Zhengdeng Lei, PhD

Zhengdeng Lei, PhD

2009 - Present Research Fellow at Duke-NUS, Singapore
2007 - 2009 High Throughput Computational Analyst, Memorial Sloan-Kettering Cancer Center, New York
2003 - 2007 PhD, Bioinformatics, University of Illinois at Chicago

Saturday, July 28, 2012

TGFbeta


Cancer

In normal cells, TGF-β, acting through its signaling pathway, stops the cell cycle at the G1 stage to stop proliferation, induce differentiation, or promote apoptosis. When a cell is transformed into a cancer cell, parts of the TGF-β signaling pathway are mutated, and TGF-β no longer controls the cell. These cancer cells proliferate. The surrounding stromal cells (fibroblasts) also proliferate. Both cells increase their production of TGF-β. This TGF-β acts on the surrounding stromal cells, immune cells, endothelial and smooth-muscle cells. It causes immunosuppression and angiogenesis, which makes the cancer more invasive.[7] TGF-β also converts effector T-cells, which normally attack cancer with an inflammatory (immune) reaction, into regulatory (suppressor) T-cells, which turn off the inflammatory reaction.

Friday, July 27, 2012

various GF/GFR such as PDGF/PDGFR vs EGF/EGFR

Pls check various GF/GFR such as PDGF/PDGFR vs EGF/EGFR(HER1-4, ERBB1-4) in SG201

Thursday, July 26, 2012

PI3K in breast cancer

It is revealed that patients with ER+/HER2+ compared with ER-/HER2+ breast cancers may actually benefit more from drugs that inhibit the PI3K/AKT molecular pathway


http://en.wikipedia.org/wiki/ErbB
Estrogen Receptor Status of HER2+ Breast Cancer Correlates With Response to Anti-HER Therapies. ScienceDaily (May 6, 2010)[1]


http://www.sciencedaily.com/releases/2010/05/100506112557.htm

http://www.pnas.org/content/107/22/10208.short

Saturday, July 21, 2012

transcription factors-Pleiotropy

the existence of three subtypes of cancers may be due to malfunctioning of transcription factors, since a single type of TF can simultaneously affect the expression of a large cohort of downstream responder genes.
But metabolic-subtype may be an exception.

Wednesday, July 18, 2012

Run NTP


source("E:\\R_lib\\Microarray.R")
setwd("E:\\Projects\\David_Wnt\\BC170\\NTP_anyplatform\\GPL1223\\GSE1379")
gct.file <- "GSE1379_series_matrix.gct"
BC.Class.anyplatform(gct.file, src.dir=getwd(), output.dir=getwd())

Sunday, July 8, 2012

Cell Culture

http://www.microbiologybytes.com/video/culture.html

Oxygen in Stem Cell Biology: A Critical Component of the Stem Cell Niche


Summary

The defining hallmark of stem cells is their ability to self-renew and maintain multipotency. This capacity depends on the balance of complex signals in their microenvironment. Low oxygen tensions (hypoxia) maintain undifferentiated states of embryonic, hematopoietic, mesenchymal, and neural stem cell phenotypes and also influence proliferation and cell-fate commitment. Recent evidence has identified a broader spectrum of stem cells influenced by hypoxia that includes cancer stem cells and induced pluripotent stem cells. These findings have important implications on our understanding of development, disease, and tissue-engineering practices and furthermore elucidate an added dimension of stem cell control within the niche.

Wednesday, July 4, 2012

run.ComBat



run.ComBat <- function(exp.file, sample_info_file="batches.info.txt", src.dir=getwd())
{
setwd(src.dir)
if(!file.exists(sample_info_file)) {
print(paste(sample_info_file, " not found in", src.dir))
return()
}
gene.exp <- read.table(exp.file, header=T, sep="\t", row.names=1)

forcombat.exp.file <- sub("txt", "forcombat.txt", exp.file, ignore.case = T)
after.combat.exp.file <- sub("txt", "AFTER.Combat.txt", exp.file, ignore.case = T)

write.table(gene.exp, file=forcombat.exp.file, sep="\t", quote=F, row.names=F)
gene.id <- rownames(gene.exp)
ComBat(expression_xls=forcombat.exp.file, sample_info_file=sample_info_file, type='txt')
file.remove(forcombat.exp.file)

combat.out.file <- paste("Adjusted_", forcombat.exp.file, "_.xls", sep="")

combat.gene.exp <- read.table(combat.out.file, header=T, sep="\t")
rownames(combat.gene.exp) <- gene.id
file.remove(combat.out.file)
write.table(combat.gene.exp, file=after.combat.exp.file, sep="\t", quote=F, col.names=NA)
}

Order statistics for combining various genomic data sources



#Order statistics. The rankings from the separate data sources are combined using
#order statistics. A Q statistic is calculated from all rank ratios using the joint
#cumulative distribution of an N-dimensional order statistic as previously done
#by Stuart et al.31
Q <- function(r)
{
r <- sort(r)
N <- length(r)
if (N==1)
{
return (r)
}
s <- 0
for (i in 1:N)
{
del.element.idx <- N-i+1
if (N==i)
{
s = s+(r[N-i+1])*Q(r[-del.element.idx])

}else{
s = s+(r[N-i+1]-r[N-i])*Q(r[-del.element.idx])
}
}
s <- factorial(N)*s
return (s)
}

#r <- c(0.2, 0.05, 0.2)
#Q(r)


d<-matrix(c(sample(1:10),sample(1:10),sample(1:10),sample(1:10)), nrow=10, byrow=F)
rownames(d) <- c(paste("gene", seq(1:10), sep=""))
colnames(d) <- c("gene.exp", "methyl", "CNV", "Mutation")
d
r = d/10
ranking <- apply(r, 1, Q)
sort(ranking)





> d #the smaller the ranking, the more important of the feature, e.g. gene 1 and 3 have highest t-score/foldchange
       gene.exp methyl CNV Mutation
gene1         1      2   2        7
gene2         9      8   8        3
gene3         2      4   3        8
gene4        10      5   1        1
gene5         3     10   4        5
gene6         8      9   7       10
gene7         6      6   9        4
gene8         7      7   5        6
gene9         5      1   6        9
gene10        4      3  10        2
> r = d/10
> ranking <- apply(r, 1, Q)
> sort(ranking)
   gene1    gene4    gene3   gene10    gene9    gene8    gene5    gene7    gene2    gene6
  4.4640  10.7424  29.7216  41.2416  49.6224  68.1120  79.4016 108.7488 144.5472 268.3296
>