mcanouil / nacho

NAnostring quality Control dasHbOard

Hi 👋,
I am Mickaël!

I am currently working as a consultant in Biostatistics, with a strong expertise in the field of genetics,
i.e., genotyping, sequencing, proteomics, metabolomics, transcriptomics, etc.
in Lille & Paris, France.
Additionally, I am also a freelance, available for hire to work on R and Quarto related projects.

And when I am not working, I like watching movies (3,417) and playing with my black Labrador Retriever named Saga!

---

nacho's Issues

Function to render a report based on the results from `visualise()`

Figures and details about the quality-control and normalisation of NanoString datasets are only available through the use of visualise().

A function to render a comprehensive report of what has be done during the normalisation and QC process within the map (or with default parameters) might be useful.

Pos and neg controls in RCC file

Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.

---

Hey Nacho team, when reading in my RCC files, each one harbors the internal positive and negative controls, labeled POS and NEG. How does this affect the predict housekeeping genes?

# insert reprex here

Release NACHO 2.0.6

Prepare for release:

• git pull
• Check current CRAN check results
• Polish NEWS
• usethis::use_github_links()
• urlchecker::url_check()
• devtools::build_readme()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• revdepcheck::revdep_check(num_workers = 4)
• git push

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version(push = TRUE)

Function template for custom `norm_` functions

A "template function" as trans_ functions from ggplot2 and scales could be implemented, to allow users to use their own normalisation methods (for the one not yet built-in).

Add normalisations from https://cran.r-project.org/package=ruv

excluding POS_F from positive control linearity calculation

Hi,

Thank you for your efforts again. Was the positive control F (POS_F) included in positive control linearity calculation?

Based on the code here it seems like all positive probes were used. Nanostring suggests excluding POS_F from this calculation since it is below the limit of detection. Here is the related part in nanostring documentation:

"Note that because POS_F has a known concentration of 0.125 fM, which is considered below the limit of detection of the system, it should be excluded from this calculation (although you will see that POS_F counts are significantly higher than the negative control counts in most cases)."

Thank you!

Question about sample sheet contents and formatting

Hi,
I have a question about formatting the input files. I'm using the load_rcc() function and am not clear on the required structure and contents of the sample sheet. I have all my RCC files together in one folder and that seems to be right. For a sample sheet, right now csv I imported with just a single column of RCC file names and "IDFILE" as the header. I'm looking at the GSE74821 example and see a ton more columns. What is the minimum information needed in the sample sheet? As it is, when I run the load_rcc function with my one column sample sheet I get the following error message:

#> Error in switch(EXPR = class(ssheet_csv), data.frame = ssheet_csv, character = utils::read.csv(file = ssheet_csv,  : EXPR must be a length 1 vector

here's what I did below:

mytargets <- read_csv("mytargets.csv")
#> Error in read_csv("mytargets.csv"): could not find function "read_csv"

mydata <- load_rcc(
  data_directory = "pathname", # Where the data is
  ssheet_csv = mytargets, #This is just a list of file names under column name "IDFILE"
  id_colname = "IDFILE", # Name of the column that contains the unique identfiers
  housekeeping_genes = NULL, # not sure where this fits in. WOuld this list of housekeeping genes be in the sample sheet somehow?
  housekeeping_predict = TRUE, # Whether or not to predict the housekeeping genes
  normalisation_method = "GEO", # Geometric mean or GLM
  n_comp = 5 # Number indicating how many principal components should be computed. 
)

Thank you very much!

Issue with "Outliers Table"

The outliers table is not shown properly due to wrong call to datatable.

library(NACHO)
data(GSE74821)
visualise(GSE74821)

datatable error

datatable results

Is it possible to upload a sample sheet using the NACHO shiny app?

This would enable to customize the plots, for example coloring by group, treatment, timepoint, etc rather than cartridgeID.

Release NACHO 2.0.0

Prepare for release:

• Check current CRAN check results
• Polish NEWS
• devtools::build_readme()
• urlchecker::url_check()
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• revdepcheck::revdep_check(num_workers = 4)
• Review pkgdown reference index for, e.g., missing topics

Submit to CRAN:

• usethis::use_version('major')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

Release NACHO 1.0.0

Prepare for release:

• Travis CI
• AppVeyor CI
• devtools::check()
• devtools::check_win_devel()
• rhub::check_for_cran()
• rhub::check_on_macos()
• Polish NEWS
• Polish pkgdown

Submit to CRAN:

• usethis::use_version('major')
• Update cran-comments.md
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• CRAN checks
• usethis::use_dev_version()
• Tweet

Update the vignette with `render()` and `autoplot()` functions

Add some examples of the newly implemented functions:

• render()
• autoplot()

Plus an example of how to use print() within a Rmarkdown document.

Changing housekeeping genes seems effectless

NACHO still guess housekeeping genes even when a list of housekeeping genes is provided.

---

library(GEOquery)
gse <- getGEO("GSE70970")
targets <- pData(phenoData(gse[[1]]))
getGEOSuppFiles(GEO = "GSE70970", baseDir = tempdir())
untar(
  tarfile = paste0(tempdir(), "/GSE70970/GSE70970_RAW.tar"), 
  exdir = paste0(tempdir(), "/GSE70970/Data")
)
# Add IDs
targets$IDFILE <- list.files(paste0(tempdir(), "/GSE70970/Data"))

library(NACHO)
GSE70970_sum <- summarise(
  data_directory = paste0(tempdir(), "/GSE70970/Data"), # Where the data is
  ssheet_csv = targets, # The samplesheet
  id_colname = "IDFILE", # Name of the column that contains the identfiers
  housekeeping_genes = NULL, # Custom list of housekeeping genes
  housekeeping_predict = TRUE, # Predict the housekeeping genes based on the data?
  normalisation_method = "GEO", # Geometric mean or GLM
  n_comp = 5 # Number indicating the number of principal components to compute. 
)
#> [NACHO] Importing RCC files.
#> [NACHO] Performing QC and formatting data.
#> [NACHO] Searching for the best housekeeping genes.
#> [NACHO] Computing normalisation factors using "GEO" method for housekeeping genes prediction.
#> [NACHO] The following predicted housekeeping genes will be used for normalisation:
#>   - hsa-miR-103
#>   - hsa-let-7e
#>   - hsa-miR-1260
#>   - hsa-miR-500+hsa-miR-501-5p
#>   - hsa-miR-1274b
#> [NACHO] Computing normalisation factors using "GEO" method.
#> [NACHO] Missing values have been replaced with zeros for PCA.
#> [NACHO] Normalising data using "GEO" method with housekeeping genes.
#> [NACHO] Returning a list.
#>   $ access              : character
#>   $ housekeeping_genes  : character
#>   $ housekeeping_predict: logical
#>   $ housekeeping_norm   : logical
#>   $ normalisation_method: character
#>   $ remove_outliers     : logical
#>   $ n_comp              : numeric
#>   $ data_directory      : character
#>   $ pc_sum              : data.frame
#>   $ nacho               : data.frame
#>   $ outliers_thresholds : list
#>   $ raw_counts          : data.frame
#>   $ normalised_counts   : data.frame

unlink(paste0(tempdir(), "/GSE70970"), recursive = TRUE)

my_housekeeping <- GSE70970_sum[["housekeeping_genes"]][-c(1, 2)]

GSE70970_norm <- normalise(
  nacho_object = GSE70970_sum,
  housekeeping_genes = my_housekeeping,
  housekeeping_norm = TRUE,
  normalisation_method = "GEO", 
  remove_outliers = TRUE
)
#> [NACHO] Normalising "GSE70970_sum" with new value for parameters:
#>   - housekeeping_genes = TRUE
#>   - remove_outliers = TRUE
#> [NACHO] Searching for the best housekeeping genes.
#> [NACHO] Computing normalisation factors using "GEO" method for housekeeping genes prediction.
#> [NACHO] The following predicted housekeeping genes will be used for normalisation:
#>   - hsa-let-7e
#>   - hsa-miR-1260
#>   - hsa-miR-1274b
#>   - hsa-miR-103
#>   - hsa-miR-16
#> [NACHO] Computing normalisation factors using "GEO" method.
#> [NACHO] Missing values have been replaced with zeros for PCA.
#> [NACHO] Returning a list.
#>   $ access              : character
#>   $ housekeeping_genes  : character
#>   $ housekeeping_predict: logical
#>   $ housekeeping_norm   : logical
#>   $ normalisation_method: character
#>   $ remove_outliers     : logical
#>   $ n_comp              : numeric
#>   $ data_directory      : character
#>   $ pc_sum              : data.frame
#>   $ nacho               : data.frame
#>   $ outliers_thresholds : list
#>   $ raw_counts          : data.frame
#>   $ normalised_counts   : data.frame


GSE70970_sum[["housekeeping_genes"]]
#> [1] "hsa-miR-103"                "hsa-let-7e"                
#> [3] "hsa-miR-1260"               "hsa-miR-500+hsa-miR-501-5p"
#> [5] "hsa-miR-1274b"
my_housekeeping
#> [1] "hsa-miR-1260"               "hsa-miR-500+hsa-miR-501-5p"
#> [3] "hsa-miR-1274b"
GSE70970_norm[["housekeeping_genes"]]
#> [1] "hsa-let-7e"    "hsa-miR-1260"  "hsa-miR-1274b" "hsa-miR-103"  
#> [5] "hsa-miR-16"

Documentation / Interface Mismatch

Hi!

the documentation for normalize() suggests that the function creates a list of normalized counts etc pp - however, I only get a Nacho Object back (which is fine but different than expected given the documentation). Maybe this is a relict?

Unified plotting between `visualise()` and `render()` (using new autoplot)

Create functions to call inside visualise() and render() (#2) for all QC plots.
This will makes the package maintenance easier and more reliable to change in plots.

Note: shinyMeta might appear in future versions.

example in vignette error

if I follow the example in the vignette I encounter this error:

Add IDs

targets$IDFILE <- list.files(paste0(tempdir(), "/GSE70970/Data"))
library(NACHO)

Attaching package: 'NACHO'

The following object is masked from 'package:BiocGenerics':

normalize

GSE70970_sum <- summarise(

• data_directory = paste0(tempdir(), "/GSE70970/Data"), # Where the data is
• ssheet_csv = targets, # The samplesheet
• id_colname = "IDFILE", # Name of the column that contains the identfiers
• housekeeping_genes = NULL, # Custom list of housekeeping genes
• housekeeping_predict = TRUE, # Predict the housekeeping genes based on the data?
• normalisation_method = "GEO", # Geometric mean or GLM
• n_comp = 5 # Number indicating the number of principal components to compute.
• )
  [NACHO] Importing RCC files.
  Error: Column cols must be length 1 (the number of rows), not 3

Question about R^2 values in positive control linearity QC

Hi,

Thank you for the great tool. I have a question on calculation of the positive control linearity QC. Based on NACHO's QC plot output and Nanostring's guide it should be "R^2 value". When I looked at the calculation function for this metric, it is actually calculating Pearson's correlation coefficient here . Just wanted to ask if you are aware of this or am I missing a point?

Thank you!

PlexSet Analysis

Hey guys,

This looks like an awesome tool! I was wondering if NACHO supported PlexSet analysis where each well/lane is multiplexed with up to 8 samples? If so, it looks like the load_rcc function requires each sample to be a separate file so should we demultiplex each well/lane?

Thanks in advance for your help!
Yan

CRAN checks notes ("Undefined global functions or variables")

Fix CRAN checks notes produced by the new render_nacho()

* checking R code for possible problems ... NOTE
print_nacho: no visible binding for global variable ‘CartridgeID’
print_nacho: no visible binding for global variable ‘CodeClass’
print_nacho: no visible binding for global variable ‘Name’
print_nacho: no visible binding for global variable ‘Count’
print_nacho: no visible binding for global variable ‘.’
print_nacho: no visible binding for global variable ‘MC’
print_nacho: no visible binding for global variable ‘BD’
print_nacho: no visible binding for global variable ‘MedC’
print_nacho: no visible binding for global variable ‘X.PC’
print_nacho: no visible binding for global variable ‘Y.PC’
print_nacho: no visible binding for global variable ‘Proportion of
  Variance’
print_nacho: no visible binding for global variable
  ‘ProportionofVariance’
print_nacho: no visible binding for global variable ‘Negative_factor’
print_nacho: no visible binding for global variable ‘Positive_factor’
print_nacho: no visible binding for global variable ‘House_factor’
print_nacho: no visible binding for global variable ‘Count_Norm’
print_nacho: no visible binding for global variable ‘Status’
Undefined global functions or variables:
  . BD CartridgeID CodeClass Count Count_Norm House_factor MC MedC Name
  Negative_factor Positive_factor Proportion of Variance
  ProportionofVariance Status X.PC Y.PC

input in `visualise()` are wrongly updated when switching tabs

QC threshold slider in visualise() is wrongly updated in "Binding Density" when switching between "MAX/FLEX" and "SPRINT" instrument measures.

library(NACHO)
data(GSE74821)
visualise(GSE74821)

Release NACHO 1.1.0

Prepare for release:

• devtools::build_readme()
• Check current CRAN check results
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• urlchecker::url_check()
• Update cran-comments.md
• Polish NEWS

Submit to CRAN:

• usethis::use_version('minor')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

Release NACHO 1.0.2

Prepare for release (2020-01-05):

• devtools::build_readme()
• Check current CRAN check results
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• Update cran-comments.md
• Polish NEWS
• Review pkgdown reference index for, e.g., missing topics

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

Change to new cran checks badge URL

👋🏽 I maintain the cran checks badges. Please change to the new cran checks badge URL (e.g., https://badges.cranchecks.info/worst/dplyr.svg). Old badges at (e.g. https://cranchecks.info/badges/worst/dplyr) will be unavailable as of Jan 1st 2023.

• Blog post from June 2022 announcing the change: https://recology.info/2022/06/cran-checks-badges/
• New badges repo: https://github.com/r-hub/cchecksbadges
• Old badges repo: https://github.com/sckott/cchecksapi

RCC files not read properly for NanoString PlexSet data

When reading Plexset RCC files which include 8 samples named Endogenous*s with * from 1 to 8.
The Code_Summary column is not unnested properly, i.e., a two-level list-column instead of a simple list-column.

library(NACHO)
.data <- summarise(
  data_directory = paste(params[["data_directory"]], "RCC", sep = "/"),
  ssheet_csv = paste(output_directory, "sample_sheet.csv", sep =  "/"),
  id_colname = "IDFILE",
  housekeeping_genes = NULL,
  housekeeping_predict = FALSE,
  housekeeping_norm = TRUE,
  normalisation_method = "GLM",
  n_comp = 10
)
# > Error in `[[<-.data.frame`(`*tmp*`, "CodeClass", value = character(0)) : replacement has 0 rows, data has 5760

This issue might araise other issues later for normalisation and visualisation.

Using NACHO for single catridge assays

Hi,

Thanks for this great tool.

We were trying to look on how to use NACHO with a single cartridge (12 samples). You have mentioned in the manual - "Each sample in the plots can be coloured based on either technical specifications which are included in the RCC files or based on specifications of your own choosing, though these specifications need to be included in the samplesheet.". I added one column as sample annotations in the samplesheet. The dynamic report was generated with Catridge Id as the grouping variable. When I changed grouping variable to ID, it shows 1-12 numbers rather than sample annotation I have given in the samplesheet.

Can you please help on this to bring the sample annotations rather than 1-12 numbers for the samples?

Thanks,
Athul

Provide easy means to export normalized Counts

Thanks for creating this tool / method! The Shiny App / general functionality is really an improvement and works pretty good. However, I'd like to export the data (e.g. normalized counts) to a separate TSV/CSV file to do downstream analyses with it, which is currently not very straightforward.

It would be very nice to see an export functionality - either in the Shiny App or (also good) in the R package in general. Couldn't find anything unfortunately...

Release NACHO 0.6.0 / 0.6.1

Prepare for release:

• devtools::check()
• devtools::check_win_devel()
• rhub::check_for_cran()
• Polish NEWS
• Update cran-comments.md

Submit to CRAN (7th of October):

• usethis::use_version('minor')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• GitHub release
• usethis::use_dev_version()

Check again (CRAN checks failed for system requirements) on the 8th of October:

• Travis CI
• AppVeyor CI
• devtools::check()
• devtools::check_win_devel()
• rhub::check() (all CRAN platforms)
• Update cran-comments.md
• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• GitHub release
• CRAN checks
• usethis::use_dev_version()
• Tweet

Visualisation after normalise function

Hi
I get a crash every time when I try to visualise the data after "renormalisation" even with the example data:

Error in visualise(nacho_norm) : 
  [NACHO] Mandatory fields are missing in "nacho_norm"!
  "load_rcc()" and/or "normalise()" must be called before "visualise()".

Here the code that I copy paste, I have this issue R 3.6.1/3.6.2 linux and mac.

  library(GEOquery)
  library(NACHO)

  # Import data from GEO
  gse <- GEOquery::getGEO(GEO = "GSE74821")
  targets <- Biobase::pData(Biobase::phenoData(gse[[1]]))
  GEOquery::getGEOSuppFiles(GEO = "GSE74821", baseDir = tempdir())
  utils::untar(
    tarfile = file.path(tempdir(), "GSE74821", "GSE74821_RAW.tar"),
    exdir = file.path(tempdir(), "GSE74821")
  )
  targets$IDFILE <- list.files(
    path = file.path(tempdir(), "GSE74821"),
    pattern = ".RCC.gz$"
  )
  targets[] <- lapply(X = targets, FUN = iconv, from = "latin1", to = "ASCII")
  utils::write.csv(
    x = targets,
    file = file.path(tempdir(), "GSE74821", "Samplesheet.csv")
  )

  # Read RCC files and format
  nacho <- load_rcc(
    data_directory = file.path(tempdir(), "GSE74821"),
    ssheet_csv = file.path(tempdir(), "GSE74821", "Samplesheet.csv"),
    id_colname = "IDFILE"
  )
  nacho_norm <- normalise(
    nacho_object = nacho,
    normalisation_method = "GLM",
    remove_outliers = TRUE
  )
  visualise(nacho_norm)

Differing Values in QC plots

Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.

---

Hi, I observed differences in the values in the QC plots like the ``BD` plot on repeated execution. See the code below that was executed multiple times.

library(NACHO)

# load RCC files
nacho_data <- load_rcc(data_directory = input_rcc_path,
                       ssheet_csv = input_samplesheet,
                       id_colname = "RCC_FILE_NAME")

plot_bd <- autoplot(
  object = nacho_data,
  x = "BD",
  colour = "CartridgeID",
  size = 0.5,
  show_legend = TRUE
)

I guess this might be due to the use of position_jitter(width = 0.25) in auto_plot.

NACHO/R/autoplot.R

Line 328 in 7784ecd

position = ggplot2::position_jitter(width = 0.25)

Although a horizontal shift of the data points might be desirable, the default value of the height parameter (seems to be NULL) seems to have an effect and shift the data points verrtically as well.

This difference can be observed executing the code below.

library(ggplot2)

jitter <- position_jitter(width = 0.1)
ggplot(mtcars, aes(am, vs)) +
  geom_point(position = jitter)

jitter2 <- position_jitter(width = 0.1, height = 0.0)
ggplot(mtcars, aes(am, vs)) +
  geom_point(position = jitter2)

I guess this is not expected. Please let me know if you need more information.

Background normalization?

Does NACHO provide / perform Background normalization methods / functionality via Background subtraction or thresholding? I just saw the normalization methods available when running load_rcc and normalize() but these seem not to include such a normalization approach.

Keys are shared for 2 rows

When trying to import many samples from different runs, this results in the following error message:

Brief description of the problem

 nacho_data <- load_rcc(data_directory = params$path_rccs, 
+                        ssheet_csv = params$path_rcc_samplesheet,
+                        id_colname = "FILENAME")
[NACHO] Importing RCC files.
|=======================================================================================================================================================================================================================================================================================|100% ~0 s remaining     
[NACHO] Performing QC and formatting data.
[NACHO] Computing normalisation factors using "GEO" method.
Error: Each row of output must be identified by a unique combination of keys.
Keys are shared for 2 rows:
* 6, 10

Release NACHO 1.0.1

Prepare for release:

• devtools::build_readme()
• Check current CRAN check results
• devtools::check(remote = TRUE, manual = TRUE)
• devtools::check_win_devel()
• rhub::check_for_cran()
• Update cran-comments.md
• Polish NEWS
• Review pkgdown reference index for, e.g., missing topics

Submit to CRAN:

• usethis::use_version('patch')
• devtools::submit_cran()
• Approve email

Wait for CRAN...

• Accepted 🎉
• usethis::use_github_release()
• usethis::use_dev_version()

Move to `data.table` framework

Move from tidyverse to data.table framework.

Add citation details

Add citation file to cite the package using the upcoming article from Bioinformatics.

load_rcc doesnt recognize subfolder

Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.

---

I am trying to implement NACHO on our nanostring data. I am have extreme trouble reading in the RCC files. Our data structure is a follows, I have a master folder RCC and a subfolder for each seq run. This subfolder has 12 samples. The number on each cartridge flow cell. I want to loop through each subfolder and have load_rcc do this. However, list.files() does not work with load_rcc. this returns warnings saying that load_rcc can find the file paths. Any help would be appreciated. However, when I put all rcc files into one folder, load_rcc works fine.

# insert reprex here

Error: invalid first argument

Thanks for your amazing package.
@mcanouil

getwd()
library(NACHO)
setwd("/home/zhou/raid/TOPACIO_Oncopanel/TOPACIO_Nanostring/mRNA")
plexset_nacho<-load_rcc("/home/zhou/raid/TOPACIO_Oncopanel/TOPACIO_Nanostring/mRNA",
                        ssheet_csv = ssheet_csv,
                        id_colname = "id_colname"
                        )

however, there are errors.

[NACHO] Importing RCC files.
|=================================================|100% ~0 s remaining     
[NACHO] Performing QC and formatting data.
Error: invalid first argument
Run `rlang::last_error()` to see where the error occurred.

> rlang::last_error()
<error/rlang_error>
invalid first argument
Backtrace:
  1. NACHO::load_rcc(...)
 12. base::.handleSimpleError(...)
 13. tidyselect:::h(simpleError(msg, call))
Run `rlang::last_trace()` to see the full context.

Column `cols` must be length 1 (the number of rows), not 3 while running Summarize

Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on https://stackoverflow.com/ or https://community.rstudio.com/.

Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.

---

Hi,

Tried to run NACHO as mentioned in the supplementary file. Data from GEO is downloaded and Samplesheet.csv is also generated. But once I am using Summarize function it shows "Error: Column cols must be length 1 (the number of rows), not 3". The reprex() shows different error.
Thanks in advance.
Athul

nacho <-summarize(data_directory =paste0(getwd(), "/GSE70970"),ssheet_csv =paste0(getwd(), "/GSE70970/Samplesheet.csv"),id_colname = "IDFILE",housekeeping_predict = TRUE, normalisation_method = "GEO",n_comp = 10)
#> Error in summarize(data_directory = paste0(getwd(), "/GSE70970"), ssheet_csv = paste0(getwd(), : could not find function "summarize"

Update to tidyr 1.0.0

Update calls to tidyr functions with v1.0.0.

---

library(NACHO)
GSE70970_sum <- summarise(
  data_directory = paste0(tempdir(), "/GSE70970/Data"), # Where the data is
  ssheet_csv = targets, # The samplesheet
  id_colname = "IDFILE", # Name of the column that contains the identfiers
  housekeeping_genes = NULL, # Custom list of housekeeping genes
  housekeeping_predict = TRUE, # Predict the housekeeping genes based on the data?
  normalisation_method = "GEO", # Geometric mean or GLM
  n_comp = 5 # Number indicating the number of principal components to compute. 
)
#> [NACHO] Importing RCC files.
#> Warning: unnest() has a new interface. See ?unnest for details.
#> Try `df %>% unnest(c(Header, Sample_Attributes, Lane_Attributes))`, with `mutate()` if needed
#> Warning: The `.drop` argument of `unnest()` is deprecated as of tidyr 1.0.0.
#> All list-columns are now preserved.
#> This warning is displayed once per session.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: unnest() has a new interface. See ?unnest for details.
#> Try `df %>% unnest(c(Header, Sample_Attributes, Lane_Attributes))`, with `mutate()` if needed

Add Sanity check for files homogeneity

Issue raised in #19

Allow named vector for RCC file in `load_rcc`?

Allow to provide to load_rcc a named vector to provide path to RCC along with the samples' labels?

This means, that no outside information can be provided.

Recode `visualise()` to make it simpler to maintain

Currently, visualise() includes many nested 'if else' among other things.
Jointly with externalise plotting function (#4) , the shiny app could be simplified to enhance controls about input requirements.