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itsjay510
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@itsjay510-10691
Last seen 8.8 years ago
Hi am trying to find Deferentially expressed genes using moderated t test.
i have data set, in which Rows represent the gene and columns represent the samples
i want to contrast treated samples with their paired control samples
help me finding DEGs.
i am using limma package
| GSM864598 | GSM864599 | GSM864602 | GSM864603 | GSM864606 | GSM864607 | GSM864596 | GSM864597 | GSM864600 | GSM864601 | GSM864604 | GSM864605 | yi | vi | |
| X244901_at | 6.776531 | 6.104926 | 5.715614 | 7.233082 | 5.571574 | 6.180012 | 5.640133 | 5.684181 | 5.530555 | 5.800419 | 5.823699 | 5.330674 | 0.105772 | 0.02183 |
| X244906_at | 6.66062 | 5.806678 | 5.698589 | 7.158189 | 6.281229 | 5.76415 | 5.889159 | 5.641552 | 5.573598 | 5.89218 | 5.899459 | 5.414471 | 0.085405 | 0.019536 |
| X244912_at | 7.932606 | 6.84728 | 6.832885 | 8.264751 | 5.328053 | 5.029717 | 6.505038 | 6.90041 | 7.003858 | 8.042032 | 7.156385 | 6.58032 | -0.04739 | 0.093342 |
| X244921_s_at | 7.997751 | 7.356617 | 7.669547 | 7.717615 | 6.588453 | 6.568265 | 7.200101 | 7.647656 | 7.38472 | 7.350834 | 7.064614 | 7.193399 | 0.001298 | 0.017241 |
| X244932_at | 11.64794 | 10.03484 | 10.23667 | 10.28013 | 8.920964 | 8.69407 | 10.8674 | 11.52479 | 11.19584 | 11.55401 | 10.4323 | 10.39093 | -0.09788 | 0.043751 |
| X244935_at | 13.59895 | 13.62997 | 13.29624 | 12.98204 | 12.26396 | 12.66498 | 13.62429 | 13.48215 | 13.36982 | 13.56064 | 14.19345 | 13.38942 | -0.03979 | 0.009672 |
| X244937_at | 7.240834 | 7.520988 | 8.10741 | 6.702542 | 5.080801 | 4.708259 | 8.177433 | 8.600435 | 8.377825 | 8.791904 | 7.971662 | 7.916772 | -0.23597 | 0.093255 |
| X244938_at | 9.882005 | 8.641475 | 8.582223 | 8.321563 | 7.644317 | 7.850079 | 9.075239 | 9.109793 | 8.643305 | 8.575635 | 7.970741 | 8.697808 | -0.02235 | 0.028871 |
| X244939_at | 7.983406 | 7.461154 | 7.688547 | 7.636542 | 5.809169 | 5.676872 | 8.362783 | 8.914598 | 8.245868 | 9.016384 | 7.89075 | 7.481278 | -0.16652 | 0.058514 |
| X244940_at | 11.84044 | 11.66448 | 11.74311 | 11.87966 | 10.6607 | 10.6187 | 11.60162 | 12.04053 | 11.41316 | 12.24339 | 11.08462 | 11.15133 | -0.01635 | 0.016113 |
| X244943_at | 7.408653 | 6.577156 | 6.488734 | 7.239988 | 6.467524 | 6.459505 | 6.493534 | 6.588874 | 6.027342 | 6.37802 | 6.045365 | 5.845268 | 0.083698 | 0.012913 |
| X244944_s_at | 10.02859 | 8.855419 | 8.977151 | 10.67256 | 8.284103 | 8.732459 | 8.587317 | 8.207636 | 8.087916 | 9.312543 | 8.676983 | 7.717144 | 0.093544 | 0.038614 |
| X244951_s_at | 8.54809 | 7.476601 | 7.842496 | 8.296598 | 7.91379 | 7.400497 | 7.745904 | 7.480556 | 7.58739 | 7.631457 | 6.910766 | 7.449561 | 0.057934 | 0.011396 |
| X244960_at | 11.72785 | 12.01406 | 11.14341 | 12.12553 | 11.17683 | 12.31869 | 11.27782 | 11.33224 | 10.92046 | 11.11843 | 11.93862 | 10.79201 | 0.045361 | 0.011404 |
| X244961_at | 12.79673 | 12.39202 | 12.29156 | 13.00277 | 11.71663 | 11.11163 | 12.84191 | 12.85663 | 12.79113 | 13.21049 | 13.1776 | 12.6762 | -0.05626 | 0.014304 |
| X244962_at | 11.8366 | 11.20559 | 11.37705 | 11.55418 | 9.952691 | 8.942187 | 11.81361 | 12.19516 | 11.70827 | 12.26071 | 11.98956 | 11.43687 | -0.096 | 0.039741 |
| X244964_at | 10.69166 | 10.7561 | 10.76403 | 10.50444 | 10.95081 | 11.11885 | 11.83012 | 11.76404 | 11.16703 | 11.40954 | 11.0751 | 11.19815 | -0.05493 | 0.004248 |
| X244965_at | 11.19758 | 10.38016 | 10.19075 | 10.87879 | 8.791433 | 9.456968 | 10.27487 | 10.43322 | 9.923493 | 11.12434 | 9.649578 | 9.90675 | -0.00682 | 0.033386 |
| X244966_at | 6.258965 | 6.442062 | 6.693303 | 6.170284 | 6.181747 | 6.654887 | 7.188491 | 7.46859 | 6.692804 | 6.99762 | 7.280977 | 6.792862 | -0.09956 | 0.00649 |
| X244967_at | 13.65813 | 13.7837 | 13.63188 | 12.2466 | 13.27109 | 13.96868 | 13.59478 | 13.60148 | 13.71314 | 12.42181 | 12.77641 | 13.56342 | 0.011097 | 0.016834 |
| X244969_at | 11.2506 | 11.21752 | 10.46054 | 11.46594 | 9.901606 | 11.34049 | 11.1107 | 10.85781 | 10.44349 | 10.86069 | 11.85028 | 10.7401 | -0.00344 | 0.017847 |
| X244970_at | 9.128685 | 7.930444 | 7.63848 | 8.230874 | 7.525657 | 7.05547 | 8.26856 | 8.347677 | 8.172589 | 8.974463 | 7.288054 | 8.228849 | -0.03659 | 0.030737 |
| X244972_at | 9.028793 | 9.287458 | 10.4138 | 8.621209 | 8.00466 | 7.004415 | 10.15984 | 10.80862 | 11.68468 | 9.848905 | 6.728298 | 10.6585 | -0.13434 | 0.140364 |
| X244973_at | 10.65944 | 8.400058 | 8.782183 | 9.046878 | 6.646284 | 6.597289 | 8.830157 | 9.027453 | 9.179461 | 9.440766 | 6.21532 | 8.704456 | -0.02493 | 0.138987 |
| X244976_at | 13.72214 | 13.84769 | 13.70145 | 13.54938 | 13.24094 | 12.81051 | 13.76959 | 13.82576 | 13.71159 | 13.75443 | 13.74014 | 13.69258 | -0.01986 | 0.003763 |
| X244978_at | 10.99985 | 10.21171 | 9.839919 | 10.35295 | 10.20204 | 11.47859 | 10.39133 | 10.4882 | 10.16153 | 11.34287 | 11.12019 | 10.27205 | -0.0109 | 0.018053 |
| X244979_at | 10.29872 | 10.19468 | 9.580985 | 9.526613 | 11.16247 | 12.06546 | 10.61693 | 10.78663 | 10.01333 | 11.50501 | 11.4808 | 10.56469 | -0.03347 | 0.038842 |
| X244980_at | 10.19863 | 9.351897 | 8.768296 | 9.196268 | 10.18491 | 10.84084 | 9.507074 | 9.769199 | 9.090223 | 10.29802 | 10.0262 | 9.455298 | 0.006767 | 0.025385 |
| X244981_at | 10.4193 | 9.407501 | 9.100691 | 8.672685 | 9.506871 | 10.0923 | 9.112143 | 9.678138 | 8.810803 | 9.965167 | 9.952154 | 9.312039 | 0.00647 | 0.020697 |
| X244982_at | 10.17748 | 9.649541 | 9.4175 | 8.887304 | 9.580687 | 10.17034 | 9.517987 | 9.753144 | 9.451972 | 10.19661 | 9.477763 | 9.469989 | 0.000266 | 0.010495 |
| X244984_at | 11.96675 | 11.81216 | 11.37606 | 10.95086 | 10.85 | 11.88019 | 11.83387 | 11.68515 | 11.25423 | 11.44653 | 11.98832 | 11.12531 | -0.0072 | 0.009859 |
| X244986_at | 12.75148 | 12.23773 | 12.14586 | 11.84417 | 11.49432 | 12.32184 | 12.26563 | 12.3432 | 11.81534 | 12.41588 | 12.56478 | 11.86412 | -0.00648 | 0.007399 |
Thank you Chris.
but am confused , how to design a contrast and design matrix for data set shown above.
am little weak at statistics.
I'd suggest reading the user guide and working through some of the examples. After that, if you have some specific questions about how to apply it to your data set, we'll be happy to answer them. For vague requests like that in your original post, we'll just be regurgitating the user's guide, and that's a waste of everyone's time.
To add to Aaron's point, if you are, as you say, 'weak at statistics', then you might seriously consider collaborating with someone local who is not. Just because there are tools available that you can use to do something doesn't mean that it's in your best interest to do so.
In other words, there are two issues here; how to get limma to fit the model you want, and knowing if it is a reasonable model to fit and interpreting the results. The former is actually the easier of the two, so if you are getting tripped up on the easier part maybe you should enlist help.