Hello Everyone,

I have several samples of some types of biological data, such as: mRNA , miRNA and DNA Methylation where each of them has two conditions : normal and tumor.

I would like to implement the differential analysis between the two conditions of the entire samples in order to obtain the p- value for each gene.

I thought of using DESeq, but I could not because my data is already normalized . For example in mRNA samples, the data are normalized and applied the log 2, as you can see below:

Gene | N1 | T1 | N2 | T2 |
---|---|---|---|---|

ARHGEF10L | 3.3151314 | 3.2328449 | 3.2583983 | 3.4465871 |

HIF3A | 3.0830942 | 1.9722883 | 3.2255372 | 1.5074648 |

RNF17 | -0.7374466 | -1.6201573 | -1.3785693 | -4.2487934 |

RNF10 | 3.5662794 | 3.5837116 | 3.5824115 | NA |

From what I read in the DESeq documentation , It needs a table containing to reads count , I have not it. So I wonder if there is some way to get some statistical analysis that makes the difference between the two conditions ( normal and tumor) of all samples in order to obtain the p-value of genes ?

The final result that I would like to obtain is like this:

Gene | p-value |
---|---|

ARHGEF10L | 0.2342 |

HIF3A | 0.676 |

RNF17 | 0.892 |

RNF10 | 0.1243 |

Please, can anyone give me a suggestion to resolve it in python?

Thank you very much for all the attention!

python, perl, R, whatever, you can do a t-test for each gene to get the p-value, and then you need to adjust your p-values for multiple comparisons with some method such as FDR.

In R, it's straightforward and simple:

Hope this helps.

Once you get the p-values for your data, you may want to have a look what Open Targets has got in its Platform e.g. the differential RNA expression as one of the pieces of Evidence for HIF3A in cancer.