Quick start

About

Although with bright prospects in Pan-disease analysis, GSClassifier was primarily developed for clinic-friendly immune subtypes of gastric cancer (GC). Currently, only PAD subtypes and PADi for GC were supported. We would try to support more cancer types in the future as possible. More details in Plans in the future section.
Gibbs' PanCancer immune subtypes based on five gene signatures (485 genes) could also be called in GSClassifier, with a pre-trained model from the ImmuneSubtypeClassifier package. If you use their jobs, please cite: references.
Particularly, all normal tissues should be eliminated before subtypes calling for cancer research.

Data

To lower the learning cost of GSClassifier, we provides some test data:

library(GSClassifier)

## Loading required package: luckyBase

testData <- readRDS(system.file("extdata", "testData.rds", package = "GSClassifier"))

Explore the testData:

names(testData)

## [1] "Kim2018_3"              "PanSTAD_phenotype_part" "PanSTAD_expr_part"

PAD

1. Preparation of the test data

load phenotype data:

design <- testData$PanSTAD_phenotype_part
table(design$Dataset)

## 
##  GSE13861  GSE13911  GSE15459  GSE22377  GSE26899  GSE26901  GSE29272  GSE51105 
##        65        39       192        43        96       108       134        94 
##  GSE54129  GSE57303  GSE62254  GSE65801  GSE84437 TCGA-STAD 
##       111        70       300        32       433       372

load target sample IDs in GSE54129 cohort:

target_ID <- design$ID[design$Dataset %in% 'GSE54129']
expr <- testData$PanSTAD_expr_part[,target_ID]
head(expr[,1:10])

##                 GSM1308413 GSM1308414 GSM1308415 GSM1308416 GSM1308417
## ENSG00000122122   7.888349   7.623663   6.873493   6.961102   7.150572
## ENSG00000117091   7.051760   6.217445   5.651839   5.830996   5.908532
## ENSG00000163219   6.056472   5.681844   5.411533   5.652684   5.555147
## ENSG00000136167   9.322191   8.765794   8.502315   8.838166   8.845952
## ENSG00000005844   7.119594   6.023631   5.400999   6.172863   6.059838
## ENSG00000123338   7.204051   6.925328   6.259809   6.610681   6.595882
##                 GSM1308418 GSM1308419 GSM1308420 GSM1308421 GSM1308422
## ENSG00000122122   7.871423   6.953329   8.334037   6.764335   6.522554
## ENSG00000117091   6.526917   5.646446   6.617520   5.637693   5.742848
## ENSG00000163219   5.962885   5.361763   5.975842   5.330428   5.172705
## ENSG00000136167   9.366074   8.675718   9.118517   8.614068   8.114096
## ENSG00000005844   6.523530   6.129181   7.331588   5.547059   5.867118
## ENSG00000123338   6.699790   6.935390   7.050288   6.536710   6.200269

2. Unsupervised clustering

res_pad <- PAD(
  expr = expr,
  cluster.method = "ward.D2",
  extra.annot = NULL,
  plot.title = 'PAD test',
  subtype = "PAD.train_20200110",
  verbose = T
)

## Use default PIAM... 
## Use default PIDG... 
## Gene match: 100%.

## Done!

3. Of note

It's strongly recommanded that the gene type of expr should be alway the same, such as ENSEMBL genes (ENSG00000111640 for GAPDH, for examples).
PAD function is only for datasets with lots of samples for its classification depends on population-based unsupervised clusting. PAD is population-dependent and non-personalized.
Beta characteristics: You could try random forest classification based on the randomForest package or methods in stats::hclust.

PADi

In GSClassifier, PADi is a pre-trained out-of-the-box model for GC personalized PAD subtypes calling.
During the sutype calling, the gene rank relations based on individuals instead of the relative values across samples would be used. Thus, you don't have to do batch normalization even though the data (the X input) come from multiple cohorts or platform.
More limitations were discussed in our paper that you had better know.

In this section, we would showed how to use PADi series: PADi, callEnsemble, and parCallEnsemble functions.

1. Preparation of the test data

 X <- testData$Kim2018_3
 head(X)

##                   PB-16-002   PB-16-003   PB-16-004
## ENSG00000121410  0.07075272 -2.08976724 -1.43569557
## ENSG00000148584 -1.49631022 -0.23917056  0.94827471
## ENSG00000175899 -0.77315329  0.52163146  0.91264015
## ENSG00000166535 -0.28860715 -0.45964255 -0.38401295
## ENSG00000256069 -0.25034243  0.06863867  0.14429081
## ENSG00000184389  0.08215945 -0.05966481  0.04937924

2. Use a specific function called `PADi`

Very simple, just:

res_padi <- PADi(X = X, verbose = F)

##  Use PAD.train_20200110 classifier...

##  Percent missing genes: 0%.

##  All done!

Check the result:

head(res_padi)

##   SampleIDs BestCall BestCall_Max           1          2          3         4
## 1 PB-16-002        4            4 0.023372779 0.02631794 0.04864784 0.3336484
## 2 PB-16-003        4            4 0.007271698 0.08650742 0.01974812 0.9530730
## 3 PB-16-004        4            4 0.011559768 0.02922151 0.09018894 0.8649045

Actually, PADi is exactly based on a general function called callEnsemble.

3. Use the `callEnsemble` function

Also simple, just:

res_padi <- callEnsemble(
    X = X,
    ens = NULL,
    geneAnnotation = NULL,
    geneSet = NULL,
    scaller = NULL,
    geneid = "ensembl",
    subtype = "PAD.train_20200110",
    verbose = F
  )

##  Use PAD.train_20200110 classifier...

##  Percent missing genes: 0%.

##  All done!

Check the result:

head(res_padi)

##   SampleIDs BestCall BestCall_Max           1          2          3         4
## 1 PB-16-002        4            4 0.023372779 0.02631794 0.04864784 0.3336484
## 2 PB-16-003        4            4 0.007271698 0.08650742 0.01974812 0.9530730
## 3 PB-16-004        4            4 0.011559768 0.02922151 0.09018894 0.8649045

4. Parallel strategy for PADi

Sometimes, the number of patients for subtype callings could be huge (hundreds or even tens of thousands). Thus, the parallel computing (Windows or Linux pass; not tested in Mac or other OS) was also developed in the current version of GSClassifier package.
The parameter numCores was used to control the No. of CPU for computing (which depends on your CPU capacity).

# No run for the tiny test data. With errors.

# Method 1:
res_padi <- PADi(X = X, verbose = F, numCores = 4)

# Method 2: 
res_padi <- parCallEnsemble(
  X = X,
  ens = NULL,
  geneAnnotation = NULL,
  geneSet = NULL,
  scaller = NULL,
  geneids = 'ensembl',
  subtype = 'PAD.train_20200110',
  verbose = T,
  numCores = 4)

5. Single sample subtype calling

In clinical practice, the single sample subtype calling might be one of the most common scenarios and is also supported by functions of PADi series.

Supposed that there is a GC patient, its information should be:

X_ind <- X[,1]; names(X_ind) <- rownames(X)
head(X_ind)

## ENSG00000121410 ENSG00000148584 ENSG00000175899 ENSG00000166535 ENSG00000256069 
##      0.07075272     -1.49631022     -0.77315329     -0.28860715     -0.25034243 
## ENSG00000184389 
##      0.08215945

Or it can also be another format:

X_ind <- as.matrix(X[,1]); rownames(X_ind) <- rownames(X)
head(X_ind)

##                        [,1]
## ENSG00000121410  0.07075272
## ENSG00000148584 -1.49631022
## ENSG00000175899 -0.77315329
## ENSG00000166535 -0.28860715
## ENSG00000256069 -0.25034243
## ENSG00000184389  0.08215945

Similar to multiples sample calling, just:

res_padi <- PADi(X = X_ind, verbose = F)

##  Use PAD.train_20200110 classifier...

##  Percent missing genes: 0%.

##  All done!

check the result:

head(res_padi)

##   SampleIDs BestCall BestCall_Max          1          2          3         4
## 1    target        4            4 0.02337278 0.02631794 0.04864784 0.3336484

Similarly, there is alternative choice:

res_padi <- callEnsemble(
    X = X_ind,
    ens = NULL,
    geneAnnotation = NULL,
    geneSet = NULL,
    scaller = NULL,
    geneid = "ensembl",
    subtype = "PAD.train_20200110",
    verbose = F
  )

##  Use PAD.train_20200110 classifier...

##  Percent missing genes: 0%.

##  All done!

Check the result:

head(res_padi)

##   SampleIDs BestCall BestCall_Max          1          2          3         4
## 1    target        4            4 0.02337278 0.02631794 0.04864784 0.3336484

6. Of note

In the results of PADi, two types of subtypes (BestCall and BestCall_Max) were integrated. BestCall was predicted based on a xgboost-trained model based on prior knowlege of PAD subtypes and the possibility matrix (columns 4 to 7 of four-subtype calling, for example), while BestCall_Max was predicted via maximum strategy. You should use THE SAME ONE in a specific practice no matter which one you use.
PADi is individual-dependent and personalized, which means that the result of subtype calling would not be influenced by the data of others.

Use external models from luckyModel package

In the future, there might be lots of models available as a resource of GSClassifier, such as luckyModel. Here we show how luckyModel support GSClassifier.

First, intall and load luckyModel:

# Install luckyModel
if (!requireNamespace("luckyModel", quietly = TRUE))
    devtools::install_github("huangwb8/luckyModel")

## Downloading GitHub repo huangwb8/luckyModel@HEAD

##   
  
  
   checking for file 'C:\Users\Administrator\AppData\Local\Temp\RtmpSir9Ly\remotes4508510f14f1\huangwb8-luckyModel-c39c2de/DESCRIPTION' ...
  
√  checking for file 'C:\Users\Administrator\AppData\Local\Temp\RtmpSir9Ly\remotes4508510f14f1\huangwb8-luckyModel-c39c2de/DESCRIPTION'
## 
  
  
  
-  preparing 'luckyModel':
##    checking DESCRIPTION meta-information ...
  
   checking DESCRIPTION meta-information ... 
  
√  checking DESCRIPTION meta-information
## 
  
  
  
-  checking for LF line-endings in source and make files and shell scripts
## 
  
  
  
-  checking for empty or unneeded directories
## 
  
   NB: this package now depends on R (>= 
  
     NB: this package now depends on R (>= 3.5.0)
## 
  
     WARNING: Added dependency on R >= 3.5.0 because serialized objects in  serialize/load version 3 cannot be read in older versions of R.  File(s) containing such objects:  'luckyModel/inst/extdata/GSClassifier/Gibbs_PanCancerImmuneSubtype_v20190731.rds'  WARNING: Added dependency on R >= 3.5.0 because serialized objects in  serialize/load version 3 cannot be read in older versions of R.  File(s) containing such objects:  'luckyModel/inst/extdata/GSClassifier/HWB_PAD_v20200110.rds'
## 
  
-  building 'luckyModel_0.1.0.tar.gz'
## 
  
   
##

library(luckyModel)

Check projects supported in current luckyModel:

list_project()

## [1] "GSClassifier"

Check available models in the project:

list_model(project='GSClassifier')

## Available models in GSClassifier:

##   *Gibbs_PanCancerImmuneSubtype_v20190731
##   *HWB_PAD_v20200110

Here, HWB_PAD_v20200110 is a standard name of PADi. They are exactly the same.

Taking PADi as an example, we here show how to use an external model from luckyModel. First, load a model:

model <- lucky_model(project = 'GSClassifier',
                     developer='HWB',
                     model = 'PAD',
                     version = 'v20200110')

Then, check the gene id type:

model$geneSet

## $PIAM
##  [1] "ENSG00000122122" "ENSG00000117091" "ENSG00000163219" "ENSG00000136167"
##  [5] "ENSG00000005844" "ENSG00000123338" "ENSG00000102879" "ENSG00000010671"
##  [9] "ENSG00000185862" "ENSG00000104814" "ENSG00000134516" "ENSG00000100055"
## [13] "ENSG00000082074" "ENSG00000113263" "ENSG00000153283" "ENSG00000198821"
## [17] "ENSG00000185811" "ENSG00000117090" "ENSG00000171608"
## 
## $PIDG
##  [1] "ENSG00000116667" "ENSG00000107771" "ENSG00000196782" "ENSG00000271447"
##  [5] "ENSG00000173517" "ENSG00000134686" "ENSG00000100614" "ENSG00000134247"
##  [9] "ENSG00000109686" "ENSG00000197321" "ENSG00000179981" "ENSG00000187189"
## [13] "ENSG00000140836"

The model should use ensembl as the value of geneid parameter in callEnsemble series.

Next, you can use the model like:

res_padi <- callEnsemble(
  X = X,
  ens = model$ens$Model,
  geneAnnotation = model$geneAnnotation,
  geneSet = model$geneSet,
  scaller = model$scaller$Model,
  geneid = "ensembl",
  subtype = NULL,
  verbose = F
)

##  Use self-defined classifier...

##  Percent missing genes: 0%.

##  All done!

Or just:

res_padi <- callEnsemble(
  X,
  ens = NULL,
  geneAnnotation = NULL,
  geneSet = NULL,
  scaller = NULL,
  geneid = "ensembl",
  subtype = model, 
  verbose = F
)

##  Use self-defined classifier...

##  Percent missing genes: 0%.

##  All done!

They are exactly the same.

Finally, check the result:

head(res_padi)

##   SampleIDs BestCall BestCall_Max           1          2          3         4
## 1 PB-16-002        4            4 0.023372779 0.02631794 0.04864784 0.3336484
## 2 PB-16-003        4            4 0.007271698 0.08650742 0.01974812 0.9530730
## 3 PB-16-004        4            4 0.011559768 0.02922151 0.09018894 0.8649045

PanCancer immune subtypes

GSClassifier could also call the PanCancer immune subtypes of Gibbs'.

First, see data available in current GSClassifier:

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"))

Let's use our test data to do this:

X <- testData$Kim2018_3
symbol <- convert(rownames(X))
rownames(X) <- symbol
X <- X[!is.na(symbol),]
dim(X)

## [1] 19118     3

Have a check

head(X)

##                 PB-16-002   PB-16-003   PB-16-004
## A1BG           0.07075272 -2.08976724 -1.43569557
## A1CF          -1.49631022 -0.23917056  0.94827471
## A2M           -0.77315329  0.52163146  0.91264015
## A2ML1         -0.28860715 -0.45964255 -0.38401295
## RP11-118B22.6 -0.25034243  0.06863867  0.14429081
## A3GALT2        0.08215945 -0.05966481  0.04937924

PanCan Immune Subtype callings:

res_pis <- callEnsemble(
    X = X,
    ens = NULL,
    geneAnnotation = NULL,
    geneSet = NULL,
    scaller = NULL,
    geneid = "symbol",
    subtype = "ImmuneSubtype",
    verbose = F
  )

Check the result:

head(res_pis)

##   SampleIDs BestCall BestCall_Max            1           2            3
## 1 PB-16-002        2            2 8.836530e-04 0.561300665 5.981909e-06
## 2 PB-16-003        4            4 5.216562e-07 0.018167170 2.148311e-04
## 3 PB-16-004        3            3 3.521230e-06 0.001126488 2.682360e-01
##              4           5           6
## 1 0.1635556966 0.011658502 0.002983824
## 2 0.3935615569 0.002543283 0.002526916
## 3 0.0001166083 0.006606377 0.007045272

Also, you can try to use luckyModel:

pci <- lucky_model(
  project = "GSClassifier",
  model = "PanCancerImmuneSubtype",
  developer = "Gibbs",
  version = "v20190731"
)

PanCan Immune Subtype callings:

res_pis <- callEnsemble(
    X = X,
    ens = NULL,
    geneAnnotation = NULL,
    geneSet = NULL,
    scaller = NULL,
    geneid = "symbol",
    subtype = pci,
    verbose = F
  )

Finally, we take a look at the PanCancer immune subtypes model:

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

## [1] "ens"            "scaller"        "geneAnnotation" "geneSet"

Its gene annotation:

head(ImmuneSubtype$geneAnnotation)

##      SYMBOL ENTREZID         ENSEMBL
## 235  ACTL6A       86 ENSG00000136518
## 294   ADAM9     8754 ENSG00000168615
## 305 ADAMTS1     9510 ENSG00000154734
## 322    ADAR      103 ENSG00000160710
## 340   ADCY7      113 ENSG00000121281
## 479   AIMP2     7965 ENSG00000106305

Its gene sets:

ImmuneSubtype$geneSet

## $LIexpression_score
##  [1] "CCL5"  "CD19"  "CD37"  "CD3D"  "CD3E"  "CD3G"  "CD3Z"  "CD79A" "CD79B"
## [10] "CD8A"  "CD8B1" "IGHG3" "IGJ"   "IGLC1" "CD14"  "LCK"   "LTB"   "MS4A1"
## 
## $CSF1_response
##   [1] "CORO1A"   "MNDA"     "CCRL2"    "SLC7A7"   "HLA-DMA"  "FYB"     
##   [7] "RNASE6"   "TLR2"     "CTSC"     "LILRB4"   "PSCDBP"   "CTSS"    
##  [13] "RASSF4"   "MSN"      "CYBB"     "LAPTM5"   "DOCK2"    "FCGR1A"  
##  [19] "EVI2B"    "ADCY7"    "CD48"     "ARHGAP15" "ARRB2"    "SYK"     
##  [25] "BTK"      "TNFAIP3"  "FCGR2A"   "VSIG4"    "FPRL2"    "IL10RA"  
##  [31] "IFI16"    "ITGB2"    "IL7R"     "TBXAS1"   "FMNL1"    "FLI1"    
##  [37] "RASSF2"   "LYZ"      "CD163"    "CD97"     "CCL2"     "FCGR2B"  
##  [43] "MERTK"    "CD84"     "CD53"     "CD86"     "HMHA1"    "CTSL"    
##  [49] "EVI2A"    "TNFRSF1B" "CXCR4"    "LCP1"     "SAMHD1"   "CPVL"    
##  [55] "HLA-DRB1" "C13orf18" "GIMAP4"   "SAMSN1"   "PLCG2"    "OSBPL3"  
##  [61] "CD8A"     "RUNX3"    "FCGR3A"   "AMPD3"    "MYO1F"    "CECR1"   
##  [67] "LYN"      "MPP1"     "LRMP"     "FGL2"     "NCKAP1L"  "HCLS1"   
##  [73] "SELL"     "CASP1"    "SELPLG"   "CD33"     "GPNMB"    "NCF2"    
##  [79] "FNBP1"    "IL18"     "B2M"      "SP140"    "FCER1G"   "LCP2"    
##  [85] "LY86"     "LAIR1"    "IFI30"    "TNFSF13B" "LST1"     "FGR"     
##  [91] "NPL"      "PLEK"     "CCL5"     "PTPRC"    "GNPTAB"   "SLC1A3"  
##  [97] "HCK"      "NPC2"     "C3AR1"    "PIK3CG"   "DAPK1"    "ALOX5AP" 
## [103] "CSF1R"    "CUGBP2"   "APOE"     "APOC1"    "CD52"     "LHFPL2"  
## [109] "C1orf54"  "IKZF1"    "SH2B3"    "WIPF1"   
## 
## $Module3_IFN_score
##  [1] "IFI44"  "IFI44L" "DDX58"  "IFI6"   "IFI27"  "IFIT2"  "IFIT1"  "IFIT3" 
##  [9] "CXCL10" "MX1"    "OAS1"   "OAS2"   "OAS3"   "HERC5"  "SAMD9"  "HERC6" 
## [17] "DDX60"  "RTP4"   "IFIH1"  "STAT1"  "TAP1"   "OASL"   "RSAD2"  "ISG15" 
## 
## $TGFB_score_21050467
##  [1] "MMP3"     "MARCKSL1" "IGF2R"    "LAMB1"    "SPARC"    "FN1"     
##  [7] "ITGA4"    "SMO"      "MMP19"    "ITGB8"    "ITGA5"    "NID1"    
## [13] "TIMP1"    "SEMA3F"   "RHOQ"     "CTNNB1"   "MMP2"     "SERPINE1"
## [19] "EPHB2"    "COL16A1"  "EPHA2"    "TNC"      "JUP"      "ITGA3"   
## [25] "TCF7L2"   "COL3A1"   "CDH6"     "WNT2B"    "ADAM9"    "DSP"     
## [31] "HSPG2"    "ARHGAP1"  "ITGB5"    "IGFBP5"   "ARHGDIA"  "LRP1"    
## [37] "IGFBP2"   "CTNNA1"   "LRRC17"   "MMP14"    "NEO1"     "EFNA5"   
## [43] "ITGB3"    "EPHB3"    "CD44"     "IGFBP4"   "TNFRSF1A" "RAC1"    
## [49] "PXN"      "PLAT"     "COL8A1"   "WNT8B"    "IGFBP3"   "RHOA"    
## [55] "EPHB4"    "MMP1"     "PAK1"     "MTA1"     "THBS2"    "CSPG2"   
## [61] "MMP17"    "CD59"     "DVL3"     "RHOB"     "COL6A3"   "NOTCH2"  
## [67] "BSG"      "MMP11"    "COL1A2"   "ZYX"      "RND3"     "THBS1"   
## [73] "RHOG"     "ICAM1"    "LAMA4"    "DVL1"     "PAK2"     "ITGB2"   
## [79] "COL6A1"   "FGD1"    
## 
## $CHANG_CORE_SERUM_RESPONSE_UP
##   [1] "CEP78"     "LSM3"      "LRRC40"    "STK17A"    "RPN1"      "JUNB"     
##   [7] "NUP85"     "FLNC"      "HMGN2"     "RPP40"     "UQCR10"    "AIMP2"    
##  [13] "CHEK1"     "VTA1"      "EXOSC8"    "CENPO"     "PNO1"      "SLC16A1"  
##  [19] "WDR77"     "UBE2J1"    "NOP16"     "NUDT1"     "SMC2"      "SLC25A5"  
##  [25] "NUPL1"     "DLEU2"     "PDAP1"     "CCBL2"     "COX17"     "BCCIP"    
##  [31] "PLG"       "RGS8"      "SNRPC"     "PLK4"      "NUTF2"     "LSM4"     
##  [37] "SMS"       "EBNA1BP2"  "C13orf27"  "VDAC1"     "PSMD14"    "MYCBP"    
##  [43] "SMURF2"    "GNG11"     "F3"        "IL7R"      "BRIP1"     "HNRNPA2B1"
##  [49] "DCK"       "ALKBH7"    "HN1L"      "MSN"       "TPM1"      "HYLS1"    
##  [55] "HAUS1"     "NUP93"     "SNRPE"     "ITGA6"     "CENPN"     "C11orf24" 
##  [61] "GGH"       "PFKP"      "FARSA"     "EIF2AK1"   "CENPW"     "TUBA4A"   
##  [67] "TRA2B"     "UMPS"      "MRTO4"     "NUDT15"    "PGM2"      "DBNDD1"   
##  [73] "SNRPB"     "MNAT1"     "NUP35"     "TCEB1"     "HSPB11"    "C19orf48" 
##  [79] "ID3"       "IPO4"      "FARSB"     "EIF4G1"    "SKA1"      "MFSD11"   
##  [85] "PLAUR"     "MARVELD2"  "MCM3"      "DHFR"      "RNF41"     "ID2"      
##  [91] "H2AFZ"     "CDK2"      "NCLN"      "ZWILCH"    "DYNLT1"    "C16orf61" 
##  [97] "SLC25A40"  "RHOC"      "CCT5"      "PDIA4"     "SNRPA"     "RBM14"    
## [103] "PDLIM7"    "PITPNC1"   "TPM3"      "CORO1C"    "ERLIN1"    "PAICS"    
## [109] "TPRKB"     "SKA2"      "MYBL1"     "SH3BP5L"   "BRCA2"     "SAR1A"    
## [115] "POLR3K"    "MRPS28"    "NUP107"    "TUBG1"     "PNN"       "FAM167A"  
## [121] "RFC3"      "MYL6"      "MCM7"      "MAGOHB"    "FAM89B"    "TOMM40"   
## [127] "CDCA4"     "MT3"       "MTHFD1"    "PSMD12"    "MYBL2"     "CKLF"     
## [133] "NRIP3"     "EZR"       "C12orf24"  "GPLD1"     "SRM"       "RAB3B"    
## [139] "NLN"       "MT1F"      "TNFRSF12A" "TPI1"      "HAS2"      "APOO"     
## [145] "FBXO41"    "MRPL37"    "GSTCD"     "SDC1"      "WDR54"     "RNF138"   
## [151] "APITD1"    "RMND5B"    "ENO1"      "MAP3K8"    "TMEM130"   "SNX17"    
## [157] "KRR1"      "TAGLN"     "PA2G4"     "RUVBL1"    "SNRPD1"    "LOXL2"    
## [163] "POLE2"     "MAPRE1"    "IMP4"      "EMP2"      "PSMD2"     "MET"      
## [169] "IFRD2"     "LMNB2"     "PLOD2"     "NCEH1"     "NME1"      "STRA13"   
## [175] "ACTL6A"    "DLEU1"     "SNRPA1"    "CBX1"      "LYAR"      "PTPLB"    
## [181] "PFN1"      "CENPJ"     "COTL1"     "SPRYD7"    "USPL1"     "MRPL12"   
## [187] "ADAMTS1"   "GLRX3"     "WSB2"      "MRPS16"    "DCLRE1B"   "MKKS"     
## [193] "C3orf26"   "CPEB4"     "SPAG17"    "MLF1IP"    "UAP1"      "COQ2"     
## [199] "WDHD1"     "DCBLD2"    "KIAA0090"  "SAR1B"     "PSMA7"     "PSMC3"    
## [205] "COPS6"     "DUT"       "PPIH"      "PHF19"     "TPM2"      "MCTS1"    
## [211] "EIF4EBP1"  "HNRNPR"

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Quick start

About

Data

PAD

1. Preparation of the test data

2. Unsupervised clustering

3. Of note

PADi

1. Preparation of the test data

2. Use a specific function called `PADi`

3. Use the `callEnsemble` function

4. Parallel strategy for PADi

5. Single sample subtype calling

6. Of note

Use external models from luckyModel package

PanCancer immune subtypes

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Introduction

Installation

Quick start

Model establishment

Advanced development

Plans in the future

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Change log

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Quick start

About

Data

PAD

1. Preparation of the test data

2. Unsupervised clustering

3. Of note

PADi

1. Preparation of the test data

2. Use a specific function called PADi

3. Use the callEnsemble function

4. Parallel strategy for PADi

5. Single sample subtype calling

6. Of note

Use external models from luckyModel package

PanCancer immune subtypes

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Clone this wiki locally

2. Use a specific function called `PADi`

3. Use the `callEnsemble` function