Merge pull request #270 from jpquast/developer

Developer

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: protti
 Title: Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools
-Version: 0.9.0
+Version: 0.9.1
 Authors@R: 
     c(person(given = "Jan-Philipp",
            family = "Quast",
@@ -43,7 +43,7 @@ Imports:
     methods,
     R.utils,
     stats
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Suggests: 
     testthat,
     covr,
@@ -67,7 +67,9 @@ Suggests:
     iq,
     scales,
     farver,
-    ggforce
+    ggforce,
+    xml2,
+    jsonlite
 Depends: 
     R (>= 4.0)
 URL: https://github.com/jpquast/protti, https://jpquast.github.io/protti/

NAMESPACE

@@ -134,6 +134,7 @@ importFrom(purrr,pluck)
 importFrom(purrr,pmap)
 importFrom(purrr,reduce)
 importFrom(purrr,set_names)
+importFrom(readr,read_csv)
 importFrom(readr,read_tsv)
 importFrom(readr,write_csv)
 importFrom(readr,write_tsv)

NEWS.md

@@ -1,3 +1,8 @@
+# protti 0.9.1
+
+## Bug fixes
+* `try_query()` now correctly handles errors that don't return a response object. We also handle gzip decompression problems better since some databases compressed responses were not handled correctly. 
+
 # protti 0.9.0
 
 ## New features 

R/calculate_protein_abundance.R

@@ -18,12 +18,11 @@
 #' for a protein to be included in the analysis. The default value is 3, which means
 #' proteins with fewer than three unique peptides will be excluded from the analysis.
 #' @param method a character value specifying with which method protein quantities should be
-#' calculated. Possible options include \code{"sum"}, which takes the sum of all precursor
-#' intensities as the protein abundance. Another option is \code{"iq"}, which performs protein
+#' calculated. Possible options include `"sum"`, which takes the sum of all precursor
+#' intensities as the protein abundance. Another option is `"iq"`, which performs protein
 #' quantification based on a maximal peptide ratio extraction algorithm that is adapted from the
 #' MaxLFQ algorithm of the MaxQuant software. Functions from the
-#' \href{https://academic.oup.com/bioinformatics/article/36/8/2611/5697917}{\code{iq}} package are
-#' used. Default is \code{"iq"}.
+#' `iq` package (\doi{10.1093/bioinformatics/btz961}) are used. Default is `"iq"`.
 #' @param for_plot a logical value indicating whether the result should be only protein intensities
 #' or protein intensities together with precursor intensities that can be used for plotting using
 #' \code{peptide_profile_plot()}. Default is \code{FALSE}.

R/data.R

@@ -33,7 +33,7 @@
 #' @format A data frame containing peptide level data from a Spectronaut report.
 #' @source Piazza, I., Beaton, N., Bruderer, R. et al. A machine learning-based chemoproteomic
 #' approach to identify drug targets and binding sites in complex proteomes. Nat Commun 11, 4200
-#' (2020). https://doi.org/10.1038/s41467-020-18071-x
+#' (2020). \doi{10.1038/s41467-020-18071-x}
 "rapamycin_10uM"
 
 #' Rapamycin dose response example data
@@ -47,13 +47,13 @@
 #' @format A data frame containing peptide level data from a Spectronaut report.
 #' @source Piazza, I., Beaton, N., Bruderer, R. et al. A machine learning-based chemoproteomic
 #' approach to identify drug targets and binding sites in complex proteomes. Nat Commun 11, 4200
-#' (2020). https://doi.org/10.1038/s41467-020-18071-x
+#' (2020). \doi{10.1038/s41467-020-18071-x}
 "rapamycin_dose_response"
 
 #' Structural analysis example data
 #'
 #' Example data used for the vignette about structural analysis. The data was obtained from
-#' \href{https://www.sciencedirect.com/science/article/pii/S0092867420316913}{Cappelletti 2021}
+#' Cappelletti et al. 2021 (\doi{10.1016/j.cell.2020.12.021})
 #' and corresponds to two separate experiments. Both experiments were limited proteolyis coupled to
 #' mass spectrometry (LiP-MS) experiments conducted on purified proteins. The first protein is
 #' phosphoglycerate kinase 1 (pgk) and it was treated with 25mM 3-phosphoglyceric acid (3PG).
@@ -69,7 +69,7 @@
 #' @source Cappelletti V, Hauser T, Piazza I, Pepelnjak M, Malinovska L, Fuhrer T, Li Y, Dörig C,
 #' Boersema P, Gillet L, Grossbach J, Dugourd A, Saez-Rodriguez J, Beyer A, Zamboni N, Caflisch A,
 #' de Souza N, Picotti P. Dynamic 3D proteomes reveal protein functional alterations at high
-#' resolution in situ. Cell. 2021 Jan 21;184(2):545-559.e22. doi: 10.1016/j.cell.2020.12.021.
+#' resolution in situ. Cell. 2021 Jan 21;184(2):545-559.e22. \doi{10.1016/j.cell.2020.12.021}.
 #' Epub 2020 Dec 23. PMID: 33357446; PMCID: PMC7836100.
 "ptsi_pgk"
 

R/fetch_eco.R

@@ -18,8 +18,7 @@
 #' essential to navigating the ever-growing (in size and complexity) corpus of scientific
 #' information."
 #'
-#' More information can be found in their
-#' \href{https://academic.oup.com/nar/article/47/D1/D1186/5165344?login=true}{publication}.
+#' More information can be found in their publication (\doi{10.1093/nar/gky1036}).
 #'
 #' @param return_relation a logical value that indicates if relational information should be returned instead
 #' the main descriptive information. This data can be used to check the relations of ECO terms to each other.

R/fetch_mobidb.R

@@ -17,7 +17,7 @@
 #' @return A data frame that contains start and end positions for disordered and flexible protein
 #' regions. The \code{feature} column contains information on the source of this
 #' annotation. More information on the source can be found
-#' \href{https://mobidb.bio.unipd.it/about/mobidb}{here}.
+#' \href{https://mobidb.org/about/mobidb}{here}.
 #' @import progress
 #' @importFrom rlang .data
 #' @importFrom purrr map_dfr keep

R/try_query.R

@@ -13,7 +13,6 @@
 #' @param type a character value that specifies the type of data at the target URL. Options are
 #' all options that can be supplied to httr::content, these include e.g.
 #' "text/tab-separated-values", "application/json" and "txt/csv". Default is "text/tab-separated-values".
-#' Default is "tab-separated-values".
 #' @param timeout a numeric value that specifies the maximum request time. Default is 60 seconds.
 #' @param accept a character value that specifies the type of data that should be sent by the API if
 #' it uses content negotiation. The default is NULL and it should only be set for APIs that use
@@ -22,6 +21,7 @@
 #'
 #' @importFrom curl has_internet
 #' @importFrom httr GET timeout http_error message_for_status http_status content accept
+#' @importFrom readr read_tsv read_csv
 #'
 #' @return A data frame that contains the table from the url.
 try_query <-
@@ -77,18 +77,56 @@ try_query <-
       return(invisible("No internet connection"))
     }
 
-    if (httr::http_error(query_result)) {
+    # If response was an error return that error message
+    if (inherits(query_result, "response") && httr::http_error(query_result)) {
       if (!silent) httr::message_for_status(query_result)
       return(invisible(httr::http_status(query_result)$message))
     }
 
+    # Handle other types of errors separately from query errors
+    if (inherits(query_result, "character")) {
+      if (!silent) message(query_result)
+      return(invisible(query_result))
+    }
+
     # Record readr progress variable to set back later
     readr_show_progress <- getOption("readr.show_progress")
     on.exit(options(readr.show_progress = readr_show_progress))
     # Change variable to not show progress if readr is used
     options(readr.show_progress = FALSE)
 
-    result <- suppressMessages(httr::content(query_result, type = type, encoding = "UTF-8", ...))
+    # Retrieve the content as raw bytes using httr::content
+    raw_content <- httr::content(query_result, type = "raw")
+    # Check for gzip magic number (1f 8b) before decompression
+    compressed <- length(raw_content) >= 2 && raw_content[1] == as.raw(0x1f) && raw_content[2] == as.raw(0x8b)
+
+    # Check if the content is gzip compressed
+    if (!is.null(query_result$headers[["content-encoding"]]) && query_result$headers[["content-encoding"]] == "gzip" && compressed) {
+      # Decompress the raw content using base R's `memDecompress`
+      decompressed_content <- memDecompress(raw_content, type = "gzip")
+
+      # Convert the raw bytes to a character string
+      text_content <- rawToChar(decompressed_content)
+
+      # Read the decompressed content based on the specified type
+      if (type == "text/tab-separated-values") {
+        result <- readr::read_tsv(text_content, ...)
+      } else if (type == "text/html") {
+        result <- xml2::read_html(text_content, ...)
+      } else if (type == "text/xml") {
+        result <- xml2::read_xml(text_content, ...)
+      } else if (type == "text/csv" || type == "txt/csv") {
+        result <- readr::read_csv(text_content, ...)
+      } else if (type == "application/json") {
+        result <- jsonlite::fromJSON(text_content, ...) # Using jsonlite for JSON parsing
+      } else if (type == "text") {
+        result <- text_content # Return raw text as-is
+      } else {
+        stop("Unsupported content type: ", type)
+      }
+    } else {
+      result <- suppressMessages(httr::content(query_result, type = type, encoding = "UTF-8", ...))
+    }
 
     return(result)
   }

README.Rmd

@@ -26,7 +26,7 @@ knitr::opts_chunk$set(
 
 The goal of **protti** is to provide flexible functions and workflows for proteomics quality control and data analysis, within a single, user-friendly package. It can be used for label-free DDA, DIA and SRM data generated with search tools and software such as Spectronaut, MaxQuant, Proteome Discoverer and Skyline. Both limited proteolysis mass spectrometry (LiP-MS) and regular bottom-up proteomics experiments can be analysed.
 
-**protti** is developed  and maintained by members of the lab of Paola Picotti at ETH Zurich. Our lab is focused on protein structural changes that occur in response to perturbations such as metabolite, drug and protein binding-events, as well as protein aggregation and enzyme activation ([Piazza 2018](https://www.sciencedirect.com/science/article/pii/S0092867417314484), [Piazza 2020](https://www.nature.com/articles/s41467-020-18071-x#additional-information), [Cappelletti, Hauser & Piazza 2021](https://www.sciencedirect.com/science/article/pii/S0092867420316913)). We have devoloped mass spectrometry-based structural and chemical proteomic methods aimed at monitoring protein conformational changes in the complex cellular milieu ([Feng 2014](https://www.nature.com/articles/nbt.2999)). 
+**protti** is developed  and maintained by members of the lab of Paola Picotti at ETH Zurich. Our lab is focused on protein structural changes that occur in response to perturbations such as metabolite, drug and protein binding-events, as well as protein aggregation and enzyme activation ([Piazza 2018](https://doi.org/10.1016/j.cell.2017.12.006), [Piazza 2020](https://doi.org/10.1038/s41467-020-18071-x), [Cappelletti, Hauser & Piazza 2021](https://doi.org/10.1016/j.cell.2020.12.021)). We have devoloped mass spectrometry-based structural and chemical proteomic methods aimed at monitoring protein conformational changes in the complex cellular milieu ([Feng 2014](https://doi.org/10.1038/nbt.2999)). 
 
 There is a wide range of functions **protti** provides to the user. The main areas of application are:
 

README.md

@@ -27,16 +27,12 @@ be analysed.
 Picotti at ETH Zurich. Our lab is focused on protein structural changes
 that occur in response to perturbations such as metabolite, drug and
 protein binding-events, as well as protein aggregation and enzyme
-activation ([Piazza
-2018](https://www.sciencedirect.com/science/article/pii/S0092867417314484),
-[Piazza
-2020](https://www.nature.com/articles/s41467-020-18071-x#additional-information),
-[Cappelletti, Hauser & Piazza
-2021](https://www.sciencedirect.com/science/article/pii/S0092867420316913)).
-We have devoloped mass spectrometry-based structural and chemical
-proteomic methods aimed at monitoring protein conformational changes in
-the complex cellular milieu ([Feng
-2014](https://www.nature.com/articles/nbt.2999)).
+activation ([Piazza 2018](https://doi.org/10.1016/j.cell.2017.12.006),
+[Piazza 2020](https://doi.org/10.1038/s41467-020-18071-x), [Cappelletti,
+Hauser & Piazza 2021](https://doi.org/10.1016/j.cell.2020.12.021)). We
+have devoloped mass spectrometry-based structural and chemical proteomic
+methods aimed at monitoring protein conformational changes in the
+complex cellular milieu ([Feng 2014](https://doi.org/10.1038/nbt.2999)).
 
 There is a wide range of functions **protti** provides to the user. The
 main areas of application are:
@@ -201,15 +197,17 @@ protein intensities.
 set.seed(42) # Makes example reproducible
 
 # Create synthetic data
-data <- create_synthetic_data(n_proteins = 100,
-                              frac_change = 0.05,
-                              n_replicates = 4,
-                              n_conditions = 2,
-                              method = "effect_random",
-                              additional_metadata = FALSE)
-
-# The method "effect_random" as opposed to "dose-response" just randomly samples 
-# the extend of the change of significantly changing peptides for each condition. 
+data <- create_synthetic_data(
+  n_proteins = 100,
+  frac_change = 0.05,
+  n_replicates = 4,
+  n_conditions = 2,
+  method = "effect_random",
+  additional_metadata = FALSE
+)
+
+# The method "effect_random" as opposed to "dose-response" just randomly samples
+# the extend of the change of significantly changing peptides for each condition.
 # They do not follow any trend and can go in any direction.
 ```
 
@@ -252,10 +250,12 @@ contains the normalised intensities.
 normalise it another time.*
 
 ``` r
-normalised_data <- data %>% 
-  normalise(sample = sample,
-            intensity_log2 = peptide_intensity_missing,
-            method = "median")
+normalised_data <- data %>%
+  normalise(
+    sample = sample,
+    intensity_log2 = peptide_intensity_missing,
+    method = "median"
+  )
 ```
 
 #### Assign Missingness
@@ -284,16 +284,18 @@ thresholds if you want to be more or less conservative with how many
 data points to retain.
 
 ``` r
-data_missing <- normalised_data %>% 
-  assign_missingness(sample = sample,
-                     condition = condition,
-                     grouping = peptide,
-                     intensity = normalised_intensity_log2,
-                     ref_condition = "condition_1",
-                     retain_columns = c(protein, change_peptide))
-
-# Next to the columns it generates, assign_missingness only contains the columns 
-# you provide as input in its output. If you want to retain additional columns you 
+data_missing <- normalised_data %>%
+  assign_missingness(
+    sample = sample,
+    condition = condition,
+    grouping = peptide,
+    intensity = normalised_intensity_log2,
+    ref_condition = "condition_1",
+    retain_columns = c(protein, change_peptide)
+  )
+
+# Next to the columns it generates, assign_missingness only contains the columns
+# you provide as input in its output. If you want to retain additional columns you
 # can provide them in the retain_columns argument.
 ```
 
@@ -317,16 +319,18 @@ missingness cutoffs also in order to define which comparisons are too
 incomplete to be trustworthy even if significant.
 
 ``` r
-result <- data_missing %>% 
-  calculate_diff_abundance(sample = sample,
-                           condition = condition,
-                           grouping = peptide,
-                           intensity_log2 = normalised_intensity_log2,
-                           missingness = missingness,
-                           comparison = comparison,
-                           filter_NA_missingness = TRUE,
-                           method = "moderated_t-test",
-                           retain_columns = c(protein, change_peptide))
+result <- data_missing %>%
+  calculate_diff_abundance(
+    sample = sample,
+    condition = condition,
+    grouping = peptide,
+    intensity_log2 = normalised_intensity_log2,
+    missingness = missingness,
+    comparison = comparison,
+    filter_NA_missingness = TRUE,
+    method = "moderated_t-test",
+    retain_columns = c(protein, change_peptide)
+  )
 ```
 
 Next we can use a Volcano plot to visualize significantly changing
@@ -335,15 +339,17 @@ interactive plot with the `interactive` argument. Please note that this
 is not recommended for large datasets.
 
 ``` r
-result %>% 
-  volcano_plot(grouping = peptide,
-                 log2FC = diff,
-                 significance = pval,
-                 method = "target",
-                 target_column = change_peptide,
-                 target = TRUE,
-                 legend_label = "Ground Truth",
-                 significance_cutoff = c(0.05, "adj_pval"))
+result %>%
+  volcano_plot(
+    grouping = peptide,
+    log2FC = diff,
+    significance = pval,
+    method = "target",
+    target_column = change_peptide,
+    target = TRUE,
+    legend_label = "Ground Truth",
+    significance_cutoff = c(0.05, "adj_pval")
+  )
 ```
 
 <img src="man/figures/README-volcano-1.png" width="100%" />

cran-comments.md

@@ -1,9 +1,7 @@
 ## Submission 
 
 * We specifically addressed and fixed the issue raised by Prof. Brian Ripley:
-  * The `analyse_functional_network()` function did not fail gracefully.
-  * We implemented a `try_catch()` that specifically rescues the cases in which the `STRINGdb` package does not fail gracefully. This fixes the issue.
-* Additionally we added new features and fixed bugs.
+  * We updated `try_query()` to also handle request unrelated errors successfully. 
 
 ## Test environments
 * macOS-latest (on GitHub actions), R 4.4.1