Documentation · References
Scientific references
Ampliova implements published methods; it did not invent them. These are the works the documentation cites, in one numbered list shared by every page.
If you publish results produced with Ampliova, cite the methods your analysis relied on. The Methods paragraph, generated from your batch, already includes the core ones.
Quantification
[1]
Efficiency-corrected relative quantification of qPCR data using LinRegPCR and a spreadsheet-based workflow. Bio-protocol 16(13):e5729. doi:10.21769/BioProtoc.5729
Louis Müller, co-author, is also Ampliova's founder.
[2]
MIQE 2.0: Revision of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments Guidelines. Clin Chem 71(6):634-651. doi:10.1093/clinchem/hvaf043
[3]
Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res 37(6):e45. doi:10.1093/nar/gkp045
[4]
Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data. BMC Bioinformatics 22:398. doi:10.1186/s12859-021-04306-1
[5]
Analysis of relative gene expression data using real-time quantitative PCR and the 2^−ΔΔCT method. Methods 25(4):402-408. doi:10.1006/meth.2001.1262
[6]
A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29(9):e45. doi:10.1093/nar/29.9.e45
[7]
qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol 8(2):R19. doi:10.1186/gb-2007-8-2-r19
[8]
Correction of RT-qPCR data for genomic DNA-derived signals with ValidPrime. Nucleic Acids Res 40(7):e51. doi:10.1093/nar/gkr1259
[9]
Procedures for detecting outlying observations in samples. Technometrics 11(1):1-21. doi:10.1080/00401706.1969.10490657
Reference genes
[10]
Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3(7):research0034. doi:10.1186/gb-2002-3-7-research0034
[11]
Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 64(15):5245-5250. doi:10.1158/0008-5472.CAN-04-0496
[12]
Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper, Excel-based tool using pair-wise correlations. Biotechnol Lett 26(6):509-515. doi:10.1023/B:BILE.0000019559.84305.47
[13]
Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol 7:33. doi:10.1186/1471-2199-7-33
[14]
miRDeepFinder: a miRNA analysis tool for deep sequencing of plant small RNAs (introduces RefFinder). Plant Mol Biol 80:75-84. doi:10.1007/s11103-012-9885-2
[15]
Expression stability analysis of candidate references for normalization of RT-qPCR data using RefSeeker R package. Bio-protocol 13(17):e4801. doi:10.21769/BioProtoc.4801
Statistics
[16]
The generalization of 'Student's' problem when several different population variances are involved. Biometrika 34(1-2):28-35. doi:10.1093/biomet/34.1-2.28
[17]
On the comparison of several mean values: an alternative approach. Biometrika 38(3-4):330-336. doi:10.1093/biomet/38.3-4.330
[18]
Why psychologists should by default use Welch's t-test instead of Student's t-test. Int Rev Soc Psychol 30(1):92-101. doi:10.5334/irsp.82
[19]
The two-sample t test: pre-testing its assumptions does not pay off. Stat Papers 52:219-231. doi:10.1007/s00362-009-0224-x
[20]
A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc 50(272):1096-1121. doi:10.1080/01621459.1955.10501294
[21]
Extension of multiple range tests to group means with unequal numbers of replications. Biometrics 12(3):307-310. doi:10.2307/3001469
[22]
Pairwise multiple comparisons in the unequal variance case. J Am Stat Assoc 75(372):796-800. doi:10.1080/01621459.1980.10477552
[23]
Multiple comparisons using rank sums. Technometrics 6(3):241-252. doi:10.1080/00401706.1964.10490181
[24]
On methods in the analysis of profile data. Psychometrika 24:95-112. doi:10.1007/BF02289823
[25]
Estimates of location based on rank tests. Ann Math Stat 34(2):598-611. doi:10.1214/aoms/1177704172
[26]
An analysis of variance test for normality (complete samples). Biometrika 52(3-4):591-611. doi:10.1093/biomet/52.3-4.591
[27]
Robust tests for the equality of variances. J Am Stat Assoc 69(346):364-367. doi:10.1080/01621459.1974.10482955
[28]
Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57(1):289-300. doi:10.1111/j.2517-6161.1995.tb02031.x
[29]
The abuse of power: the pervasive fallacy of power calculations for data analysis. Am Stat 55(1):19-24. doi:10.1198/000313001300339897
[30]
Primer of Applied Regression and Analysis of Variance, 2nd edition. McGraw-Hill, New York.
ChIP-qPCR
[31]
Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization. Plant Methods 3:11. doi:10.1186/1746-4811-3-11
Software
[32]
SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17:261-272. doi:10.1038/s41592-019-0686-2