Khan: Four tumors microarray data
In AliciaNieto/fdaRNA: RNA-Seq and Microarray Preprocessing
Description
Usage
Format
Details
Source
References
Examples
Description
The Khan dataset consists of gene expression measurements, obtained using cDNA microarrays, of four types of pediatric small round blue cell tumors:
the Ewing family of tumors (EW, 23 microarrays), Burkitt lymphomas (BL, 8 microarrays), neuroblastoma (NB, 12 microarrays) and rhabdomyosarcoma (RM, 20 microarrays). There is a total of 63 microarrays and an additional set of 25 microarys for testing purposes. It has 6,567 genes that were filtered to 2,308 genes in Khan et al. (2001). Here it is provided the filtered sample.
Usage
1
data("Khan")
Format
A data frame with 2308 observations on the following 88 variables.
TRAIN1.EWa numeric vector
TRAIN2.EWa numeric vector
TRAIN3.EWa numeric vector
TRAIN4.EWa numeric vector
TRAIN5.EWa numeric vector
TRAIN6.EWa numeric vector
TRAIN7.EWa numeric vector
TRAIN8.EWa numeric vector
TRAIN9.EWa numeric vector
TRAIN10.EWa numeric vector
TRAIN11.EWa numeric vector
TRAIN12.EWa numeric vector
TRAIN13.EWa numeric vector
TRAIN14.EWa numeric vector
TRAIN15.EWa numeric vector
TRAIN16.EWa numeric vector
TRAIN17.EWa numeric vector
TRAIN18.EWa numeric vector
TRAIN19.EWa numeric vector
TRAIN20.EWa numeric vector
TRAIN21.EWa numeric vector
TRAIN22.EWa numeric vector
TRAIN23.EWa numeric vector
TRAIN24.BLa numeric vector
TRAIN25.BLa numeric vector
TRAIN26.BLa numeric vector
TRAIN27.BLa numeric vector
TRAIN28.BLa numeric vector
TRAIN29.BLa numeric vector
TRAIN30.BLa numeric vector
TRAIN31.BLa numeric vector
TRAIN32.NBa numeric vector
TRAIN33.NBa numeric vector
TRAIN34.NBa numeric vector
TRAIN35.NBa numeric vector
TRAIN36.NBa numeric vector
TRAIN37.NBa numeric vector
TRAIN38.NBa numeric vector
TRAIN39.NBa numeric vector
TRAIN40.NBa numeric vector
TRAIN41.NBa numeric vector
TRAIN42.NBa numeric vector
TRAIN43.NBa numeric vector
TRAIN44.RMa numeric vector
TRAIN45.RMa numeric vector
TRAIN46.RMa numeric vector
TRAIN47.RMa numeric vector
TRAIN48.RMa numeric vector
TRAIN49.RMa numeric vector
TRAIN50.RMa numeric vector
TRAIN51.RMa numeric vector
TRAIN52.RMa numeric vector
TRAIN53.RMa numeric vector
TRAIN54.RMa numeric vector
TRAIN55.RMa numeric vector
TRAIN56.RMa numeric vector
TRAIN57.RMa numeric vector
TRAIN58.RMa numeric vector
TRAIN59.RMa numeric vector
TRAIN60.RMa numeric vector
TRAIN61.RMa numeric vector
TRAIN62.RMa numeric vector
TRAIN63.RMa numeric vector
TEST64.NBa numeric vector
TEST65.EWa numeric vector
TEST66.NAa numeric vector
TEST67.RMa numeric vector
TEST68.NAa numeric vector
TEST69.EWa numeric vector
TEST70.BLa numeric vector
TEST71.NBa numeric vector
TEST72.NAa numeric vector
TEST73.RMa numeric vector
TEST74.NAa numeric vector
TEST75.EWa numeric vector
TEST76.NAa numeric vector
TEST77.NBa numeric vector
TEST78.BLa numeric vector
TEST79.NBa numeric vector
TEST80.RMa numeric vector
TEST81.BLa numeric vector
TEST82.EWa numeric vector
TEST83.EWa numeric vector
TEST84.EWa numeric vector
TEST85.RMa numeric vector
TEST86.NBa numeric vector
TEST87.RMa numeric vector
TEST88.NBa numeric vector
Details
Variables:
Col. 1-23: TRAIN1.EW through TRAIN23.EW. Training set with tumor type EW.
Col. 24-31: TRAIN24.BL through TRAIN31.BL. Training set with tumor type EW.
Col. 32-43: TRAIN32.NB through TRAIN43.NB. . Training set with tumor type EW.
Col. 34-63: TRAIN44.RM through TRAIN63.RM. Training set with tumor type EW.
Col. 64-88: EST64.NB through TEST88.NB. Testing set.
Source
Own
References
Amaratunga D, Cabrera J, Shkedy Z. Exploration and analysis of DNA microarray and other high dimensional data. J. Wiley & Sons, 2014.
Khan J, Wei J, Ringner M, Saal L, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu C, Peterson C, and Meltzer P. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, v.7, pp.673-679, 2001.
Nieto-Reyes A, Cabrera J. Statistical depth based normalization and outlier detection of gene expression data. Preprint.
Examples
1
2
3
K=Khan[,1:63]
names(K)
outlier.detection(K,MS = TRUE)
AliciaNieto/fdaRNA documentation built on May 29, 2020, 11:58 a.m.
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Description Usage Format Details Source References Examples
Description
The Khan dataset consists of gene expression measurements, obtained using cDNA microarrays, of four types of pediatric small round blue cell tumors: the Ewing family of tumors (EW, 23 microarrays), Burkitt lymphomas (BL, 8 microarrays), neuroblastoma (NB, 12 microarrays) and rhabdomyosarcoma (RM, 20 microarrays). There is a total of 63 microarrays and an additional set of 25 microarys for testing purposes. It has 6,567 genes that were filtered to 2,308 genes in Khan et al. (2001). Here it is provided the filtered sample.
Usage
1 | data("Khan")
|
Format
A data frame with 2308 observations on the following 88 variables.
TRAIN1.EWa numeric vector
TRAIN2.EWa numeric vector
TRAIN3.EWa numeric vector
TRAIN4.EWa numeric vector
TRAIN5.EWa numeric vector
TRAIN6.EWa numeric vector
TRAIN7.EWa numeric vector
TRAIN8.EWa numeric vector
TRAIN9.EWa numeric vector
TRAIN10.EWa numeric vector
TRAIN11.EWa numeric vector
TRAIN12.EWa numeric vector
TRAIN13.EWa numeric vector
TRAIN14.EWa numeric vector
TRAIN15.EWa numeric vector
TRAIN16.EWa numeric vector
TRAIN17.EWa numeric vector
TRAIN18.EWa numeric vector
TRAIN19.EWa numeric vector
TRAIN20.EWa numeric vector
TRAIN21.EWa numeric vector
TRAIN22.EWa numeric vector
TRAIN23.EWa numeric vector
TRAIN24.BLa numeric vector
TRAIN25.BLa numeric vector
TRAIN26.BLa numeric vector
TRAIN27.BLa numeric vector
TRAIN28.BLa numeric vector
TRAIN29.BLa numeric vector
TRAIN30.BLa numeric vector
TRAIN31.BLa numeric vector
TRAIN32.NBa numeric vector
TRAIN33.NBa numeric vector
TRAIN34.NBa numeric vector
TRAIN35.NBa numeric vector
TRAIN36.NBa numeric vector
TRAIN37.NBa numeric vector
TRAIN38.NBa numeric vector
TRAIN39.NBa numeric vector
TRAIN40.NBa numeric vector
TRAIN41.NBa numeric vector
TRAIN42.NBa numeric vector
TRAIN43.NBa numeric vector
TRAIN44.RMa numeric vector
TRAIN45.RMa numeric vector
TRAIN46.RMa numeric vector
TRAIN47.RMa numeric vector
TRAIN48.RMa numeric vector
TRAIN49.RMa numeric vector
TRAIN50.RMa numeric vector
TRAIN51.RMa numeric vector
TRAIN52.RMa numeric vector
TRAIN53.RMa numeric vector
TRAIN54.RMa numeric vector
TRAIN55.RMa numeric vector
TRAIN56.RMa numeric vector
TRAIN57.RMa numeric vector
TRAIN58.RMa numeric vector
TRAIN59.RMa numeric vector
TRAIN60.RMa numeric vector
TRAIN61.RMa numeric vector
TRAIN62.RMa numeric vector
TRAIN63.RMa numeric vector
TEST64.NBa numeric vector
TEST65.EWa numeric vector
TEST66.NAa numeric vector
TEST67.RMa numeric vector
TEST68.NAa numeric vector
TEST69.EWa numeric vector
TEST70.BLa numeric vector
TEST71.NBa numeric vector
TEST72.NAa numeric vector
TEST73.RMa numeric vector
TEST74.NAa numeric vector
TEST75.EWa numeric vector
TEST76.NAa numeric vector
TEST77.NBa numeric vector
TEST78.BLa numeric vector
TEST79.NBa numeric vector
TEST80.RMa numeric vector
TEST81.BLa numeric vector
TEST82.EWa numeric vector
TEST83.EWa numeric vector
TEST84.EWa numeric vector
TEST85.RMa numeric vector
TEST86.NBa numeric vector
TEST87.RMa numeric vector
TEST88.NBa numeric vector
Details
Variables:
Col. 1-23: TRAIN1.EW through TRAIN23.EW. Training set with tumor type EW.
Col. 24-31: TRAIN24.BL through TRAIN31.BL. Training set with tumor type EW.
Col. 32-43: TRAIN32.NB through TRAIN43.NB. . Training set with tumor type EW.
Col. 34-63: TRAIN44.RM through TRAIN63.RM. Training set with tumor type EW.
Col. 64-88: EST64.NB through TEST88.NB. Testing set.
Source
Own
References
Amaratunga D, Cabrera J, Shkedy Z. Exploration and analysis of DNA microarray and other high dimensional data. J. Wiley & Sons, 2014.
Khan J, Wei J, Ringner M, Saal L, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu C, Peterson C, and Meltzer P. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, v.7, pp.673-679, 2001.
Nieto-Reyes A, Cabrera J. Statistical depth based normalization and outlier detection of gene expression data. Preprint.
Examples
1 2 3 | K=Khan[,1:63]
names(K)
outlier.detection(K,MS = TRUE)
|
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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