**10**wrote:

Hi,

I've a small question concerning differential expression analysis regarding 12 samples divided into 2 cell lines (cell 1 & 2), two conditions: tumor (T) and control (C):

Cell | Condition | Patient number (p) |

Cell1 | T | 1 |

Cell1 | T | 2 |

Cell1 | T | 4 |

Cell2 | T | 1 |

Cell2 | T | 2 |

Cell2 | T | 4 |

Cell1 | C | 3 |

Cell1 | C | 5 |

Cell1 | C | 6 |

Cell2 | C | 3 |

Cell2 | C | 5 |

Cell2 | C | 6 |

In order to capture the differences among Cell1 and Cell2 (considering Cell2 and condition C as reference levels), I used the following model:

design = ~ Cell+Condition+Cell:Condition

but the DEGs (significant genes) I am getting here via interaction term (Cell:Condition) is not considering patient. Therefore, please help me to design a model which can perform a differential expression analysis by doing the following:

(i) Cell1/Cell2: T samples vs. Cell1/Cell2 C samples including patient information.

**I like to obtain DEGs between Cell 1 and Cell2 cell lines considering (i) comparison.**

**Thank you,**

**16k**• written 3 months ago by dickson.russel •

**10**