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zqfang / QPCR

Calculate the Delta_Ct, Delta_Delta_Ct , Fold Changes and Student's t-tests from qRT-PCR experiment
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strange difference result #3

Open lmanchon opened 4 years ago

lmanchon commented 4 years ago

--Hi,

i have a problem with one of my result using 'pcr' R package, below it's my little input file:

Sample_Name Target Ct DMSO miR124 29.46 DMSO miR124 29.13 DMSO miR124 29.03 DMSO miR16 24.97 DMSO miR16 24.94 DMSO miR16 25.05 ABX464 miR124 27.1 ABX464 miR124 28.09 ABX464 miR124 26.91 ABX464 miR16 25.09 ABX464 miR16 25.21 ABX464 miR16 25.12

gene control=miR16 condition control=DMSO Gene of interest: miR124

With 'pcr' package i obtain FC(miR124)=3.98

group gene normalized calibrated relative_expression error lower upper 1 ABX464 miR124 2.226667 -1.993333 3.981559 0.6366579 2.5609434 6.190223 2 DMSO miR124 4.220000 0.000000 1.000000 0.2320919 0.8513995 1.174537

but with your tool i found FC(miR124)=5.11: python qpcrCalculate.py -d test_interest_data.xls -s Sheet1 -i miR16 -e DMSO -o bioRep

                     CT Rep0  CT Rep1  CT Rep2  Ct Mean (old)     Ct SD    Ct Mean  Delta Ct  DDelta Ct  Fold Changes

Sample Name Target Name 96H_ABX464 miR124 27.10 28.09 26.91 27.366667 0.633588 27.005000 1.865 -2.355 5.115942 miR16 25.09 25.21 25.12 25.140000 0.062450 25.140000 0.000 0.000 1.000000 96H_DMSO miR124 29.46 29.13 29.03 29.206667 0.225019 29.206667 4.220 0.000 1.000000 miR16 24.97 24.94 25.05 24.986667 0.056862 24.986667 0.000 0.000 1.000000

I don't know how to explain this difference, do you have a clue ?

thank you so much.

best,

Laurent --

zqfang commented 4 years ago

how about add python qpcrCalculate.py ... -m bioRep ... in your command? In this way, you'll use all data instead of throwing some away.

I've improved the usage. Try to use python qpcrCalculate.py ... -m stat ... to get p values