MCL and SLL lymphoma subtypes

Description

A total of 237 features are identified for 22 lymphoma patients.

Usage

data(mcl_sll)

Format

A matrix. Each of the 237 columns represents a features except the first column which contains the label vector. Each of the 22 rows represents a patients.

Details

7 cases diagnosed with Mantel Cell Lymphoma (MCL) and 15 cases with Small Lymphocytic Lymphoma (SLL). The presented features are computed based on flow cytometry data The fist column contains the label vector which has value 1 for MCL cases and 0 for SLL cases.

Source

British Columbia Cancer Agency

References

"Statistical Analysis of Overfitting Features", manuscript in preparation.

See Also

FeaLect, train.doctor, doctor.validate, random.subset, compute.balanced,compute.logistic.score, ignore.redundant, input.check.FeaLect

Examples

library(FeaLect)
data(mcl_sll)
F <- as.matrix(mcl_sll[ ,-1])	# The Feature matrix
L <- as.numeric(mcl_sll[ ,1])	# The labels
names(L) <- rownames(F)
message(dim(F)[1], " samples and ",dim(F)[2], " features.")
L

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(FeaLect)
Loading required package: lars
Loaded lars 1.2

Loading required package: rms
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

Loading required package: SparseM

Attaching package: 'SparseM'

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

    backsolve

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FeaLect/mcl_sll.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mcl_sll
> ### Title: MCL and SLL lymphoma subtypes
> ### Aliases: mcl_sll
> ### Keywords: datasets
> 
> ### ** Examples
> 
> library(FeaLect)
> data(mcl_sll)
> F <- as.matrix(mcl_sll[ ,-1])	# The Feature matrix
> L <- as.numeric(mcl_sll[ ,1])	# The labels
> names(L) <- rownames(F)
> message(dim(F)[1], " samples and ",dim(F)[2], " features.")
22 samples and 236 features.
> L
PAT10105 PAT10474 PAT10658  PAT7399 PAT20762 PAT14569 PAT20839 PAT10301 
       1        1        1        1        1        1        1        0 
PAT10384 PAT10863  PAT7591  PAT7593  PAT8651  PAT8065  PAT8273  PAT8355 
       0        0        0        0        0        0        0        0 
 PAT8725  PAT8019 PAT14706  PAT8334  PAT7883  PAT8893 
       0        0        0        0        0        0 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>