Tests for checking Batch Effects
Batch 180209 | |
---|---|
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. | 0.7422 | 0.7422 | 0 |
Median | 3.038 | 3.038 | 0 |
Mean | 5.298 | 5.298 | 0 |
3rd Qu. | 7.535 | 7.535 | 0 |
Max. | 51.74 | 51.74 | 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 | 0.0001614 | 0.2163 | 0.4379 | 0.463 | 0.703 | 1 | 0.05301 |
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 | |
---|---|---|---|---|---|---|
TTC30B | 34.15 | 124.8 | 4.658 | 0.0001417 | 0.9156 | -4.593 |
KHDRBS2 | 27.73 | 85.27 | 4.407 | 0.0002566 | 0.9156 | -4.594 |
MRC1 | 813.6 | 2872 | 4.372 | 0.0002785 | 0.9156 | -4.594 |
SLC36A1 | 155.9 | 668.3 | 4.202 | 0.0004165 | 0.9156 | -4.594 |
TANC2 | 32.05 | 122.3 | 4.051 | 0.0005953 | 0.9156 | -4.594 |
SCNN1A | 17.13 | 34.45 | 3.896 | 0.00086 | 0.9156 | -4.594 |
TOB2 | 222.2 | 1982 | 3.856 | 0.000946 | 0.9156 | -4.594 |
P4HB | 730.6 | 6006 | 3.834 | 0.0009964 | 0.9156 | -4.594 |
G6PD | 234.6 | 1828 | 3.829 | 0.001008 | 0.9156 | -4.594 |
SRGN | 599.6 | 2622 | 3.786 | 0.001116 | 0.9156 | -4.594 |
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 | 17.27 | 17.27 | 7.5 | 7.5 | 0.2164 | 0 | 1 |
PC2 | 14.07 | 31.34 | 3.7 | 3.7 | 0.3944 | 0 | 1 |
PC3 | 11 | 42.34 | 0.8 | 0.8 | 0.697 | 0 | 1 |
PC4 | 9.503 | 51.84 | 1.1 | 1.1 | 0.6458 | 0 | 1 |
PC5 | 6.383 | 58.23 | 0 | 0 | 0.9267 | 0 | 1 |
PC6 | 5.728 | 63.95 | 34.5 | 34.5 | 0.00408 | 0 | 1 |
PC7 | 4.258 | 68.21 | 4.1 | 4.1 | 0.3686 | 0 | 1 |
PC8 | 3.606 | 71.82 | 9.7 | 9.7 | 0.1582 | 0 | 1 |
PC9 | 3.325 | 75.14 | 1.6 | 1.6 | 0.5738 | 0 | 1 |
PC10 | 3.055 | 78.2 | 0 | 0 | 0.9786 | 0 | 1 |
PC11 | 2.609 | 80.81 | 10.6 | 10.6 | 0.1401 | 0 | 1 |
PC12 | 2.48 | 83.29 | 0.3 | 0.3 | 0.8155 | 0 | 1 |
PC13 | 2.288 | 85.58 | 0.7 | 0.7 | 0.7206 | 0 | 1 |
PC14 | 2.15 | 87.73 | 0.5 | 0.5 | 0.749 | 0 | 1 |
PC15 | 1.947 | 89.67 | 0.2 | 0.2 | 0.8618 | 0 | 1 |
PC16 | 1.903 | 91.58 | 8.9 | 8.9 | 0.1772 | 0 | 1 |
PC17 | 1.846 | 93.42 | 3 | 3 | 0.4377 | 0 | 1 |
PC18 | 1.751 | 95.17 | 0.3 | 0.3 | 0.8042 | 0 | 1 |
PC19 | 1.664 | 96.84 | 1.6 | 1.6 | 0.5765 | 0 | 1 |
PC20 | 1.596 | 98.43 | 0.4 | 0.4 | 0.7922 | 0 | 1 |
PC21 | 1.568 | 100 | 10.7 | 10.7 | 0.1371 | 0 | 1 |
PC22 | 7.599e-29 | 100 | 2.2 | 2.2 | 0.5056 | 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: 1