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)