Get your google cache back

Method 1:

http://webcache.googleusercontent.com/search?q=cache:THE_WEB_URL

Method 2:
1. Save a bookmark with code below

javascript:window.open("http://webcache.googleusercontent.com/search?q=cache:"+encodeURIComponent(location.href))

2. go to the website and then click the saved bookmark, and you get the cache.

Batch effect

library(affy)

wk.dir <- "E:\\CEL\\BrainCancer\\U133B\\76samples"
rma.file <- "Brain.U133B.76.txt"

####################
# RMA
setwd(wk.dir)
data.rma<-justRMA()
write.exprs(data.rma, file=rma.file)


####################
# Check batch effect
source("E:\\R_lib\\Microarray.R")
get.files.info()
#!!!Edit the color manually in file "files.info.user.batch.txt"
check.batch.effect(rma.file)


####################
# RMA for each batch
rma.by.batch()

BC170(TRANSBIG) vs BC_LCM

Overlap of signatures:

Inv:

1.	211577_s_at	insulin-like growth factor 1 (so...
2.	213605_s_at	hypothetical protein MGC22265
3.	217430_x_at	collagen, type I, alpha 1
4.	222380_s_at	programmed cell death 6

T cell receptor????



Prol:
823_at
49306_at
55081_at
60474_at
200039_s_at
200052_s_at
200600_at
200628_s_at
200687_s_at
200755_s_at
200756_x_at
200757_s_at
200783_s_at
200790_at
200824_at
200827_at
200872_at
200934_at
201012_at
201030_x_at
201037_at
201088_at
201160_s_at
201161_s_at
201195_s_at
201196_s_at
201197_at
201201_at
201202_at
201215_at
201231_s_at
201250_s_at
201291_s_at
201292_at
201300_s_at
201387_s_at
201397_at
201433_s_at
201478_s_at
201479_at
201487_at
201555_at
201559_s_at
201564_s_at
201579_at
201587_s_at
201663_s_at
201664_at
201725_at
201774_s_at
201795_at
201820_at
201833_at
201853_s_at
201896_s_at
201897_s_at
201923_at
201930_at
201938_at
201968_s_at
201970_s_at
201976_s_at
201980_s_at
201983_s_at
201984_s_at
202035_s_at
202036_s_at
202037_s_at
202052_s_at
202074_s_at
202094_at
202095_s_at
202097_at
202107_s_at
202132_at
202133_at
202134_s_at
202146_at
202147_s_at
202188_at
202200_s_at
202233_s_at
202234_s_at
202236_s_at
202267_at
202269_x_at
202270_at
202307_s_at
202341_s_at
202342_s_at
202345_s_at
202412_s_at
202413_s_at
202431_s_at
202435_s_at
202446_s_at
202483_s_at
202504_at
202524_s_at
202580_x_at
202589_at
202613_at
202619_s_at
202625_at
202626_s_at
202637_s_at
202638_s_at
202643_s_at
202644_s_at
202647_s_at
202690_s_at
202705_at
202719_s_at
202760_s_at
202854_at
202870_s_at
202887_s_at
202902_s_at
202911_at
202932_at
202935_s_at
202951_at
202954_at
202965_s_at
203021_at
203022_at
203038_at
203074_at
203120_at
203126_at
203139_at
203145_at
203211_s_at
203213_at
203214_x_at
203256_at
203263_s_at
203276_at
203358_s_at
203362_s_at
203405_at
203418_at
203428_s_at
203510_at
203554_x_at
203560_at
203574_at
203612_at
203625_x_at
203636_at
203637_s_at
203645_s_at
203691_at
203692_s_at
203693_s_at
203702_s_at
203705_s_at
203706_s_at
203712_at
203744_at
203748_x_at
203755_at
203764_at
203780_at
203819_s_at
203820_s_at
203828_s_at
203856_at
203881_s_at
203909_at
203921_at
203964_at
204023_at
204030_s_at
204033_at
204060_s_at
204061_at
204087_s_at
204124_at
204126_s_at
204127_at
204146_at
204162_at
204170_s_at
204197_s_at
204198_s_at
204203_at
204222_s_at
204240_s_at
204244_s_at
204259_at
204268_at
204304_s_at
204318_s_at
204401_at
204437_s_at
204444_at
204510_at
204533_at
204580_at
204603_at
204641_at
204655_at
204695_at
204702_s_at
204709_s_at
204724_s_at
204725_s_at
204733_at
204750_s_at
204751_x_at
204767_s_at
204768_s_at
204806_x_at
204822_at
204825_at
204855_at
204913_s_at
204914_s_at
204915_s_at
204962_s_at
204992_s_at
205024_s_at
205044_at
205046_at
205047_s_at
205126_at
205157_s_at
205159_at
205199_at
205229_s_at
205240_at
205339_at
205347_s_at
205350_at
205363_at
205393_s_at
205394_at
205475_at
205485_at
205487_s_at
205548_s_at
205569_at
205585_at
205671_s_at
205681_at
205733_at
205778_at
205819_at
205860_x_at
206023_at
206033_s_at
206074_s_at
206082_at
206102_at
206157_at
206245_s_at
206276_at
206332_s_at
206364_at
206373_at
206391_at
206392_s_at
206513_at
206550_s_at
206560_s_at
206571_s_at
206632_s_at
206734_at
206999_at
207002_s_at
207030_s_at
207039_at
207165_at
207266_x_at
207571_x_at
207668_x_at
207719_x_at
207828_s_at
208103_s_at
208358_s_at
208433_s_at
208627_s_at
208628_s_at
208638_at
208639_x_at
208729_x_at
208767_s_at
208795_s_at
208941_s_at
208965_s_at
208966_x_at
209026_x_at
209056_s_at
209122_at
209125_at
209126_x_at
209155_s_at
209170_s_at
209191_at
209205_s_at
209211_at
209212_s_at
209283_at
209289_at
209290_s_at
209318_x_at
209373_at
209395_at
209396_s_at
209397_at
209406_at
209408_at
209421_at
209464_at
209535_s_at
209545_s_at
209642_at
209644_x_at
209669_s_at
209686_at
209709_s_at
209773_s_at
209791_at
209800_at
209822_s_at
209825_s_at
209834_at
209842_at
209868_s_at
209891_at
209900_s_at
209990_s_at
210015_s_at
210024_s_at
210029_at
210052_s_at
210073_at
210074_at
210087_s_at
210092_at
210145_at
210147_at
210153_s_at
210163_at
210164_at
210347_s_at
210381_s_at
210466_s_at
210559_s_at
210563_x_at
210692_s_at
210754_s_at
210785_s_at
210821_x_at
210845_s_at
210873_x_at
210933_s_at
210983_s_at
211042_x_at
211056_s_at
211063_s_at
211075_s_at
211084_x_at
211122_s_at
211126_s_at
211450_s_at
211466_at
211467_s_at
211519_s_at
211714_x_at
211725_s_at
211762_s_at
211767_at
211798_x_at
211911_x_at
211966_at
211967_at
212020_s_at
212021_s_at
212022_s_at
212023_s_at
212141_at
212142_at
212171_x_at
212174_at
212236_x_at
212247_at
212262_at
212263_at
212265_at
212274_at
212276_at
212295_s_at
212311_at
212314_at
212333_at
212345_s_at
212350_at
212371_at
212378_at
212397_at
212398_at
212501_at
212589_at
212590_at
212680_x_at
212771_at
212816_s_at
212922_s_at
212949_at
213029_at
213032_at
213060_s_at
213113_s_at
213122_at
213134_x_at
213137_s_at
213170_at
213226_at
213260_at
213310_at
213338_at
213457_at
213484_at
213523_at
213564_x_at
213680_at
213711_at
214038_at
214051_at
214104_at
214240_at
214431_at
214581_x_at
214596_at
214710_s_at
214838_at
214844_s_at
214845_s_at
215034_s_at
215049_x_at
215127_s_at
215223_s_at
215363_x_at
215719_x_at
215729_s_at
215942_s_at
215945_s_at
216237_s_at
216252_x_at
216640_s_at
216841_s_at
216952_s_at
217077_s_at
217294_s_at
217755_at
217834_s_at
217867_x_at
218019_s_at
218039_at
218051_s_at
218236_s_at
218238_at
218239_s_at
218308_at
218319_at
218336_at
218350_s_at
218355_at
218497_s_at
218499_at
218542_at
218564_at
218585_s_at
218618_s_at
218662_s_at
218663_at
218684_at
218726_at
218738_s_at
218755_at
218781_at
218796_at
218826_at
218847_at
218856_at
218868_at
218877_s_at
218886_at
218963_s_at
218984_at
218995_s_at
219000_s_at
219006_at
219010_at
219065_s_at
219148_at
219212_at
219225_at
219275_at
219306_at
219385_at
219439_at
219454_at
219489_s_at
219493_at
219497_s_at
219498_s_at
219555_s_at
219582_at
219588_s_at
219615_s_at
219654_at
219740_at
219787_s_at
219795_at
219806_s_at
219863_at
219867_at
219918_s_at
219959_at
219960_s_at
219974_x_at
220085_at
220147_s_at
220238_s_at
220239_at
220295_x_at
220386_s_at
220425_x_at
220432_s_at
220559_at
220624_s_at
220625_s_at
220651_s_at
220658_s_at
220840_s_at
220865_s_at
220892_s_at
220941_s_at
221004_s_at
221016_s_at
221059_s_at
221185_s_at
221203_s_at
221261_x_at
221436_s_at
221477_s_at
221505_at
221510_s_at
221520_s_at
221524_s_at
221591_s_at
221676_s_at
221677_s_at
221698_s_at
221830_at
221854_at
221872_at
221875_x_at
221881_s_at
221920_s_at
221922_at
222036_s_at
222037_at
222039_at
222062_at
222077_s_at
222158_s_at
222242_s_at

Meta:
59437_at
200670_at
200711_s_at
200811_at
201568_at
201596_x_at
202090_s_at
202109_at
202201_at
202263_at
202454_s_at
202489_s_at
202862_at
202908_at
203453_at
203771_s_at
204045_at
204295_at
204485_s_at
204567_s_at
204623_at
204667_at
205012_s_at
205081_at
205225_at
205472_s_at
205597_at
205768_s_at
205769_at
205879_x_at
206469_x_at
207142_at
207843_x_at
208682_s_at
208872_s_at
209004_s_at
209114_at
209123_at
209149_s_at
209173_at
209224_s_at
209309_at
209366_x_at
209459_s_at
209460_at
209623_at
209625_at
209665_at
209681_at
209696_at
209739_s_at
209740_s_at
209759_s_at
209917_s_at
210720_s_at
211421_s_at
212508_at
212510_at
212637_s_at
212956_at
213627_at
213846_at
214053_at
214404_x_at
215726_s_at
215867_x_at
216381_x_at
217014_s_at
217979_at
218025_s_at
218035_s_at
218048_at
218164_at
218195_at
218211_s_at
218640_s_at
218692_at
218976_at
219127_at
219872_at
220192_x_at
221874_at
221934_s_at
222125_s_at

Overlap of gene signatures of GC and PDA

1. Inv overlap signature_GC and signature_PDA, then DAVID/Gather


KEGG Pathway# Genesp ValueBayes Factor
1.path:hsa04010: MAPK signaling pathway19[show]      0.0051

2.path:hsa04020: Calcium signaling pathway16[show]      0.0071

3.path:hsa04210: Apoptosis10[show]     0.0071

4.path:hsa04610: Complement and coagulation cascades8[show]     0.010

Gene Ontology		# Genes		p Value		Bayes Factor
1.	GO:0007154 [3]: cell communication	156	[show]		< 0.0001	15
2.	GO:0007275 [2]: development	88	[show]		< 0.0001	9
3.	GO:0007165 [4]: signal transduction	123	[show]		< 0.0001	8
4.	GO:0009653 [3]: morphogenesis	62	[show]		< 0.0001	8
5.	GO:0009887 [4]: organogenesis	52	[show]		< 0.0001	7
6.	GO:0048513 [3]: organ development	52	[show]		< 0.0001	7
7.	GO:0006956 [6]: complement activation	8	[show]		0.0002	5
8.	GO:0007242 [5]: intracellular signaling cascade	50	[show]		0.0006	4

2. Pro

KEGG Pathway		# Genes		p Value		Bayes Factor
1.	path:hsa04110: Cell cycle	16	[show]		< 0.0001	11
2.	path:hsa04080: Neuroactive ligand-receptor interaction	2	[show]		0.001	2
3.	path:hsa00100: Biosynthesis of steroids	4	[show]		0.007	1

Gene Ontology		# Genes		p Value		Bayes Factor
1.	GO:0000278 [6]: mitotic cell cycle	35	[show]		< 0.0001	36
2.	GO:0007067 [8]: mitosis	29	[show]		< 0.0001	35
3.	GO:0000087 [7]: M phase of mitotic cell cycle	29	[show]		< 0.0001	35
4.	GO:0000279 [6]: M phase	32	[show]		< 0.0001	33
5.	GO:0000280 [7]: nuclear division	31	[show]		< 0.0001	33
6.	GO:0007049 [5]: cell cycle	62	[show]		< 0.0001	30
7.	GO:0008283 [4]: cell proliferation	73	[show]		< 0.0001	24
8.	GO:0000910 [5]: cytokinesis	18	[show]		< 0.0001	14

3.Meta

KEGG Pathway		# Genes		p Value		Bayes Factor
1.	path:hsa00480: Glutathione metabolism	3	[show]		0.0002	5
2.	path:hsa00260: Glycine, serine and threonine metabolism	2	[show]		0.003	2
3.	path:hsa00791: Atrazine degradation	1	[show]		0.004	1
4.	path:hsa00460: Cyanoamino acid metabolism	1	[show]		0.007	1
5.	path:hsa00430: Taurine and hypotaurine metabolism	1	[show]		0.009	1
6.	path:hsa00625: Tetrachloroethene degradation	1	[show]		0.009	1
7.	path:hsa00363: Bisphenol A degradation	1	[show]		0.01	0

Gene Ontology # Genes p Value Bayes Factor 1. GO:0007586 [4]: digestion 3 [show]         0.001 3 2. GO:0008203 [7]: cholesterol metabolism 3 [show]         0.001 3 3. GO:0016125 [6]: sterol metabolism 3 [show]         0.001 3 4. GO:0006848 [8]: pyruvate transport 1 [show]        0.005 2 5. GO:0007039 [7]: vacuolar protein catabolism 1 [show]        0.005 2 6. GO:0007165 [4]: signal transduction 3 [show]       0.01 1 7. GO:0045494 [8]: photoreceptor maintenance 1 [show]       0.01 1 8. GO:0006621 [6]: protein-ER retention 1 [show]       0.01 1

survival rate of gastric cancer

Statistics

This year, an estimated 21,320 adults (13,020 men and 8,300 women) in the United States will be diagnosed with stomach cancer. It is estimated that 10,540 deaths (6,190 men and 4,350 women) from this disease will occur this year.

The incidence of stomach cancer varies in different parts of the world. Although it is decreasing in the Western world, it is still one of the most common cancer types worldwide.

The five-year survival rate (percentage of people who survive at least five years after the cancer is detected, excluding those who die from other diseases) of people with stomach cancer is about 26%. This statistic reflects the fact that most people with stomach cancer are diagnosed after the cancer has already spread to other parts of the body. If stomach cancer is found before it has spread, the five-year survival rate is generally higher but depends on the stage of the cancer found during surgery.

Cancer survival statistics should be interpreted with caution. These estimates are based on data from thousands of people with this type of cancer in the United States each year, but the actual risk for a particular individual may differ. It is not possible to tell a person how long he or she will live with stomach cancer. Because the survival statistics are measured in five-year intervals, they may not represent advances made in the treatment or diagnosis of this cancer. Learn more about understanding statistics.

Statistics adapted from the American Cancer Society's publication, Cancer Facts & Figures 2012.

http://www.cancer.net/patient/Cancer+Types/Stomach+Cancer/ci.Stomach+Cancer.printer

read cel file

library(affxparser)
celfile = "E:\\CEL\\BreastCancer\\GSE28821_LCM\\OneBatch\\GSM713753.CEL"
dat.header <- readCelHeader(celfile)$datheader
split.header <- strsplit(dat.header, " ")[[1]]
scan.date <- grep("\\d+\\/\\d+\\/\\d+", split.header, perl=T, value = T)
scan.time <- grep("\\d+:\\d+:\\d+", split.header, perl=T, value = T)

Bibtex to Endnotes

http://db.tt/57ztmPmu

Principal Investigator@Ontario Institute for Cancer Research

http://bioinformatics.ca/resources/jobs/principal-investigator

Institution/Company: 

Ontario Institute for Cancer Research

Location: 

Downtown Toronto

Job Description: 

Position: Principal Investigator
Site: MaRS Centre, Toronto
Department: Informatics & Bio-computing
Reports To: Director, Informatics & Bio-computing Platform
Salary: Commensurate with level of experience
Hours: 35 Hrs/week
Status: Full-time, Permanent

The Ontario Institute for Cancer Research (OICR) is seeking Junior, Intermediate and Senior Principal Investigators (PIs) in Bioinformatics, Computational Biology and Biostatistics to undertake world-class computational research in a wide range of research areas, including any of the following: (1) discovery of key genetic alterations in the initiation or progression of cancer; (2) identification of biomarkers indicative of tumour subtypes or predictive of response to targeted therapy; (3) modeling of regulatory networks relevant to disease pathways; (4) analysis of genetic and environmental risk factors for cancer in patient populations; (5) use of machine learning and/or biostatistical approaches to develop novel algorithms and computational techniques for genomic and/or epigenomic data; (6) development of interoperability standards for exchanging and collaboratively annotating genome-scale data sets; or (7) development of software engineering techniques for managing and manipulating genome-scale datasets and complex analytic workflows. We expect to appoint up to five PIs over the period 2012-2014.

PIs will be expected to mentor trainees, and to build collaborations both within and outside the OICR community. In addition to base funds provided by the Institute to support the PI's salary and personnel, PIs are expected to raise additional research funds from external competitive granting agencies. The OICR will assist PIs in obtaining faculty appointments at the University of Toronto or another affiliated academic institution.

QUALIFICATIONS
• An MD or PhD with a proven track record in computational biology, bioinformatics, or biostatistics;
• For new PIs, a record of independent research and either first-author peer reviewed publications or the publication of software, databases or other significant community resources.
• For senior and intermediate-level PIs, international recognition and a strong publication record of relevance, proven leadership and management experience including the building of strong research teams, as well as a strong record of mentorship and/or teaching;
• Eligible to hold the rank of assistant, associate or full professor at an Ontario university;
• Excellent communication and presentation skills.

OICR is an innovative cancer research institute located in the MaRS Centre in the Discovery District in downtown Toronto. OICR is addressing significant challenges in cancer research with multi-disciplinary, multi-institutional teams. New discoveries to prevent, detect and treat cancer will be moved from the bench to practical applications in patients. The OICR team is growing quickly. We are innovative, dedicated professionals who bring expertise to each of our roles. We are looking for individuals interested in being part of a culture of excellence that will result in Ontario being recognized internationally as a leading jurisdiction for cancer research.

Launched in December 2005, OICR is an independent institute funded by the Government of Ontario through the Ministry of Economic Development and Innovation.

For more information about OICR, please visit the website at www.oicr.on.ca.

POSTED DATE: June 1, 2012
CLOSING DATE: Posted until filled

Interested candidates may apply here

https://www.recruitingsite.com/csbsites/oicr/JobDescription.asp?JobNumber=675388

OICR has a diverse workforce and is an equal opportunity employer.

The Ontario Institute for Cancer Research thanks all applicants. However, only those under consideration will be contacted. Candidates will be expected to provide their current employer as a reference.

Resume Format: If you elect to apply, you will need a text or HTML version of your resume so that you can cut and paste it into the application box provided. Before you submit the completed application, you will be asked to attach one or two files to your application. Please attach your resume as a .doc file.

try

1. use microdissected BC-class to predict NCI60/GEMINI
ANSWER: looks like not good.

2. use macrodissected BC-class to predict BC57 with some dropped samples (to be population balance)
Answer: tried, but not good result, so it is not because of population bias.

Normalization before NTP

Before NTP, is it better to do standardization on gene(row) then on array(column) to avoid population bias.
Or may median polish (an iterative method)?

ANSWER: It is NOT GOOD to do standardization on gene(row) then on array(column) to avoid population bias.


################################################
# standardization by row and column, respetively
#By row (gene)
std.data.by.row <- t(scale(t(data.filtered), scale=T))
#By column (array)
std.data <- scale(std.data.by.row, scale=T)

Macrodissection versus microdissection of rectal carcinoma: minor influence of stroma cells to tumor cell gene expression profiles

As microdissection yielded low tissue and RNA quantities, extra rounds of mRNA amplification were necessary to obtain sufficient RNA for microarray experiments. These second rounds of amplification influenced the gene expression profiles. Moreover, the presence of stroma cells in macrodissected samples had a minor contribution to the tumor cell gene expression profiles, which can be explained by the observation that more RNA is extracted from tumor epithelial cells than from stroma.

two classifiers for cancer

In practice, we need two classifiers, one from microdissected tumor samples, the other from macrodissected tumor samples. Hence we can predicted microdissected samples by microdissected classifier, and macrodissected samples by macrodissected classifier.


Q: Is it not good to predicted microdissected samples by macrodissected classifier?
Answer: NOT good, confirmed.


Q: Really? It could also be due to population bias between training set and test set?
Answer: Not because of population bias


Q: it may be okay to predict macrodissected samples by microdissected classifier.?
Answer: NO, the other way around isn't work either.


 check with breast cancer datasets.

duke-nus center for cb

65165240
http://www.ezlink.com.sg/top-up/ez-reload-apply2.php?card=yes

Macrodissection vs microdissection

Macrodissection versus microdissection of rectal carcinoma: minor influence of stroma cells to tumor cell gene expression profiles

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2738