Tests for checking Batch Effects
Batch 180212 | |
---|---|
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. | 0.574 | 0.574 | 0 |
Median | 2.494 | 2.494 | 0 |
Mean | 4.461 | 4.461 | 0 |
3rd Qu. | 6.316 | 6.316 | 0 |
Max. | 47.3 | 47.3 | 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 | 0.0004042 | 0.2592 | 0.4828 | 0.4968 | 0.7376 | 1 | 0.03345 |
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 | |
---|---|---|---|---|---|---|
ZBTB14 | -33.8 | 192.1 | -4.265 | 0.0003593 | 0.9975 | -4.591 |
PCDHB5 | -8.483 | 20.23 | -4.135 | 0.0004881 | 0.9975 | -4.591 |
NQO1 | 56.93 | 169 | 3.782 | 0.001125 | 0.9975 | -4.592 |
ZNF576 | 8.617 | 32 | 3.758 | 0.001192 | 0.9975 | -4.592 |
RASL11A | -22.52 | 75.68 | -3.751 | 0.00121 | 0.9975 | -4.592 |
ZNF267 | -5.65 | 18.68 | -3.73 | 0.001272 | 0.9975 | -4.592 |
IFI27L2 | 20.25 | 65.95 | 3.689 | 0.001402 | 0.9975 | -4.592 |
EFCAB6 | 7 | 13.68 | 3.652 | 0.001531 | 0.9975 | -4.592 |
TNNT1 | 69.75 | 137 | 3.623 | 0.001637 | 0.9975 | -4.592 |
CTH | -42.88 | 124.6 | -3.612 | 0.001679 | 0.9975 | -4.592 |
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 | 26.95 | 26.95 | 4.4 | 4.4 | 0.3516 | 0 | 1 |
PC2 | 11.89 | 38.84 | 0 | 0 | 0.9357 | 0 | 1 |
PC3 | 7.537 | 46.38 | 4.2 | 4.2 | 0.3614 | 0 | 1 |
PC4 | 6.037 | 52.41 | 8.2 | 8.2 | 0.1967 | 0 | 1 |
PC5 | 5.292 | 57.71 | 11 | 11 | 0.1324 | 0 | 1 |
PC6 | 4.237 | 61.94 | 0.5 | 0.5 | 0.7483 | 0 | 1 |
PC7 | 3.864 | 65.81 | 5.6 | 5.6 | 0.2888 | 0 | 1 |
PC8 | 3.237 | 69.04 | 2 | 2 | 0.5254 | 0 | 1 |
PC9 | 3.115 | 72.16 | 0.5 | 0.5 | 0.7486 | 0 | 1 |
PC10 | 3.053 | 75.21 | 6 | 6 | 0.2709 | 0 | 1 |
PC11 | 2.922 | 78.13 | 0.2 | 0.2 | 0.8451 | 0 | 1 |
PC12 | 2.692 | 80.82 | 20.3 | 20.3 | 0.03545 | 0 | 1 |
PC13 | 2.558 | 83.38 | 3.1 | 3.1 | 0.4344 | 0 | 1 |
PC14 | 2.41 | 85.79 | 17.1 | 17.1 | 0.05547 | 0 | 1 |
PC15 | 2.32 | 88.11 | 3.8 | 3.8 | 0.3841 | 0 | 1 |
PC16 | 2.245 | 90.36 | 0.1 | 0.1 | 0.9064 | 0 | 1 |
PC17 | 2.068 | 92.43 | 1.8 | 1.8 | 0.5537 | 0 | 1 |
PC18 | 2.021 | 94.45 | 2.4 | 2.4 | 0.4925 | 0 | 1 |
PC19 | 1.959 | 96.41 | 8 | 8 | 0.2035 | 0 | 1 |
PC20 | 1.816 | 98.22 | 0.9 | 0.9 | 0.6803 | 0 | 1 |
PC21 | 1.778 | 100 | 0 | 0 | 0.9849 | 0 | 1 |
PC22 | 4.816e-29 | 100 | 17.6 | 17.6 | 0.0519 | 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: 0