| Title |
A Bayesian approach to determine the composition of heterogeneous cancer tissue
|
|---|---|
| Published in |
BMC Bioinformatics, March 2018
|
| DOI | 10.1186/s12859-018-2062-0 |
| Pubmed ID | |
| Authors |
Ashish Katiyar, Anwoy Mohanty, Jianping Hua, Sima Chao, Rosana Lopes, Aniruddha Datta, Michael L. Bittner |
| Abstract |
Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. The algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma). The algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 31% |
| Researcher | 5 | 31% |
| Student > Bachelor | 1 | 6% |
| Unknown | 5 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 25% |
| Mathematics | 2 | 13% |
| Business, Management and Accounting | 1 | 6% |
| Environmental Science | 1 | 6% |
| Computer Science | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 6 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 March 2018.
All research outputs
#14,559,172
of 23,316,003 outputs
Outputs from BMC Bioinformatics
#4,830
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Outputs of similar age
#189,858
of 333,248 outputs
Outputs of similar age from BMC Bioinformatics
#66
of 112 outputs
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