Collection of functions to expedite qPCR analysis. See image below for workflow options.
# install.packages("devtools")
devtools::install_github("jwvillain/quickPCR")# Load libraries
library(quickPCR)
library(ggplot2)
#Import data:
data<-read.table('qPCR data2.txt', na.strings = "",fill = TRUE,header = T)
conditionKey<-read.csv('conditionKey2.csv')
#Initial data processing
processedData<-quickProcess(data_df = data,
normalizer_char = "RPS9", #name of your normalizer
sampleName_num = 2, #column number with your sample name information in data_df
targetGene_num = 3, #column number with your target gene name information in data_df
CT_num = 7, #column number with your CT information in data_df
conditionKey_df = conditionKey)Input data
#view data:
#Note: imported data can be formated in different ways. Minimium for the imported data is three columns defining the 1) sample names 2) gene names 3) CT values.
head(data)
Well Sample_Name Target_Name Task Reporter Quencher CT
1 B4 S10 RPS9 UNKNOWN VIC NFQ-MGB 23.47855
2 B4 S10 LGR5 UNKNOWN FAM NFQ-MGB 34.50635
3 B5 S10 RPS9 UNKNOWN VIC NFQ-MGB 23.50337
4 B5 S10 LGR5 UNKNOWN FAM NFQ-MGB 34.58119
5 B6 S10 RPS9 UNKNOWN VIC NFQ-MGB 23.52603
6 B6 S10 LGR5 UNKNOWN FAM NFQ-MGB 34.78944
#Note: 'conditionKey' must have the sample name in the first column and condition in the second. Sample names must match those in the 'data' dataframe
head(conditionKey)
Sample_name Condition
1 S32 0.5 ng/mL Drug
2 S33 0.5 ng/mL Drug
3 S34 0.5 ng/mL Drug
4 S35 0.5 ng/mL Drug
5 S36 0.5 ng/mL Drug
6 S37 1 ng/mL DrugExpected output
For the normalizer gene, an "NA" value will appear for delta CT (dCT) and 2^-dCT across all samples. Additionally, this tool will only support up to 3 technical replicates (CT1, CT2, CT3) for analysis. If fewer than three replicates are used, an "NA" value will be placed in that cell of the dataframe as shown below.
Non-numeric values, which usually result from lowly expressed genes where the instrument labels those wells "undetermined," are replaced with a numeric value of 40. This replacement value can be adjusted by specifying "undetermined_num = X" when running quickProcess(). X is the numeric values you want to replace non-numeric values with (for example, can set to 50).
head(processedData)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741
3 S10 Control MKI67 26.74216 26.89423 26.73264 26.78967 0.09066912 3.287024 0.1024488589
4 S10 Control OLFM4 27.40610 27.65227 27.60735 27.55524 0.13109659 4.052591 0.0602626812
5 S10 Control CDH1 25.07976 24.87838 NA 24.97907 0.14239835 1.476416 0.3593804231
1 S10 Control RPS9 23.47855 23.50337 23.52603 23.50265 0.02374695 NA NA
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657Calculate RQVs
RQV_noNormalize<-quickRQV(data_df = processedData,
control_char = "Control", #Character specifying the control condition
RQV_input_num = 10) #Numeric specifying the column with the 2^-dCT valuesExpected output
Average RQV for the control condition should equal 1 for each gene.
head(RQV_noNormalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT RQV
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.3729184
3 S10 Control MKI67 26.74216 26.89423 26.73264 26.78967 0.09066912 3.287024 0.1024488589 0.6623044
4 S10 Control OLFM4 27.40610 27.65227 27.60735 27.55524 0.13109659 4.052591 0.0602626812 0.4378348
5 S10 Control CDH1 25.07976 24.87838 NA 24.97907 0.14239835 1.476416 0.3593804231 0.6999969
1 S10 Control RPS9 23.47855 23.50337 23.52603 23.50265 0.02374695 NA NA NA
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.2178802Calculate p-values using RQVs. Average RQV for the control condition should equal 1 for each gene.
signif<-quickSignif(data_df = RQV_noNormalize,
reference_condition_char = "Control", #Character specifying the condition you want to compare to
test_char = "wilcox", #Character specifying statistical test (wilcox or ttest)
data_input_num = 11) #Numeric with the values you are using to calculate p-valuesExpected output
Reminder that your control condition used to calculate RQVs will have an average value of 1.
head(signif)
Condition1 Condition2 Gene_Target Average_Condition1 SD_Condition1 Average_Condition2 SD_Condition2 wilcox_pvalue
1 4 ng/mL Drug Control LGR5 5.0780174 3.40115028 1 1.0790082 0.01904762
2 4 ng/mL Drug Control MKI67 0.7719667 0.47690271 1 0.2597114 0.25714286
3 4 ng/mL Drug Control OLFM4 0.7404012 0.50978977 1 0.7748819 0.60952381
4 4 ng/mL Drug Control CDH1 1.4315459 0.51006291 1 0.4228664 0.35238095
5 0.5 ng/mL Drug Control LGR5 0.4901223 0.36141733 1 1.0790082 0.73015873
6 0.5 ng/mL Drug Control MKI67 0.7035484 0.25707187 1 0.2597114 0.11111111
Calculate z-score values using RQVs
ZScore_RQV_noNormalize<-quickZScore(data_df = RQV_noNormalize,
data_input_num = 11) #Numeric with the values you are using to calculate Z-scoresExpected output
head(ZScore_RQV_noNormalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT RQV Z.Score
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.3729184 -0.6160301
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.2178802 -0.6780490
12 S12 Control LGR5 33.43422 32.91566 33.26767 33.20585 0.26474682 9.951401 0.0010100198 0.8400462 -0.4291682
17 S13 4 ng/mL Drug LGR5 29.03057 29.07294 29.10276 29.06876 0.03627536 6.349655 0.0122620626 10.1985120 3.3144346
21 S14 4 ng/mL Drug LGR5 30.24761 30.57711 30.56170 30.46214 0.18594941 7.995673 0.0039179838 3.2586365 0.5383239
26 S15 4 ng/mL Drug LGR5 32.18607 31.83747 32.03858 32.02070 0.17498527 9.590382 0.0012972016 1.0788989 -0.3336216
Generate plot of the RQV values to get a quick view of the data. Use "?quickPlot" to see more information on how to customize plots.
qPCR_plot<-quickPlot(data_df = subset(RQV_noNormalize, RQV_noNormalize[,3] == "LGR5"),
input_num = 11, #Numeric with the values you are wanting to graph
control_char = "Control") #Character specifying the control condition
ggsave("RQV_noNormalize.pdf",
plot = qPCR_plot,
units = 'in',
width = 8,
height = 8)
Expected output
Need to subset to specify one gene to plot. Red dotted line marks the average of the specified control condition.
Calculate AUs by multiplying 2^-dCT by 1000.
AU_noNormalize<-quickAU(data_df = processedData,
AU_input_num = 10) #Numeric specifying the column with the 2^-dCT valuesExpected output
head(AU_noNormalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT AU
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.4483741
3 S10 Control MKI67 26.74216 26.89423 26.73264 26.78967 0.09066912 3.287024 0.1024488589 102.4488589
4 S10 Control OLFM4 27.40610 27.65227 27.60735 27.55524 0.13109659 4.052591 0.0602626812 60.2626812
5 S10 Control CDH1 25.07976 24.87838 NA 24.97907 0.14239835 1.476416 0.3593804231 359.3804231
1 S10 Control RPS9 23.47855 23.50337 23.52603 23.50265 0.02374695 NA NA NA
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.2619657Calculate p-values using AUs.
signif<-quickSignif(data_df = AU_noNormalize,
reference_condition_char = "Control", #Character specifying the condition you want to compare to
test_char = "wilcox", #Character specifying statistical test (wilcox or ttest)
data_input_num = 11) #Numeric specifying the column with data you want to use to calculate significanceExpected output
head(signif)
Condition1 Condition2 Gene_Target Average_Condition1 SD_Condition1 Average_Condition2 SD_Condition2 wilcox_pvalue
1 4 ng/mL Drug Control LGR5 6.1054954 4.0893336 1.202338 1.297333 0.01904762
2 4 ng/mL Drug Control OLFM4 101.9072854 70.1664114 137.637934 106.653147 0.60952381
3 4 ng/mL Drug Control MKI67 119.4120278 73.7699168 154.685463 40.173571 0.25714286
4 4 ng/mL Drug Control CDH1 734.9598500 261.8677806 513.402909 217.100838 0.35238095
5 0.5 ng/mL Drug Control LGR5 0.5892929 0.4345459 1.202338 1.297333 0.73015873
6 0.5 ng/mL Drug Control MKI67 108.8287045 39.7652814 154.685463 40.173571 0.11111111
Calculate z-score values using AUs
ZScore_AU_noNormalize<-quickZScore(data_df = AU_noNormalize,
data_input_num = 11) #Numeric specifying the column with data you want to use to calculate Z-score valuesExpected output
head(ZScore_AU_noNormalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT AU Z.Score
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.4483741 -0.6160301
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.2619657 -0.6780490
12 S12 Control LGR5 33.43422 32.91566 33.26767 33.20585 0.26474682 9.951401 0.0010100198 1.0100198 -0.4291682
17 S13 4 ng/mL Drug LGR5 29.03057 29.07294 29.10276 29.06876 0.03627536 6.349655 0.0122620626 12.2620626 3.3144346
21 S14 4 ng/mL Drug LGR5 30.24761 30.57711 30.56170 30.46214 0.18594941 7.995673 0.0039179838 3.9179838 0.5383239
26 S15 4 ng/mL Drug LGR5 32.18607 31.83747 32.03858 32.02070 0.17498527 9.590382 0.0012972016 1.2972016 -0.3336216
>
Generate plot of the AU values to get a quick view of the data. Use "?quickPlot" to see more information on how to customize plots.
qPCR_plot2<-quickPlot(data_df = subset(ZScore_AU_noNormalize, ZScore_AU_noNormalize[,3] == "LGR5"),
input_num = 11, #Numeric specifying the column with data you want to use to generate your plot
control_char = "Control") #Character specifying the control condition
ggsave("AU_noNormalize.pdf",
plot = qPCR_plot2,
units = 'in',
width = 8,
height = 8)
Expected output
Need to subset to specify one gene to plot. Red dotted line marks the average of the specified control condition.
For certain datasets, you may need to normalize your 2^-dCT values to another gene. If you are looking at changes in a cell population within a heterogenous tissue (like primary intestinal tissue), you may need to normalize to a another gene from your qPCR data. For example, if looking at changes in LGR5 stem cells from the epithelium, you may want to normalize 2^-dCT values to an epithelial marker (CDH1).
Normalize your 2^-dCT values to another gene from your qPCR data.
normalized<-quickNormalize(data_df = processedData,
normalizer2_char = "CDH1", #character value specifying which gene you want to normalize your 2^-dCT values to
twoToNeg_dCT_num = 10) #Numeric specifying the column with the data you want to processExpected output
head(normalized)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT CDH1_Normalized
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.0012476309
3 S10 Control MKI67 26.74216 26.89423 26.73264 26.78967 0.09066912 3.287024 0.1024488589 0.2850707837
4 S10 Control OLFM4 27.40610 27.65227 27.60735 27.55524 0.13109659 4.052591 0.0602626812 0.1676849304
5 S10 Control CDH1 25.07976 24.87838 NA 24.97907 0.14239835 1.476416 0.3593804231 1.0000000000
1 S10 Control RPS9 23.47855 23.50337 23.52603 23.50265 0.02374695 NA NA NA
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.0008374836
Calculate RQVs
RQV_Normalize<-quickRQV(data_df = normalized,
control_char = "Citric Acid Control",
RQV_input_num = 11)Expected output
Average RQV for the control condition should equal 1 for each gene. For the gene you normalized to in the previous step (in this case CDH1), the normalized values will be 1 across all your samples.
head(RQV_Normalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT CDH1_Normalized RQV
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.0012476309 0.6452716
3 S10 Control MKI67 26.74216 26.89423 26.73264 26.78967 0.09066912 3.287024 0.1024488589 0.2850707837 0.8673992
4 S10 Control OLFM4 27.40610 27.65227 27.60735 27.55524 0.13109659 4.052591 0.0602626812 0.1676849304 0.6668514
5 S10 Control CDH1 25.07976 24.87838 NA 24.97907 0.14239835 1.476416 0.3593804231 1.0000000000 1.0000000
1 S10 Control RPS9 23.47855 23.50337 23.52603 23.50265 0.02374695 NA NA NA NA
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.0008374836 0.4331445Calculate p-values using the gene normalized RQVs.
signif<-quickSignif(data_df = RQV_Normalize,
reference_condition_char = "Control", #Character specifying the condition you want to compare to
test_char = "wilcox", #Character specifying statistical test (wilcox or ttest)
data_input_num = 12) #Numeric specifying the column with data you want to use to calculate significanceExpected output
When running this step after quickNormalize(), quickSignif() will automatically identify which gene you normalized to and remove it from the output.
head(signif)
Condition1 Condition2 Gene_Target Average_Condition1 SD_Condition1 Average_Condition2 SD_Condition2 wilcox_pvalue
1 4 ng/mL Drug Control LGR5 5.3606959 4.3643295 1 0.7272935 0.06666667
2 4 ng/mL Drug Control MKI67 0.5920915 0.4730187 1 0.3823081 0.06666667
3 4 ng/mL Drug Control OLFM4 0.6421271 0.4567213 1 0.3962562 0.35238095
4 0.5 ng/mL Drug Control LGR5 0.7813548 0.6553367 1 0.7272935 0.55555556
5 0.5 ng/mL Drug Control MKI67 0.8176996 0.3555748 1 0.3823081 0.73015873
6 0.5 ng/mL Drug Control OLFM4 1.2395298 0.6083415 1 0.3962562 0.55555556
Calculate z-score values using the RQVs from the normalized 2^-dCT values.
ZScore_RQV_Normalize<-quickZScore(data_df = RQV_Normalize,
data_input_num = 12) #Numeric specifying the column with data you want to use to calculate Z-score valuesExpected output
When running this step after quickNormalize(), quickZScore() will automatically identify which gene you normalized to and remove it from the output.
head(ZScore_RQV_Normalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT CDH1_Normalized RQV Z.Score
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.0012476309 0.6452716 -0.5918466
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.0008374836 0.4331445 -0.6639490
12 S12 Control LGR5 33.43422 32.91566 33.26767 33.20585 0.26474682 9.951401 0.0010100198 0.0016683323 0.8628574 -0.5178888
17 S13 4 ng/mL Drug LGR5 29.03057 29.07294 29.10276 29.06876 0.03627536 6.349655 0.0122620626 0.0205502522 10.6285396 2.8014840
21 S14 4 ng/mL Drug LGR5 30.24761 30.57711 30.56170 30.46214 0.18594941 7.995673 0.0039179838 0.0034887935 1.8043954 -0.1978583
26 S15 4 ng/mL Drug LGR5 32.18607 31.83747 32.03858 32.02070 0.17498527 9.590382 0.0012972016 0.0013215090 0.6834812 -0.5788591
Generate plot of the gene normalized RQV values to get a quick view of the data. Use "?quickPlot" to see more information on how to customize plots.
qPCR_plot3<-quickPlot(data_df = subset(RQV_Normalize, RQV_Normalize[,3] == "LGR5"),
input_num = 12, #Numeric specifying the column with data you want to use to generate your plot
control_char = "Control") #Character specifying the control condition
ggsave("RQV_Normalize.png",
plot = qPCR_plot3,
units = 'in',
width = 8,
height = 8)
Expected output
Need to subset to specify one gene to plot. Red dotted line marks the average of the specified control condition.
For certain datasets, you may need to normalize your 2^-dCT values to another gene. If you are looking at changes in a cell population within a heterogenous tissue (like primary intestinal tissue), you may need to normalize to a another gene from your qPCR data. For example, if looking at changes in LGR5 stem cells from the epithelium, you may want to normalize 2^-dCT values to an epithelial marker (CDH1).
Normalize your 2^-dCT values to another gene from your qPCR data.
normalized<-quickNormalize(data_df = processedData,
normalizer2_char = "CDH1", #character value specifying which gene you want to normalize your 2^-dCT values to
twoToNeg_dCT_num = 10) #Numeric specifying the column with the data you want to processExpected output
head(normalized)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT CDH1_Normalized
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.0012476309
3 S10 Control MKI67 26.74216 26.89423 26.73264 26.78967 0.09066912 3.287024 0.1024488589 0.2850707837
4 S10 Control OLFM4 27.40610 27.65227 27.60735 27.55524 0.13109659 4.052591 0.0602626812 0.1676849304
5 S10 Control CDH1 25.07976 24.87838 NA 24.97907 0.14239835 1.476416 0.3593804231 1.0000000000
1 S10 Control RPS9 23.47855 23.50337 23.52603 23.50265 0.02374695 NA NA NA
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.0008374836
Calculate AUs by multiplying normalized 2^-dCT by 1000.
AU_Normalize<-quickAU(data_df = normalized,
AU_input_num = 11) #Numeric specifying the column with the 2^-dCT valuesExpected output
For the gene you normalized to in the previous step (in this case CDH1), the normalized AU values will be 1000 across all your samples.
head(AU_Normalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT CDH1_Normalized AU
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.0012476309 1.2476309
3 S10 Control MKI67 26.74216 26.89423 26.73264 26.78967 0.09066912 3.287024 0.1024488589 0.2850707837 285.0707837
4 S10 Control OLFM4 27.40610 27.65227 27.60735 27.55524 0.13109659 4.052591 0.0602626812 0.1676849304 167.6849304
5 S10 Control CDH1 25.07976 24.87838 NA 24.97907 0.14239835 1.476416 0.3593804231 1.0000000000 1000.0000000
1 S10 Control RPS9 23.47855 23.50337 23.52603 23.50265 0.02374695 NA NA NA NA
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.0008374836 0.8374836Calculate p-values using the gene normalized AUs.
signif<-quickSignif(data_df = AU_Normalize,
reference_condition_char = "Control", #Character specifying the condition you want to compare to
test_char = "wilcox", #Character specifying statistical test (wilcox or ttest)
data_input_num = 12) #Numeric specifying the column with data you want to use to calculate significanceExpected output
When running this step after quickNormalize(), quickSignif() will automatically identify which gene you normalized to and remove it from the output.
head(signif)
Condition1 Condition2 Gene_Target Average_Condition1 SD_Condition1 Average_Condition2 SD_Condition2 wilcox_pvalue
1 4 ng/mL Drug Control LGR5 10.364891 8.438419 1.933497 1.40622 0.06666667
2 4 ng/mL Drug Control OLFM4 161.467824 114.846096 251.457721 99.64168 0.35238095
3 4 ng/mL Drug Control MKI67 194.590895 155.457605 328.650046 125.64557 0.06666667
4 0.5 ng/mL Drug Control LGR5 1.510747 1.267092 1.933497 1.40622 0.55555556
5 0.5 ng/mL Drug Control MKI67 268.737023 116.859672 328.650046 125.64557 0.73015873
6 0.5 ng/mL Drug Control OLFM4 311.689348 152.972156 251.457721 99.64168 0.55555556
Calculate z-score values using the AUs from the normalized 2^-dCT values.
ZScore_RQV_Normalize<-quickZScore(data_df = AU_Normalize,
data_input_num = 12) #Numeric specifying the column with data you want to use to calculate Z-score valuesExpected output
When running this step after quickNormalize(), quickZScore() will automatically identify which gene you normalized to and remove it from the output.
head(ZScore_AU_Normalize)
Sample_Name Condition Target_Gene CT1 CT2 CT3 Average_CT Standard_Deviation dCT twoToNeg_dCT CDH1_Normalized AU Z.Score
2 S10 Control LGR5 34.50635 34.58119 34.78944 34.62566 0.14668779 11.123009 0.0004483741 0.0012476309 1.2476309 -0.5918466
7 S11 Control LGR5 34.75470 35.09823 34.24502 34.69932 0.42929086 11.898335 0.0002619657 0.0008374836 0.8374836 -0.6639490
12 S12 Control LGR5 33.43422 32.91566 33.26767 33.20585 0.26474682 9.951401 0.0010100198 0.0016683323 1.6683323 -0.5178888
17 S13 4 ng/mL Drug LGR5 29.03057 29.07294 29.10276 29.06876 0.03627536 6.349655 0.0122620626 0.0205502522 20.5502522 2.8014840
21 S14 4 ng/mL Drug LGR5 30.24761 30.57711 30.56170 30.46214 0.18594941 7.995673 0.0039179838 0.0034887935 3.4887935 -0.1978583
26 S15 4 ng/mL Drug LGR5 32.18607 31.83747 32.03858 32.02070 0.17498527 9.590382 0.0012972016 0.0013215090 1.3215090 -0.5788591Generate plot of the gene normalized RQV values to get a quick view of the data. Use "?quickPlot" to see more information on how to customize plots.
qPCR_plot4<-quickPlot(data_df = subset(AU_Normalize, AU_Normalize[,3] == "LGR5"),
input_num = 12, #Numeric specifying the column with data you want to use to generate your plot
control_char = "Control") #Character specifying the control condition
ggsave("AU_Normalize.png",
plot = qPCR_plot4,
units = 'in',
width = 8,
height = 8)
Expected output
Need to subset to specify one gene to plot. Red dotted line marks the average of the specified control condition.
Customize your plots using quickPlot()
qPCR_plot4_custom<-quickPlot(data_df = subset(AU_Normalize, AU_Normalize[,3] == "LGR5"),
input_num = 12,
control_char = "Control",
conditionOrder_vec = c("Control","0.5 ng/mL Drug","1 ng/mL Drug","2 ng/mL Drug","4 ng/mL Drug"), #character vector specifying the order in which you want your conditions to appear along the x-axis
ymin_num = 0, #Numeric value specifying the minimum you want along the y-axis
ymax_num = 25, #Numeric value specifying the maximium you want along the y-axis
dotSize_num = 4, #Numeric used to determine the dot size
xTitle_char = "Treatment", #Character value with label for your x-axis.
yTitle_char = "LGR5 (AU)", #Character value with label for your y-axis.
axisTextSize_num = 26, #Numeric value to specify the text size of the axes values.
axisTitleSize_num = 30, #Numeric value to specify the text size of the axes titles.
legendPosition_char = "right", #Character value specifying the legend position. Default set to "none."
legendTextSize_num = 15, #Numeric value to specify the text size of the legend text.
legendTitleSize_num = 18, #Numeric value to specify the text size of the legend title.
legendTitle_char = "Color Key", #Character value with label for your legend.
jitter_num = 0.25, #Numeric value specifying the horizontal jitter or horizontal range datapoints can appear within a given condition.
legendSize_num = 1, #Numeric value specifying the size of the icons in the legend.
extraMargin_num = 1.5 #Numeric value specifying the extra margin space you want in your plot (in cm).
)
ggsave("AU_Normalize_custom.png",
plot = qPCR_plot4_custom,
units = 'in',
width = 8,
height = 8)
Expected output
Customize your plot using commands/tools compatible with ggplot2
library(ggsignif)
qPCR_plot4_custom2<-qPCR_plot4+
scale_fill_manual(values = c("#8B92D2","#C3EDD9","#E01542","#95F5FF","#EB6B50"))+
geom_signif(comparisons = list(c('Control','4 ng/mL Drug'),
c('Control','0.5 ng/mL Drug'),
c('Control','1 ng/mL Drug'),
c('Control','2 ng/mL Drug')),
step_increase = 0.18,
test = "wilcox.test",
textsize=6)+
ylim(0,34)
ggsave("AU_Normalize_custom2.png",
plot = qPCR_plot4_custom2,
units = 'in',
width = 8,
height = 8)Expected output
Create a heatmap using Z-score values
library(ComplexHeatmap)
library(circlize)
library(dplyr)
#establish color palette for heatmap
col_fun = colorRamp2(c(-3,-1.5, 0, 1.5,3), c("purple","blue", "white","orange", "red")) #numeric values based off of min, max values of Z-scores
col_fun(seq(-3, 3))
#Create dataframe for heatmap and populate it with Z-scores calculated by quickZScore()
#In this case, columns should represent samples. Rows should represent genes.
heatmap_df<-data.frame(matrix(
ncol = length(unique(ZScore_AU_Normalize$Sample_Name)),
nrow = length(unique(ZScore_AU_Normalize$Target_Gene))))
rownames(heatmap_df)<-unique(ZScore_AU_Normalize$Target_Gene)
colnames(heatmap_df)<-unique(ZScore_AU_Normalize$Sample_Name)
for(i in 1:ncol(heatmap_df)){
temp<-subset(ZScore_AU_Normalize,
ZScore_AU_Normalize$Sample_Name == colnames(heatmap_df)[i])
for(j in 1:nrow(heatmap_df)){
temp2<-subset(temp,
temp$Target_Gene == rownames(heatmap_df)[j])
heatmap_df[j,i]<-temp2[1,13]
}
}
#Create and format annotation file. Rownames should be the sample names. Condition should be in the first (and only) column.
annotation_df<-data.frame(ZScore_AU_Normalize$Sample_Name,ZScore_AU_Normalize$Condition)
annotation_df<-distinct(annotation_df)
rownames(annotation_df)<-annotation_df$ZScore_AU_Normalize.Sample_Name
annotation_df<-subset(annotation_df, select = -ZScore_AU_Normalize.Sample_Name)
colnames(annotation_df)<-"Condition"
#Set colors for annotation
colours <- list('Condition' = c('Control' = 'lightgray',
'4 ng/mL Drug' = 'purple',
'0.5 ng/mL Drug' = "lightblue",
'1 ng/mL Drug' = "blue",
'2 ng/mL Drug' = "darkblue"))
colAnn <- HeatmapAnnotation(df = annotation_df,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
#Generate and export heatmap
png("ZScore_Heatmap.png",width=13,height=3.25,units="in",res=1200)
Heatmap(
data.matrix(heatmap_df),
name = "Z-Score",
col = col_fun,
show_row_names = TRUE,
show_column_names = TRUE,
cluster_rows = TRUE,
cluster_columns = TRUE,
show_column_dend = TRUE,
show_row_dend = TRUE,
row_dend_reorder = TRUE,
column_dend_reorder = TRUE,
clustering_method_rows = "ward.D2",
clustering_method_columns = "ward.D2",
width = unit(250, "mm"),
height = unit(50, "mm"),
top_annotation=colAnn
)
while (!is.null(dev.list())) dev.off()Expected output
Create a heatmap for RQVs to show log2(FC) and significance
#Generate heatmap with average fold change and pvalues
library(dplyr)
RQV_noNormalized_signif$Average_foldChange<-RQV_noNormalized_signif$Average_Condition1/RQV_noNormalized_signif$Average_Condition2
RQV_noNormalized_signif <- as.data.frame(RQV_noNormalized_signif) %>%
mutate(pvalue_cutoff = ifelse(wilcox_pvalue < 0.05 ,
"wilcox pvalue < 0.05",
"wilcox pvalue > 0.05"))
RQV_noNormalized_signif$log2FC<-log2(RQV_noNormalized_signif$Average_foldChange)
#Create color palette for datapoints (log2 FC)
#establish color palette for heatmap
paletteLength <- 100
colors <- colorRampPalette(c("purple","blue","grey90","orange","red"))(paletteLength)
#Generate dotplot
ggplot(RQV_noNormalized_signif,
aes(x=Condition1,
y = Gene_Target,
color = log2FC,
shape = factor(pvalue_cutoff, c('wilcox pvalue > 0.05', 'wilcox pvalue < 0.05'))
)) +
geom_point(size = 6)+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1,vjust=1))+
scale_color_gradientn(colors = colors,
limits = c(-3,3)
)+
labs(color = "log2(Fold Change)",
shape = "Wilcox P-value Cutoff")+
theme(axis.text=element_text(size=15),
axis.title.x=element_text(size=20),
axis.title.y=element_text(size=20),
legend.title = element_text(size=18),
legend.text = element_text(size=14)) +
theme(axis.text.x = element_text(colour="black"),
axis.text.y = element_text(colour="black"))
ggsave("RQV no normalize average heatmap.png",
units = 'in',
width = 7,
height = 5.5)Expected output
