Tests for checking Batch Effects
Batch 180319 | |
---|---|
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. | 1.44 | 1.44 | 0 |
Median | 5.655 | 5.655 | 0 |
Mean | 8.791 | 8.791 | 0 |
3rd Qu. | 13.35 | 13.35 | 0 |
Max. | 55.04 | 55.04 | 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 | 7.738e-05 | 0.09447 | 0.2866 | 0.361 | 0.5948 | 1 | 0.1637 |
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 | |
---|---|---|---|---|---|---|
HOXB3 | -10 | 13.95 | -4.586 | 0.0001679 | 0.1884 | -2.683 |
ERG | -20.52 | 36.09 | -4.566 | 0.0001758 | 0.1884 | -2.693 |
BCL6B | -35.9 | 120.2 | -4.563 | 0.0001771 | 0.1884 | -2.694 |
SEMA3G | -81.67 | 210.5 | -4.54 | 0.000187 | 0.1884 | -2.706 |
TNIP1 | -285.8 | 2255 | -4.481 | 0.0002148 | 0.1884 | -2.737 |
NTF3 | -17.7 | 37.45 | -4.466 | 0.0002226 | 0.1884 | -2.744 |
FAM212B | -39.13 | 110.5 | -4.453 | 0.0002296 | 0.1884 | -2.751 |
NFKBIA | -341.1 | 1434 | -4.443 | 0.0002352 | 0.1884 | -2.756 |
CCL25 | 27.97 | 46.05 | 4.423 | 0.0002464 | 0.1884 | -2.767 |
UNC93A | 134.5 | 366.5 | 4.41 | 0.0002544 | 0.1884 | -2.774 |
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 | 22.56 | 22.56 | 29.3 | 29.3 | 0.00932 | 0 | 1 |
PC2 | 13.36 | 35.92 | 0.1 | 0.1 | 0.8958 | 0 | 1 |
PC3 | 10.55 | 46.48 | 0.7 | 0.7 | 0.7082 | 0 | 1 |
PC4 | 7.288 | 53.77 | 1.3 | 1.3 | 0.6182 | 0 | 1 |
PC5 | 5.704 | 59.47 | 8.4 | 8.4 | 0.1906 | 0 | 1 |
PC6 | 5.131 | 64.6 | 9 | 9 | 0.1738 | 0 | 1 |
PC7 | 4.204 | 68.81 | 0 | 0 | 0.9851 | 0 | 1 |
PC8 | 3.635 | 72.44 | 2.5 | 2.5 | 0.4843 | 0 | 1 |
PC9 | 3.417 | 75.86 | 3.8 | 3.8 | 0.3828 | 0 | 1 |
PC10 | 2.905 | 78.76 | 4.7 | 4.7 | 0.3338 | 0 | 1 |
PC11 | 2.743 | 81.5 | 0.4 | 0.4 | 0.7817 | 0 | 1 |
PC12 | 2.443 | 83.95 | 0.2 | 0.2 | 0.8285 | 0 | 1 |
PC13 | 2.22 | 86.17 | 4.9 | 4.9 | 0.324 | 0 | 1 |
PC14 | 2.11 | 88.28 | 4.2 | 4.2 | 0.362 | 0 | 1 |
PC15 | 1.866 | 90.14 | 1.9 | 1.9 | 0.5379 | 0 | 1 |
PC16 | 1.823 | 91.97 | 4.3 | 4.3 | 0.3551 | 0 | 1 |
PC17 | 1.718 | 93.68 | 4.8 | 4.8 | 0.328 | 0 | 1 |
PC18 | 1.659 | 95.34 | 0.1 | 0.1 | 0.9139 | 0 | 1 |
PC19 | 1.628 | 96.97 | 2.7 | 2.7 | 0.4663 | 0 | 1 |
PC20 | 1.574 | 98.54 | 7.6 | 7.6 | 0.2138 | 0 | 1 |
PC21 | 1.456 | 100 | 9.2 | 9.2 | 0.17 | 0 | 1 |
PC22 | 6.183e-29 | 100 | 3.2 | 3.2 | 0.4249 | 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