Departamento Teoría de la Señal y Comunicaciones | Home | Datasets Login | About WebGeneKFCA

Dataset Translating Dosage Compensation to Trisomy 21 details

Description:

Down syndrome is a common disorder with enormous medical and social costs, caused by trisomy for Chr21. We tested the concept that gene imbalance across an extra chromosome can be de facto corrected in DS patient stem cells by manipulating a single gene, XIST. Using zinc finger nucleases, we targeted a large, inducible XIST transgene into the Chr21 DYRK1A locus, in DS pluripotent stem cells. XIST RNA coats Chr21 and triggers stable heterochromatin modifications, chromosome-wide transcriptional silencing and DNA methylation to form a “Chr21 Barr Body.” This provides a model to study human chromosome inactivation and creates a system to investigate genomic expression changes and cellular pathologies of trisomy 21, free from genetic and epigenetic noise. In this study, we used microarrays to understand the genome-wide impacts of inducible XIST expression on Chr21 in trisomy 21 human iPS cell lines, and to evaluate the extent of Chr21 silencing trisomic samples versus a disomic male iPS cell line.

Size:
49395 probesets x 27 experiments
Species:
Homo Sapiens
InputData:
Microarray, PrimeView
Gene expression levels (Download ) :
Density estimation

You can have different analysis over these datasets based on different preprocessor. This preprocesor stage is necessary to normalize the raw input matrix from the dataset.

For a given experiment each gene will work on different levels of gene expression, this makes the comparison among different genes impossible. Thus it is required to normalize the expression of each gene to make it comparable among them.
The normalization consists in applying the next formula where $$x_{ij}$$ is the expression of gene i at the experiment j, $$xn_i$$ is the normalized output:

  • $$xn_{ij}=log(x_{ij})$$
  • $$xn_{ij}=log(x_{ij}/\frac {1} {n}\sum _{j=1}^{n}x_{ij})$$
  • $$xn_{ij}=log(x_{ij}/\sqrt [n] {\prod _{j=1}^{n}x_{ij}})$$
  • $$xn_{ij}=log(x_{ij}/max_{j=1}^{n}(x_{ij}))$$
  • $$xn_{ij}=(log(x_{i,j})-\overline{log(x_{i·})})/\sum _{j=1}^{n}log(x_{ij})·\sqrt {m}$$
  • Mean 0 and var 1 in rows and columns of log(x_{ij})
  • $$xn_{ij}=x_{ij}$$
  • $$xn_{ij}=x_{ij}/\frac {1} {n}\sum _{j=1}^{n}x_{ij}$$
  • $$xn_{ij}=x_{ij}/\sqrt [n] {\prod _{j=1}^{n}x_{ij}}$$
  • $$xn_{ij}=x_{ij}/max_{j=1}^{n}(x_{ij})$$
  • $$xn_{ij}=(x_{i,j}-\overline{x_{i·}})/\sum _{j=1}^{n}x_{ij}·\sqrt {m}$$
  • Mean 0 and var 1 in rows and columns

For this dataset there are 2 different analysis with different preprocessors.
Please choose one:

NameAlgorithmGene Expression TypeCreation Date
Preprocessor 1$$xn_{ij}=log(x_{ij}/\frac {1} {n}\sum _{j=1}^{n}x_{ij})$$Raw probeset value2013-08-09 16:53:42.0Details
Preprocessor 2$$xn_{ij}=log(x_{ij}/\frac {1} {n}\sum _{j=1}^{n}x_{ij})$$Raw probeset value2013-08-09 17:19:09.0Details

You need to be logged to create a preprocessor analysis