Recent changes to Home

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Tue, 21 Jul 2020 06:17:26 -0000

--- v13
+++ v14
@@ -28,8 +28,6 @@

 > install.packages("miniABS_0.1.1.tar.gz", repos = NULL, type = "source")
 library(miniABS)
->>    Symbol
- MYBL2  SFRP1  CEP55  ESR1  FOXA1  MKI67  MLPH  PGR  ERBB2  FGFR4  KRT17

 ----------
 How to use
@@ -38,6 +36,7 @@

 The Input should be a log2 transformed expression matrix of 11 genes.
 >data(marker) # list of 11 genes used in miniABS
+>> MYBL2  SFRP1  CEP55  ESR1  FOXA1  MKI67  MLPH  PGR  ERBB2  FGFR4  KRT17

 >data(exprRNAseq) # example RNA-seq expression data (eg. log2(FPKM+1))

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Tue, 21 Jul 2020 06:16:56 -0000

--- v12
+++ v13
@@ -6,7 +6,7 @@

 ----------
-outstanding features of miniABS
+Outstanding features of miniABS
 ------------------------------------------

 * miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression inference (which can ensure reproducibility of subtype assignments).
@@ -28,7 +28,8 @@

 > install.packages("miniABS_0.1.1.tar.gz", repos = NULL, type = "source")
 library(miniABS)
-
+>>    Symbol
+ MYBL2  SFRP1  CEP55  ESR1  FOXA1  MKI67  MLPH  PGR  ERBB2  FGFR4  KRT17

 ----------
 How to use

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Tue, 21 Jul 2020 06:15:09 -0000

--- v11
+++ v12
@@ -26,7 +26,7 @@

 Download the source and install it as shown below.

-> install.packages("miniABS_0.1.0.tar.gz", repos = NULL, type = "source")
+> install.packages("miniABS_0.1.1.tar.gz", repos = NULL, type = "source")
 library(miniABS)

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Sat, 18 Aug 2018 08:31:05 -0000

--- v10
+++ v11
@@ -9,7 +9,7 @@
 outstanding features of miniABS
 ------------------------------------------

-* miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression deduction (which can ensure reproducibility of subtype assignments).
+* miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression inference (which can ensure reproducibility of subtype assignments).

 * miniABS has high accuracy without bias for various gene expression quantification technologies.

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Fri, 17 Aug 2018 07:01:39 -0000

--- v9
+++ v10
@@ -9,15 +9,15 @@
 outstanding features of miniABS
 ------------------------------------------

-miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression deduction (which can ensure reproducibility of subtype assignments).
+* miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression deduction (which can ensure reproducibility of subtype assignments).

-miniABS has high accuracy without bias for various gene expression quantification technologies.
+* miniABS has high accuracy without bias for various gene expression quantification technologies.

-miniABS predicts subtypes with only 11 biologically meaningful genes in the breast cancer subtype.
+* miniABS predicts subtypes with only 11 biologically meaningful genes in the breast cancer subtype.

-The miniABS uses pairwise gene expression ratios (PGER), which is statistically significantly different between subtypes than the expression of a single gene, allowing subtypes to be well classified.
+* The miniABS uses pairwise gene expression ratios (PGER), which is statistically significantly different between subtypes than the expression of a single gene, allowing subtypes to be well classified.

-The miniABS does not lose information when subtyping is classified using actual ratio values rather than the magnitude of the expression of the two genes.
+* The miniABS does not lose information when subtyping is classified using actual ratio values rather than the magnitude of the expression of the two genes.

 ----------

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Fri, 17 Aug 2018 07:00:42 -0000

--- v8
+++ v9
@@ -19,6 +19,7 @@

 The miniABS does not lose information when subtyping is classified using actual ratio values rather than the magnitude of the expression of the two genes.

+
 ----------
 How to install
 --------------------
@@ -30,7 +31,6 @@

 ----------
-
 How to use
 ----------------

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Fri, 17 Aug 2018 06:59:22 -0000

--- v7
+++ v8
@@ -4,8 +4,11 @@
 -------------------------------------------------------------------------------------------------------------------------

+
+----------
 outstanding features of miniABS
 ------------------------------------------
+
 miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression deduction (which can ensure reproducibility of subtype assignments).

 miniABS has high accuracy without bias for various gene expression quantification technologies.
@@ -19,6 +22,7 @@
 ----------
 How to install
 --------------------
+
 Download the source and install it as shown below.

 > install.packages("miniABS_0.1.0.tar.gz", repos = NULL, type = "source")

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Fri, 17 Aug 2018 06:57:51 -0000

--- v6
+++ v7
@@ -4,6 +4,17 @@
 -------------------------------------------------------------------------------------------------------------------------

+outstanding features of miniABS
+------------------------------------------
+miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression deduction (which can ensure reproducibility of subtype assignments).
+
+miniABS has high accuracy without bias for various gene expression quantification technologies.
+
+miniABS predicts subtypes with only 11 biologically meaningful genes in the breast cancer subtype.
+
+The miniABS uses pairwise gene expression ratios (PGER), which is statistically significantly different between subtypes than the expression of a single gene, allowing subtypes to be well classified.
+
+The miniABS does not lose information when subtyping is classified using actual ratio values rather than the magnitude of the expression of the two genes.

 ----------
 How to install

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Thu, 19 Jul 2018 12:31:35 -0000

--- v5
+++ v6
@@ -1,6 +1,6 @@
 **miniABS**
 ===============================================
-miniABS: an absolute single sample subtype classifier of breast cancer with 11 functional genes
+miniABS: an absolute, single-sample subtype classifier of breast cancer with 11 functional genes
 -------------------------------------------------------------------------------------------------------------------------

Home modified by Mi-kyoung Seo

Mi-kyoung Seo — Wed, 30 May 2018 01:46:10 -0000

--- v4
+++ v5
@@ -29,8 +29,8 @@
 * Set the directory where the output file will be created to ratioDir.

 >ratioDir = "directory"
-createMatrix(exprRNAseq, ratioDir) #generate a ratio matrix
- classifierMiniABS(ratioDir) # classify using 7 models and report final predicted subtypes
+>createMatrix(exprRNAseq, ratioDir) #generate a ratio matrix
+>classifierMiniABS(ratioDir) # classify using 7 models and report final predicted subtypes

 A description of the function can be found by entering :