Tests for checking Batch Effects
Batch 180305 | |
---|---|
Condition crowned | 5 |
Condition worker | 5 |
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. | 6.947 | 6.947 | 0 |
Median | 20.7 | 20.7 | 0 |
Mean | 25.09 | 25.09 | 0 |
3rd Qu. | 39.22 | 39.22 | 0 |
Max. | 92.99 | 92.99 | 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 | 6.797e-06 | 0.05274 | 0.1864 | 0.2876 | 0.4618 | 1 | 0.2424 |
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 | |
---|---|---|---|---|---|---|
CAPN6 | 1834 | 1888 | 10.18 | 4.369e-06 | 0.04769 | -4.103 |
COL4A3 | 1497 | 1429 | 9.273 | 9.17e-06 | 0.04769 | -4.112 |
KEL | 206.8 | 501.4 | 8.608 | 1.642e-05 | 0.04769 | -4.12 |
POLE2 | 40.4 | 60 | 8.501 | 1.811e-05 | 0.04769 | -4.122 |
SKA1 | 86.4 | 108.4 | 8.37 | 2.042e-05 | 0.04769 | -4.124 |
GINS3 | 64.4 | 92.2 | 8.348 | 2.085e-05 | 0.04769 | -4.124 |
MASTL | 58.4 | 109 | 8.202 | 2.388e-05 | 0.04769 | -4.126 |
DIAPH3 | 137.6 | 169.4 | 7.986 | 2.932e-05 | 0.04769 | -4.13 |
ESCO2 | 88.2 | 106.9 | 7.912 | 3.15e-05 | 0.04769 | -4.131 |
GAS6 | 929.6 | 2904 | 7.901 | 3.183e-05 | 0.04769 | -4.131 |
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 | 46.5 | 46.5 | 37.2 | 37.2 | 0.06124 | 0 | 1 |
PC2 | 17.69 | 64.18 | 35.2 | 35.2 | 0.07062 | 0 | 1 |
PC3 | 10.57 | 74.76 | 0 | 0 | 0.963 | 0 | 1 |
PC4 | 6.874 | 81.63 | 17 | 17 | 0.2368 | 0 | 1 |
PC5 | 5.089 | 86.72 | 1.7 | 1.7 | 0.7167 | 0 | 1 |
PC6 | 3.744 | 90.46 | 4.7 | 4.7 | 0.5484 | 0 | 1 |
PC7 | 3.671 | 94.14 | 3 | 3 | 0.6329 | 0 | 1 |
PC8 | 3.354 | 97.49 | 0.7 | 0.7 | 0.8209 | 0 | 1 |
PC9 | 2.51 | 100 | 0.5 | 0.5 | 0.84 | 0 | 1 |
PC10 | 1.275e-28 | 100 | 21.5 | 21.5 | 0.1771 | 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