• The book R packages is a straightaway and useful reference book for R developers. The free-access website of R packages is https://r-pkgs.org/. As a developer of R, if you haven't hear about it, it's strongly recommanded to just read it. Hadley Wickham, the main author of the book, is an active R developer and have led some master works like ggplot2 and plyr.

• With GSClassifier package, it could be easy for users to build a model only with certain gene sets and transcriptomics data. If you are interesting in sharing your model, GSClassifier also provides a simple methodology for this vision. In this section, let's see how to achieve it!

First, load the package

library(GSClassifier)

## Loading required package: luckyBase

With GSClassifier_Data(), all models supported in the current GSClassifier package would showed.

GSClassifier_Data()

## Available data:

##   ImmuneSubtype.rds 
##   PAD.train_20200110.rds

## Usage example:

##   PAD <- readRDS(system.file("extdata", "PAD.train_20200110.rds", package = "GSClassifier")) 
##   ImmuneSubtype <- readRDS(system.file("extdata", "ImmuneSubtype.rds", package = "GSClassifier"))

For more details of GSClassifier_Data(), just:

?GSClassifier_Data()

Set model=F, all .rds data would be showed:

GSClassifier_Data(model = F)

## Available data:

##   general-gene-annotation.rds 
##   ImmuneSubtype.rds 
##   PAD.train_20200110.rds 
##   testData.rds

## Usage example:

##   PAD <- readRDS(system.file("extdata", "PAD.train_20200110.rds", package = "GSClassifier")) 
##   ImmuneSubtype <- readRDS(system.file("extdata", "ImmuneSubtype.rds", package = "GSClassifier"))

Currently, a GSClassifier model and related product environments is designed as a list object. Let's take PAD.train_20210110(also called PADi) as an example.

PADi <- readRDS(system.file("extdata", "PAD.train_20200110.rds", package = "GSClassifier")) 

This picture shows the components of PADi:

As shown, a typical GSClassifier model is consist of four parts (with different colors in the picture):

• 1. ens:
  • Repeat: productive parameters of GSClassifier models
  • Model: GSClassifier models. Here, PADi had 20 models from different subs of the training cohorts
• 2. scaller:
  • Repeat: productive parameters of the scaller model, which was used for BestCall calling
  • Model: the scaller model
• 3. geneAnnotation: a data frame containing gene annotation information
• 4. geneSet: a list contains several gene sets

Thus, you can assemble your model like:

model <- list()

# bootstrap models based on the training cohort
model[['ens']] <- <Your model for subtypes calling>

# Scaller model
model[['scaller']] <- <Your scaller for BestCall calling>

# a data frame contarining gene annotation for IDs convertion
model[['geneAnnotation']] <- <Your gene annotation>

# Your gene sets
model[['geneSet']] <- <Your gene sets>

saveRDS(model, 'your-model.rds')

More tutorials for model establishment, please go to markdown tutorial or html tutorial.

Considering most users of GSClassifier might have no need for lots of models, We divided the model storage feature into an new ensembl package called luckyModel. Don't worry, the usage is very easy!

If you want to summit your model, you should apply for a contributor of luckyModel first. Then, just send the model (.rds) into the inst/extdata/<project> path of luckyModel. After audit, your branch would be accepted and available for the users.

The name of your model must be the format as following:

# <project>
GSClassifier

# <creator>_<model>_v<yyyymmdd>:
HWB_PAD_v20211201.rds

For repeatablility, you had better submit a .zip or .tar.gz file that containing the information of your model. Here are some suggestions:

• <creator>_<model>_v<yyyymmdd>.md

  • Destinations: Why you develop the model

  • Design: The evidence for gene sigatures, et al

  • Data sources: The data for model training and validating, et al

  • Applications: Where to use your model

  • Limintations: Limitation or improvement direction of your model

• <creator>_<model>_v<yyyymmdd>.R: The code you used for model training and validating.

• Data-of-<creator>_<model>_v<yyyymmdd>.rds (Optional): Due to huge size of omics data, it's OK for you not to submit the raw data.

💘 Welcome your contributions!

For convenience, we provided a general gene annotation dataset for different genomics:

gga <- readRDS(system.file("extdata", "general-gene-annotation.rds", package = "GSClassifier"))
names(gga)

## [1] "hg38" "hg19" "mm10"

I believe they're enough for routine medicine studies.

Here, take a look at hg38:

hg38 <- gga$hg38
head(hg38)

##           ENSEMBL       SYMBOL  ENTREZID
## 1 ENSG00000223972      DDX11L1 100287102
## 3 ENSG00000227232       WASH7P      <NA>
## 4 ENSG00000278267    MIR6859-1 102466751
## 5 ENSG00000243485 RP11-34P13.3      <NA>
## 6 ENSG00000284332    MIR1302-2 100302278
## 7 ENSG00000237613      FAM138A    645520

With this kind of data, it's simple to customize your own gene annotation (take PADi as examples):

tGene <- as.character(unlist(PADi$geneSet))
geneAnnotation <- hg38[hg38$ENSEMBL %in% tGene, ]
dim(geneAnnotation)

## [1] 32  3

Have a check:

head(geneAnnotation)

##              ENSEMBL SYMBOL ENTREZID
## 353  ENSG00000171608 PIK3CD     5293
## 1169 ENSG00000134686   PHC2     1912
## 2892 ENSG00000134247 PTGFRN     5738
## 3855 ENSG00000117090 SLAMF1     6504
## 3858 ENSG00000117091   CD48      962
## 4043 ENSG00000198821  CD247      919

This geneAnnotation could be the model[['geneAnnotation']].

Also, we use a function called convert to do gene ID convertion.

luckyBase::convert(c('GAPDH','TP53'), 'SYMBOL', 'ENSEMBL', hg38)

## [1] "ENSG00000111640" "ENSG00000141510"

Note: the luckyBase package integrates lots of useful tiny functions, you could explore it sometimes.