1. Data Analysis for the
miScript miRNA PCR Arrays
Samuel J. Rulli, Ph.D.
miRNA / qPCR Applications Scientist
Samuel.Rulli@QIAGEN.com
The miRNA PCR Arrays and reagents are intended for molecular biology applications. This product
is not intended for the diagnosis, prevention or treatment of disease.
Sample & Assay Technologies
2. Data Analysis for the
miScript miRNA PCR Arrays
Questions, Comments, Concerns?
US Applications Support
888-503-3187
̣
Questions, Comments, Concerns?
Global Applications Support
SABio@qiagen.com
support@sabiosciences.com
The miRNA PCR Arrays and reagents are intended for molecular biology applications. This product
is not intended for the diagnosis, prevention or treatment of disease.
Sample & Assay Technologies
3. Overview of Webinar
I. Brief Technology and Protocol Overview
What a miRNA PCR Array looks like
Simple Protocol
II. Calculating Fold change Values
Baseline and Threshold settings
Exporting and Organizing Data
Experimental Design
Basic Experiment with HKGs
Website Demonstration
Basic Experiment with No HKGs? What do I use?
Serum miRNA Applications
Summary
Pilot Study Promotion/ Re-Order Promotion
.
.
The miRNA PCR Arrays and reagents is intended for molecular biology applications. This product is
not intended for the diagnosis, prevention or treatment of disease.
-3-
Sample & Assay Technologies
5. How miScript miRNA PCR Arrays Work
cDNA Synthesis
universal RT Reaction
1 hours
Load Plates
2 minutes
Export Raw Ct Values
Biologically Relevant Fold Change Values
∆∆ Ct calculations
“mir-103 is up-regulated 5 fold in
experiment versus control”
-5-
Sample & Assay Technologies
6. How miScript miRNA PCR Arrays Work
cDNA Synthesis
universal RT Reaction
1 hours
Load Plates
2 minutes
Export Raw Ct Values
Biologically Relevant Fold Change Values
∆∆ Ct calculations
“mir-103 is up-regulated 5 fold in
experiment versus control”
-6-
Sample & Assay Technologies
7. Overview of Webinar
I. Brief Technology and Protocol Overview
What a miRNA PCR Array looks like
Simple Protocol
II. Calculating Fold change Values
Baseline and Threshold settings
Exporting and Organizing Data
Experimental Design
Basic Experiment with HKGs
Website Demonstration
Basic Experiment with No HKGs? What do I use?
Summary
.
.
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Sample & Assay Technologies
8. Defining Baseline and Threshold
For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Time PCR Instruments*:
Baseline
• Use Automated Baseline
-(if your instrument has Adaptive Baseline function) OR
• Manually Set Baseline
-Using Linear View:
Set to Cycle #2 or #3 up to 1 or 2 cycle values before earliest
amplification (with highest cycle being cycle #15)
.
Threshold Value
• Use Log View
• Place in
1) Linear phase of amplification curve
2) Above background signal, but within lower half to one third of curve
.
Export Ct values to blank spread sheet (Excel).
.
Threshold Must Be Same Between Runs (important for PPC and RTC and
selecting house keeping genes)
)
.
*For Roche LC480: Use Second Derivative Maximum
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Sample & Assay Technologies
9. Defining Baseline and Threshold
For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Time PCR Instruments*:
Baseline
• Use Automated Baseline
-(if your instrument has Adaptive Baseline function) OR
• Manually Set Baseline
-Using Linear View:
Set to Cycle #2 or #3 up to 1 or 2 cycle values before earliest
amplification (with highest cycle being cycle #15)
.
Threshold Value
• Use Log View
• Place in
1) Linear phase of amplification curve
2) Above background signal, but within lower half to one third of curve
.
Export Ct values to blank spread sheet (Excel).
.
Threshold Must Be Same Between Runs (important for PPC and RTC and
selecting house keeping genes)
)
.
*For Roche LC480: Use Second Derivative Maximum
-9-
Sample & Assay Technologies
10. Defining Baseline and Threshold
For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Time PCR Instruments*:
Baseline
• Use Automated Baseline
-(if your instrument has Adaptive Baseline function) OR
• Manually Set Baseline
-Using Linear View:
Set to Cycle #2 or #3 up to 1 or 2 cycle values before earliest
amplification (with highest cycle being cycle #15)
.
Threshold Value
• Use Log View
• Place in
1) Linear phase of amplification curve
2) Above background signal, but within lower half to one third of curve
.
Export Ct values to blank spread sheet (Excel).
.
Threshold Must Be Same Between Runs (important for PPC and RTC and
selecting house keeping genes)
)
.
*For Roche LC480: Use Second Derivative Maximum
- 10 -
Sample & Assay Technologies
11. Defining Baseline and Threshold
For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Time PCR Instruments*:
Baseline
• Use Automated Baseline
-(if your instrument has Adaptive Baseline function) OR
• Manually Set Baseline
-Using Linear View:
Set to Cycle #2 or #3 up to 1 or 2 cycle values before earliest
amplification (with highest cycle being cycle #15)
.
Threshold Value
• Use Log View
• Place in
1) Linear phase of amplification curve
2) Above background signal, but within lower half to one third of curve
.
Export Ct values to blank spread sheet (Excel).
.
Threshold Must Be Same Between Runs (important for PPC and RTC and
selecting house keeping genes)
)
.
*For Roche LC480: Use Second Derivative Maximum
- 11 -
Sample & Assay Technologies
12. Defining Baseline and Threshold
For ABI, Stratagene, Bio-Rad, and Eppendorf Real-Time PCR Instruments*:
Baseline
• Use Automated Baseline
-(if your instrument has Adaptive Baseline function) OR
• Manually Set Baseline
-Using Linear View:
Set to Cycle #2 or #3 up to 1 or 2 cycle values before earliest
amplification (with highest cycle being cycle #15)
.
Threshold Value
• Use Log View
• Place in
1) Linear phase of amplification curve
2) Above background signal, but within lower half to one third of curve
.
Export Ct values to blank spread sheet (Excel).
.
Threshold Must Be Same Between Runs (important for PPC and RTC
and selecting house keeping genes)
*For Roche LC480: Use Second Derivative Maximum
- 12 -
Sample & Assay Technologies
13. Defining Baseline and Threshold
*For Roche LC480: Use Second Derivative Maximum
Baseline
• Use Automated Baseline
-(if your instrument has Adaptive Baseline function) OR
• Manually Set Baseline
-Using Linear View:
Set to Cycle #2 or #3 up to 1 or 2 cycle values before earliest
amplification (with highest cycle being cycle #15)
.
Threshold Value
• Use Log View
• Place in
1) Linear phase of amplification curve
2) Above background signal, but within lower half to one third of curve
.
Export Cp values to blank spread sheet (Excel).
.
Threshold Must Be Same Between Runs (important for PPC and RTC
and selecting house keeping genes)
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Sample & Assay Technologies
19. Setting Threshold
Use the Same Threshold for All PCR Arrays
Threshold Line
C(t)
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Sample & Assay Technologies
20. 2 Ways to “CRUNCH” the Data
Excel Based Templates
•Free!
•Download from http://www.sabiosciences.com/mirnaArrayDataAnalysis.php
•Good for 2 Group Comparisons (Control + Experimental)
•10 PCR Arrays per Group
Web-Based Data Analysis
•Free!
•Upload Excel spreadsheet at
•http://pcrdataanalysis.sabiosciences.com/mirna/arrayanalysis.php
•Good for 11 Group Comparisons (Control + 10 Experimental)
•255 PCR Arrays Total
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Sample & Assay Technologies
21. 2 Ways to “CRUNCH” the Data
Excel Based Templates
•Free!
•Download from http://www.sabiosciences.com/mirnaArrayDataAnalysis.php
•Good for 2 Group Comparisons (Control + Experimental)
•10 PCR Arrays per Group
Web-Based Data Analysis
•Free!
•Upload Excel spreadsheet at http://www.sabiosciences.com/pcr/arrayanalysis.php
•Good for 11 Group Comparisons (Control + 10 Experimental)
•255 PCR Arrays Total
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Sample & Assay Technologies
22. 2 Ways to “CRUNCH” the Data
Excel Based Templates
•Free!
•Download from http://www.sabiosciences.com/mirnaArrayDataAnalysis.php
•Good for 2 Group Comparisons (Control + Experimental)
•10 PCR Arrays per Group
Web-Based Data Analysis
•Free!
•Upload Excel spreadsheet at
http://pcrdataanalysis.sabiosciences.com/mirna/arrayanalysis.php
•Good for 11 Group Comparisons (Control + 10 Experimental)
•255 PCR Arrays Total
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Sample & Assay Technologies
23. Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values
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Sample & Assay Technologies
24. Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values
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Sample & Assay Technologies
25. Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values
- 25 -
Sample & Assay Technologies
26. Organizing Raw C(t) values
Download Excel Template from SABiosciences’ Web Portal…or make your own.
Cataloged Array
Row 1
Sample Name
Column A:
Well Location
Column B-??:
Raw C(t) Values
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Sample & Assay Technologies
27. Experiment miRNA expression Profiling during differentiation
Osteogenesis – Day 16
T4
T3
T2
hMSC
T1
Neurogenesis – 72 hr
T1
T2
T3
T4
Differentiation protocol
Collect miRNA at different time points
Repeat experiment 3x (biological replicates)
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Sample & Assay Technologies
28. Experiment miRNA expression Profiling during differentiation
Osteogenesis – Day 16
T4
T2
hMSC
T3
T1
Differentiation protocol
Collect miRNA at different time points
Repeat experiment 3x (biological replicates)
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Sample & Assay Technologies
29. Our Experiment-Data Analysis Overview
Control
A
B
hMSCs
Group 1
C
A
B
Group 2
C
Time Point 1
A
B
Group 3
C
Time Point 2
A
B
C
Time Point 3
3 biological replicates that will be grouped into (3 groups + control)
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Sample & Assay Technologies
30. Our Experiment-Data Analysis Overview
Control
A
A
Group 1
Group 2
B
C
A
B
C
A
B
C
A
B
C
A
B
B
Group 3
A
C
C
B
A
B
C
C
1 PCR Array for Each Sample
- 30 -
Sample & Assay Technologies
31. Our Experiment-Data Analysis Overview
Control
Group 1
Group 2
B
A
C
A
B
C
A
B
A
C
A
B
C
A
B
B
Group 3
A
C
C
B
A
B
C
C
∆C(t)
C(t)GOI- C(t)HKG
1.
Calculate ∆ C(t) for on each array for each GOI (Gene Of Interest)
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Sample & Assay Technologies
32. Our Experiment-Data Analysis Overview
Control
Group 1
Group 2
B
A
∆C(t)
C
A
B
C
A
B
A
C
A
B
C
A
∆C(t)
∆C(t)
∆C(t)+∆C(t)+∆C(t)
3
1.
2.
∆C(t)
∆C(t)
∆C(t)
∆C(t)
B
Group 3
B
∆C(t)
∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3
A
C
C
∆C(t)
B
A
B
∆C(t)
C
C
∆C(t) ∆C(t)
∆C(t)+∆C(t)+∆C(t)
3
Calculate ∆ C(t) for on each array for each GOI (Gene Of Interest)
Calculate Average ∆ C(t) for each gene within a Group
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Sample & Assay Technologies
33. Our Experiment-Data Analysis Overview
Control
A
B
∆C(t)
Group 1
C
∆C(t)
∆C(t)
A
∆C(t)
B
∆C(t)
Group 2
A
C
∆C(t)
∆C(t)
B
Group 3
C
∆C(t)
∆C(t)
A
∆C(t)
B
C
∆C(t) ∆C(t)
∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3
1.
2.
3.
Calculate ∆ C(t) for on each array for each GOI (Gene Of Interest)
Calculate Average ∆ C(t) for each gene within a Group
Calculate ∆∆ C(t) for each gene between Groups
- 33 -
Sample & Assay Technologies
34. Our Experiment-Data Analysis Overview
Control
A
B
∆C(t)
Group 1
C
∆C(t)
∆C(t)
A
∆C(t)
B
∆C(t)
Group 2
A
C
∆C(t)
∆C(t)
B
Group 3
C
∆C(t)
∆C(t)
A
∆C(t)
B
C
∆C(t) ∆C(t)
∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3
∆C(t)+∆C(t)+∆C(t)
3
1.
2.
3.
4.
Calculate ∆ C(t) for on each array for each GOI (Gene Of Interest)
Calculate Average ∆ C(t) for each gene within a Group
Calculate ∆∆ C(t) for each gene between Groups
∆∆Ct)
∆∆
Calculate Fold Change: 2(-∆∆
- 34 -
Sample & Assay Technologies
36. What are HKGs? Why do I need them? (Do I need them?)
House-keeping genes or Normalization genes:
Expressed in all samples and co-purify with miRNA fraction
Not changing expression levels due to disease or experimental conditions
Used to normalize for amount of sample and RT efficiency
6 small non-coding RNAs included on each array as “HKGs”
Ex: SNORD 61, SNORD 68, SNORD 72 , SNORD 95 , SNORD 96A, RNU6-2
Any other miRNA or assay on the miRNA Array can be a normalization gene
Use 1 HKG or an average of the most stable HKGs
.
Identification of stable HKGs
Prior experience / data from publication
Start with same amount of sample (RNA) and assume equal RT efficiency (actually
can measure this with miRNA RTC)
Pair-wise comparison (delta- Ct) between genes and assume genes are not changing
expression levels in the same direction.
- 36 -
Sample & Assay Technologies
37. Special Cases: Alternative ways to find HKGs
Pair wise Comparison:
Ct (HKG1)
22
23
26
Ct (HKG2)
18
19
22
Delta Ct
4
4
4
Useful if:
starting with different amounts of sample
using different threshold setting on machine or different machines
have different RT efficiencies
- 37 -
Sample & Assay Technologies
38. Special Cases: Serum Analysis or No HKGs
Isolation of miRNAs from Serum creates problems in normalization
No universal endogenous HKGs
What is a good normalization strategy?
None (normalize to volume)
snRNA or miRNA in samples
All (global normalization)
spike in control
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Sample & Assay Technologies
40. Serum miRNA Profiling
Human Serum miScript miRNA PCR Array (MIHS-106Z)
.
Profile the expression of mature miRNA sequences that researchers have
detected in serum and other bodily fluids
Includes miRNAs found to be present at higher levels in serum from
individuals with specific diseases
Heart and liver injury or disease, atherosclerosis, diabetes, and a number of
organ-specific cancers
What is on the array?
84 mature miRNA sequences
miRNA housekeeping gene assays
Reverse Transcription Control assays
PCR Control assays
RNA Recovery Control assays
– Works with separately purchased Syn-cel-miR-39 miScript miRNA Mimic
(MSY0000010) spiked into the sample before nucleic acid preparation to monitor
biological fluid miRNA recovery rates
The miRNA PCR Arrays and reagents is intended for molecular biology applications. This product is
not intended for the diagnosis, prevention or treatment of disease.
- 40 -
Sample & Assay Technologies
41. Expression Profiling of Normal Human Serum Samples
Two “normal” human serum samples (Sample A and Sample B)
.
Total RNA was isolated using the miRNeasy Mini Kit
QIAGEN Supplementary Protocol for total RNA purification from serum or plasma
Optional syn-cel-miR-39 spike-in control included
5 µl of each RNA elution was used in an miScript miRNA First Strand Kit
reverse transcription reaction
Mature miRNA expression was profiled using the Human Serum miScript
miRNA PCR Array (MIHS-106Z)
Non-normalized Ct values are highly comparable
How should the data be normalized to uncover fine
differences between the two samples?
Raw Ct: Serum Sample B
40
35
30
25
20
2
R = 0.9079
15
15
20
25
30
35
40
Raw Ct: Serum Sample A
- 41 -
Sample & Assay Technologies
42. Expression Profiling of Normal Human Serum Samples
Two normal human serum samples (Sample A and Sample B)
40
.
R C S
aw t: erumS ple B
am
Total RNA was isolated using the miRNeasy Mini Kit
QIAGEN Supplementary Protocol for total RNA purification from serum or plasma
Option
35 syn-cel-miR-39 spike-in control included
5 µl of each RNA elution was used in an miScript miRNA First Strand Kit
reverse transcription reaction
Mature miRNA expression was profiled using the Human Serum miScript
30
miRNA PCR Array (MAH-106)
40
Non-normalized Ct values are highly comparable
How should the data be normalized to uncover fine
differences between the two samples?
20
Raw Ct: Serum Sample B
25
35
30
2
R 25 0.9079
=
15
20
2
15
20
25
30
15
Raw Ct: Serum Sample 15
A
- 42 -
40
35
20
25
30
R = 0.9079
35
40
Raw Ct: Serum Sample A
Sample & Assay Technologies
43. Expression Profiling of Normal Human Serum Samples
Two normal human serum samples (Sample assumes:
RAW Ct or volume normalization A and Sample B)
40
Total RNA was isolated using the miRNeasy Mini Kit
Same isolation efficiency
QIAGENSame RT efficiency total RNA purification from serum or plasma
Supplementary Protocol for
Option Same baseline and threshold
35 syn-cel-miR-39 spike-in control included settings
5 µl of each Same volumetric constraints miRNA First Strand Kit
RNA elution was used in an miScript
R C S
aw t: erumS ple B
am
.
reverse transcription reaction
Mature miRNA expression was profiled using the Human Serum miScript
30
miRNA PCR Array (MAH-106)
40
Non-normalized Ct values are highly comparable
How should the data be normalized to uncover fine
differences between the two samples?
20
Raw Ct: Serum Sample B
25
35
30
2
R 25 0.9079
=
15
20
2
15
20
25
30
15
Raw Ct: Serum Sample 15
A
- 43 -
40
35
20
25
30
R = 0.9079
35
40
Raw Ct: Serum Sample A
Sample & Assay Technologies
44. Serum Sample Data Normalization
Step 1: Check reverse transcription control (miRTC) and PCR control (PPC) Ct values
.
Position
Control
Ct: Sample A
Ct: Sample B
H09
miRTC
18.76
18.52
H10
miRTC
18.73
18.64
H11
PPC
19.43
19.61
H12
PPC
19.61
19.76
As determined by the raw Ct values, the reverse transcription and PCR
efficiency of both samples are highly comparable
Ct values differ by less than 0.25 units
- 44 -
Sample & Assay Technologies
45. Serum Sample Data Normalization (cont.)
Step 2: Observe housekeeping gene Ct values
.
Position
Gene
Ct: Sample
A
H05
SNORD72
31.81
32.79
H06
SNORD95
35.00
35.00
H07
SNORD96A
35.00
35.00
H08
RNU6-2
35.00
35.00
Ct: Sample B
Housekeeping genes are either not expressed or exhibit borderline
detectable expression
As is often found with serum samples, standard housekeeping genes cannot be
used for data normalization
How should you proceed?
- 45 -
Sample & Assay Technologies
46. Serum Sample Data Normalization (cont.)
Four potential data normalization options
1.
Normalize data of each plate to its RNA Recovery Control
Assays (wells H02 to H04)
Can only be used if Syn-cel-miR-39 miScript miRNA Mimic
(MSY0000010) was spiked into the sample before nucleic acid
preparation
2.
Normalize data to Ct mean of all expressed targets (targets
with Ct < 35) for a given plate
3.
Normalize data to Ct mean of targets that are commonly
expressed in the two samples of interest
4.
Normalize data to ‘0’
Essentially you are relying on the consistency in the quantity and
quality of your original RT input
- 46 -
Sample & Assay Technologies
47. Serum Sample Data Normalization (cont.)
Option 1: Normalize to RNA Recover Control Assays
Calculate the average Ct of the cel-miR-39 wells (H02 to H04)
Position
Control
Ct: Sample A
Ct: Sample B
H02
cel-miR-39
17.84
19.37
H03
cel-miR-39
17.85
19.49
H04
cel-miR-39
17.85
19.39
Sample A: 17.85
Sample B: 19.42
Using these cel-miR-39 Ct means as normalizers, calculate ∆∆Ct values,
fold-change, and fold up/down regulation
Fold-Regulation (B to A)
100
80
60
40
20
0
-20
-40
- 47 -
Sample & Assay Technologies
48. Serum Sample Data Normalization (cont.)
Option 2: Normalize to Ct Mean of All Expressed Targets for a given plate
Determined the number of expressed targets in each plate (Ct < 35)
Sample A: 66
Sample B: 59
Calculate the Ct Mean of the expressed targets
Sample A: 28.96
Sample B: 29.70
Using these Ct means as normalizers, calculate ∆∆Ct values, fold-change,
and fold up/down regulation
NOTE: same strongly up-regulated and down-regulated miRNAs are identified
Fold-Regulation (B to A)
60
40
20
0
-20
-40
-60
- 48 -
Sample & Assay Technologies
49. Serum Sample Data Normalization (cont.)
Option 3: Normalize to Ct Mean of Commonly Expressed Targets
Determined the number of commonly expressed targets for the plates being
analyzed (Ct < 35 in all samples)
Commonly expressed in Sample A and Sample B: 48
Calculate the associated Ct Mean
Sample A: 27.52
Sample B: 28.86
Using these Ct means as normalizers, calculate ∆∆Ct values, fold-change,
and fold up/down regulation
NOTE: same strongly up-regulated and down-regulated miRNAs are identified
Fold-Regulation (B to A)
80
60
40
20
0
-20
-40
- 49 -
Sample & Assay Technologies
50. Serum Sample Data Normalization (cont.)
Option 4: Normalize to ‘0’
Normalizing to ‘0’ relies on the consistency in the quantity and quality of
your original RT input
For serum samples, this may not be the best option, as the RNA is not routinely
quantified prior to addition to a reverse transcription reaction
Normalizing the data to ‘0’, calculate ∆∆Ct values, fold-change, and fold
up/down regulation
Fold-Regulation (B to A)
40
20
0
-20
-40
-60
-80
-100
NOTE: These results are not completely comparable to the results achieved
with the other three normalization methods. The same strongly up-regulated
and down-regulated miRNAs are identified; however, additionally up- and downregulated genes are potentially (incorrectly) identified. This suggests that there
is the need for some method of normalization, other than just normalizing to ‘0’.
- 50 -
Sample & Assay Technologies
51. miRNA Data Analysis Summary
When setting baseline and theshold with your qPCR Instrument:
ABI, Stratagene, BioRad, Eppendorf
•Automatic Baseline
•Threshold in lower ½ to lower 1/3 of curves (PPC = 18 to 22)
Roche LC480:
•Second derivative maximum
Export and Collect Raw Ct values. Organize for upload
Organize experiment:
Group Biological/Technical replicates
Focus on Sample and Experimental Quality
RTCs; PPCs; Spike in Control (if applicable)
Select MOST STABLE HKGs for your experiment
Click through Fold Change Data, Export Results, Publish
- 51 -
Sample & Assay Technologies