Tests for checking Batch Effects
Batch 180222 | |
---|---|
Condition crowned | 12 |
Condition worker | 10 |
Standardized Pearson Correlation Coefficient | Cramer’s V | |
---|---|---|
Confounding Coefficients (0=no confounding, 1=complete confounding) | NA | NA |
Full (Condition+Batch) | Condition | Batch | |
---|---|---|---|
Min. | 0 | 0 | 0 |
1st Qu. | 1.138 | 1.138 | 0 |
Median | 4.199 | 4.199 | 0 |
Mean | 6.271 | 6.271 | 0 |
3rd Qu. | 9.48 | 9.48 | 0 |
Max. | 52.33 | 52.33 | 0 |
Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | Ps<0.05 | |
---|---|---|---|---|---|---|---|
Batch P-values | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
Condition P-values | 0.0001421 | 0.1633 | 0.3603 | 0.4117 | 0.6366 | 1 | 0.064 |
Boxplots for all values for each of the samples and are colored by batch membership.
Condition: worker (logFC) | AveExpr | t | P.Value | adj.P.Val | B | |
---|---|---|---|---|---|---|
USP11 | -20.88 | 68.09 | -4.702 | 0.0001287 | 0.6674 | -4.592 |
ADAMTS7 | -72.28 | 218.2 | -4.164 | 0.0004576 | 0.6674 | -4.592 |
PRKACA | -404.2 | 2032 | -3.953 | 0.000754 | 0.6674 | -4.592 |
C3AR1 | -94.08 | 230.3 | -3.905 | 0.0008449 | 0.6674 | -4.592 |
PYCR1 | 151.2 | 401.5 | 3.801 | 0.001079 | 0.6674 | -4.592 |
FAM184A | -77 | 325.5 | -3.777 | 0.001144 | 0.6674 | -4.592 |
HAUS3 | -100.2 | 337.6 | -3.775 | 0.001149 | 0.6674 | -4.592 |
EZH2 | -41.5 | 123.1 | -3.773 | 0.001154 | 0.6674 | -4.592 |
GPATCH4 | -37.88 | 212.9 | -3.732 | 0.001271 | 0.6674 | -4.592 |
GMNC | -88.07 | 166.6 | -3.699 | 0.001374 | 0.6674 | -4.593 |
This plot helps identify outlying samples.
This is a heatmap of the given data matrix showing the batch effects and variations with different conditions.
This is a heatmap of the correlation between samples.
This is a Circular Dendrogram of the given data matrix colored by batch to show the batch effects.
This is a plot of the top two principal components colored by batch to show the batch effects.
Proportion of Variance (%) | Cumulative Proportion of Variance (%) | Percent Variation Explained by Either Condition or Batch | Percent Variation Explained by Condition | Condition Significance (p-value) | Percent Variation Explained by Batch | Batch Significance (p-value) | |
---|---|---|---|---|---|---|---|
PC1 | 33.03 | 33.03 | 10.6 | 10.6 | 0.1401 | 0 | 1 |
PC2 | 9.175 | 42.21 | 3.9 | 3.9 | 0.3783 | 0 | 1 |
PC3 | 7.547 | 49.75 | 0 | 0 | 0.9608 | 0 | 1 |
PC4 | 6.085 | 55.84 | 0.2 | 0.2 | 0.8256 | 0 | 1 |
PC5 | 5.095 | 60.93 | 1.8 | 1.8 | 0.5498 | 0 | 1 |
PC6 | 4.268 | 65.2 | 12 | 12 | 0.1145 | 0 | 1 |
PC7 | 3.723 | 68.93 | 5.3 | 5.3 | 0.303 | 0 | 1 |
PC8 | 3.549 | 72.47 | 0 | 0 | 0.9347 | 0 | 1 |
PC9 | 3.027 | 75.5 | 2.6 | 2.6 | 0.47 | 0 | 1 |
PC10 | 2.702 | 78.2 | 16.1 | 16.1 | 0.06464 | 0 | 1 |
PC11 | 2.648 | 80.85 | 23.3 | 23.3 | 0.0229 | 0 | 1 |
PC12 | 2.471 | 83.32 | 4.1 | 4.1 | 0.3635 | 0 | 1 |
PC13 | 2.277 | 85.6 | 0.1 | 0.1 | 0.9102 | 0 | 1 |
PC14 | 2.127 | 87.72 | 0 | 0 | 0.9988 | 0 | 1 |
PC15 | 2.044 | 89.77 | 9.2 | 9.2 | 0.1708 | 0 | 1 |
PC16 | 1.909 | 91.68 | 0.5 | 0.5 | 0.7557 | 0 | 1 |
PC17 | 1.795 | 93.47 | 5.7 | 5.7 | 0.2847 | 0 | 1 |
PC18 | 1.721 | 95.19 | 1 | 1 | 0.6505 | 0 | 1 |
PC19 | 1.63 | 96.82 | 0.9 | 0.9 | 0.6736 | 0 | 1 |
PC20 | 1.621 | 98.45 | 0.4 | 0.4 | 0.7881 | 0 | 1 |
PC21 | 1.555 | 100 | 2.3 | 2.3 | 0.5028 | 0 | 1 |
PC22 | 3.539e-29 | 100 | 6.3 | 6.3 | 0.2613 | 0 | 1 |
This is a heatmap plot showing the variation of gene expression mean, variance, skewness and kurtosis between samples grouped by batch to see the batch effects variation
## Note: Sample-wise p-value is calculated for the variation across samples on the measure across genes. Gene-wise p-value is calculated for the variation of each gene between batches on the measure across each batch. If the data is quantum normalized, then the Sample-wise measure across genes is same for all samples and Gene-wise p-value is a good measure.
This is a plot showing whether parametric or non-parameteric prior is appropriate for this data. It also shows the Kolmogorov-Smirnov test comparing the parametric and non-parameteric prior distribution.
## Warning in combatPlot(shinyInput$lcounts, batch = shinyInput$batch, mod = mod): There is no batch
## Number of Surrogate Variables found in the given data: 2