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. | 0.508 | 0.508 | 0 |
Median | 1.944 | 1.944 | 0 |
Mean | 3.425 | 3.425 | 0 |
3rd Qu. | 4.701 | 4.701 | 0 |
Max. | 52.65 | 52.65 | 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 | 0.0001325 | 0.3324 | 0.536 | 0.5384 | 0.7526 | 1 | 0.01553 |
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 | |
---|---|---|---|---|---|---|
MYH7 | -5.583 | 13.05 | -4.103 | 0.0005264 | 0.9991 | -4.593 |
B3GNTL1 | -8.567 | 48.77 | -3.564 | 0.001877 | 0.9991 | -4.593 |
ACCSL | -15.43 | 70.82 | -3.405 | 0.002726 | 0.9991 | -4.593 |
FCHSD1 | -109.7 | 749.4 | -3.19 | 0.004488 | 0.9991 | -4.593 |
C2ORF72 | -47.95 | 158.5 | -3.158 | 0.004831 | 0.9991 | -4.593 |
SLC17A5 | -70.6 | 489.9 | -3.118 | 0.005288 | 0.9991 | -4.593 |
KCTD12 | -28.83 | 127.7 | -3.098 | 0.005541 | 0.9991 | -4.593 |
GALNT14 | -31.62 | 181 | -2.951 | 0.007746 | 0.9991 | -4.594 |
ITIH4 | 6.967 | 19.5 | 2.946 | 0.007823 | 0.9991 | -4.594 |
DKC1 | -14.97 | 93.36 | -2.944 | 0.007867 | 0.9991 | -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 | 38.25 | 38.25 | 2.5 | 2.5 | 0.4782 | 0 | 1 |
PC2 | 12.7 | 50.94 | 0.3 | 0.3 | 0.8043 | 0 | 1 |
PC3 | 7.74 | 58.68 | 3.2 | 3.2 | 0.4279 | 0 | 1 |
PC4 | 4.775 | 63.46 | 9.2 | 9.2 | 0.1694 | 0 | 1 |
PC5 | 3.938 | 67.4 | 2.3 | 2.3 | 0.5046 | 0 | 1 |
PC6 | 3.525 | 70.92 | 0.7 | 0.7 | 0.7133 | 0 | 1 |
PC7 | 3.02 | 73.94 | 9 | 9 | 0.1748 | 0 | 1 |
PC8 | 2.651 | 76.59 | 4.6 | 4.6 | 0.3391 | 0 | 1 |
PC9 | 2.56 | 79.15 | 3.2 | 3.2 | 0.4268 | 0 | 1 |
PC10 | 2.22 | 81.37 | 7.1 | 7.1 | 0.2303 | 0 | 1 |
PC11 | 2.159 | 83.53 | 5.2 | 5.2 | 0.3075 | 0 | 1 |
PC12 | 1.932 | 85.46 | 0 | 0 | 0.9588 | 0 | 1 |
PC13 | 1.914 | 87.38 | 2.5 | 2.5 | 0.486 | 0 | 1 |
PC14 | 1.795 | 89.17 | 10 | 10 | 0.1512 | 0 | 1 |
PC15 | 1.711 | 90.88 | 15.4 | 15.4 | 0.07071 | 0 | 1 |
PC16 | 1.645 | 92.53 | 1.3 | 1.3 | 0.6124 | 0 | 1 |
PC17 | 1.561 | 94.09 | 6.6 | 6.6 | 0.2494 | 0 | 1 |
PC18 | 1.534 | 95.62 | 3.1 | 3.1 | 0.4349 | 0 | 1 |
PC19 | 1.51 | 97.13 | 9.7 | 9.7 | 0.1592 | 0 | 1 |
PC20 | 1.443 | 98.58 | 0 | 0 | 0.9688 | 0 | 1 |
PC21 | 1.425 | 100 | 4.2 | 4.2 | 0.3609 | 0 | 1 |
PC22 | 7.771e-29 | 100 | 0.1 | 0.1 | 0.9048 | 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