or another.

Can you take these ideas and come up with a second iteration of your approach?

ADD COMMENT
0
Entering edit mode

Hi Martin,

I didn't realize Seurat wasn't under the Bioconductor umbrella and apologize for not including a reprex - I've done so now.

Thank you very much for providing a very helpful answer anyways!

library(dplyr)
library(Seurat)
library(tibble)
# Download ~11Mb file - subset from SRA653146
download.file(url = "https://www37.zippyshare.com/d/6WAzFBtp/16064/SRA653146_subset.csv", destfile = "SRA653146_subset.csv") 

# read in raw counts
raw_counts <- read.csv("SRA653146_subset.csv")

# remove genes that are not expressed in any cell
raw_counts <- raw_counts[, colSums(raw_counts != 0) > 0]

# convert to matrix
rownames(raw_counts) <- raw_counts[,1]
raw_counts <- raw_counts[,-1]

# convert to Seurat object
dataSet <- CreateSeuratObject(counts = raw_counts, min.cells = 3, min.features = 200)

dataSet[["percent.mt"]] <- PercentageFeatureSet(dataSet, pattern = "^MT-") # add percentage mitochondria

# subset Seurat object
dataSet <- Seurat:::subset.Seurat(dataSet, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)

# normalize the data
dataSet <- Seurat::NormalizeData(dataSet, normalization.method = "LogNormalize", scale.factor = 10000)

# find highly variable features
dataSet <- FindVariableFeatures(dataSet, selection.method = "vst", nfeatures = 2000)

# scale and cluster
all.genes <- rownames(dataSet)
dataSet <- ScaleData(dataSet, features = all.genes)
dataSet <- RunPCA(dataSet, features = VariableFeatures(object = dataSet))
dataSet <- FindNeighbors(dataSet, reduction = "pca", dims = 1:20)
dataSet <- FindClusters(dataSet, resolution = 0.5, algorithm = 1)


f <- function() {
  tryCatch({
    if(dataSet <- RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE, perplexity = 50))
      stop("oops")
  }, error = function(e) {
    warning(e)                      # record error as warning
    dataSet <- RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE, perplexity = 5)
    dataSet
  })
}

dataSet_tSNE <- f()

This suggestion to provide a 'recovery' value (or function) in case of an error is almost 100% of the way there. If a perplexity value of 50 results in the error perplexity is too large for the number of samples a back-up function runs.

Ideally I would like a vectorized solution in the error = function(e){} that keeps trying lower values of perplexity until "it works", something like your sapply(49:5, f) example - although I cannot figure out how to implement this.

Only how to wrap multiple tryCatch()'s together but this is not optimal if your recursively trying perplexity parameters from 50 -> 5....

f2 <- function() {
  tryCatch({
    if(dataSet <- RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE, perplexity = 50))
      stop("oops")
  }, error = function(e) {
    warning(e)                      # record error as warning
    tryCatch({
      if(dataSet <- RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE, perplexity = 49))
        stop("oops2")
    }, 

   . . . # etc.

error = function(e) {
    warning(e)
    dataSet <- RunTSNE(object = dataSet, dims.use = 1:10, do.fast = TRUE, perplexity = 5)
    dataSet
  })
  })
}
ADD REPLY
0
Entering edit mode

If I had a function that only 'worked' for some small value of i, e.g.,

f <- function(i) {
    if (i < 10) {
        "ok"
    } else {
        stop("yuck")
    }
}

Naively I could write a loop that tried larger and then smaller values until it 'worked'

i <- 50
repeat {
    result <- tryCatch({
        f(i)
    }, error = function(e) {
        NA
    })
    if (!is.na(result))  # success!
        break            # exit loop
    i <- i - 5           # try new value
}

and after the loop i would be the first value to succeed

> i
[1] 9

A better approach would be to write a function that calls the function you're interested in, but returns a numerical value indicating how close one is to the 'best' result (e.g., largest value that returns a computed result)

g <- function(i) {
    tryCatch({
        f(i)
        i
    }, error = function(e) {
        0
    })
}

You can see how g() evaluates for different values of i -- it's maximized at the largest value below 10.

> sapply(1:20, g)
 [1] 1 2 3 4 5 6 7 8 9 0 0 0 0 0 0 0 0 0 0 0

We can find this maximum efficiently using optimize() or other base R functions (e.g., nlm(), uniroot(), perhaps re-defining g()), using

> optimize(g, c(1, 20), maximum = TRUE)
$maximum
[1] 9.999995

$objective
[1] 9.999995

I think though you should reflect on the overall strategy of choosing perplexity in this way.

ADD REPLY

Login before adding your answer.

Traffic: 510 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6