Tests for checking Batch Effects
Batch 180222 | |
---|---|
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. | 0.635 | 0.635 | 0 |
Median | 2.516 | 2.516 | 0 |
Mean | 4.241 | 4.241 | 0 |
3rd Qu. | 5.898 | 5.898 | 0 |
Max. | 55.12 | 55.12 | 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 | 7.598e-05 | 0.2761 | 0.4808 | 0.4983 | 0.7245 | 1 | 0.0276 |
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 | |
---|---|---|---|---|---|---|
BNC1 | 107.1 | 121.2 | 5.022 | 6.045e-05 | 0.9287 | -4.593 |
ZADH2 | 557.1 | 2735 | 4.464 | 0.0002241 | 0.9957 | -4.593 |
C2ORF72 | -78.03 | 279.9 | -4.053 | 0.0005934 | 0.9957 | -4.593 |
NT5DC3 | 26.25 | 95.18 | 3.779 | 0.001133 | 0.9957 | -4.593 |
PHLDB3 | -45.47 | 203 | -3.779 | 0.001134 | 0.9957 | -4.593 |
ACKR3 | 270.5 | 903.6 | 3.772 | 0.001153 | 0.9957 | -4.593 |
ATOH8 | 207.7 | 702.6 | 3.691 | 0.001395 | 0.9957 | -4.593 |
MUM1L1 | 10.62 | 31.41 | 3.63 | 0.001611 | 0.9957 | -4.594 |
ARC | 57.13 | 148.1 | 3.608 | 0.001696 | 0.9957 | -4.594 |
METTL21B | 47.07 | 176.2 | 3.542 | 0.00198 | 0.9957 | -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 | 36.88 | 36.88 | 3.6 | 3.6 | 0.3998 | 0 | 1 |
PC2 | 10.39 | 47.27 | 0 | 0 | 0.9829 | 0 | 1 |
PC3 | 6.404 | 53.68 | 9.9 | 9.9 | 0.1537 | 0 | 1 |
PC4 | 6.147 | 59.82 | 2.5 | 2.5 | 0.4809 | 0 | 1 |
PC5 | 5.564 | 65.39 | 9.3 | 9.3 | 0.1676 | 0 | 1 |
PC6 | 3.566 | 68.95 | 4.3 | 4.3 | 0.3537 | 0 | 1 |
PC7 | 3.01 | 71.96 | 3.3 | 3.3 | 0.4218 | 0 | 1 |
PC8 | 2.964 | 74.93 | 1.2 | 1.2 | 0.6326 | 0 | 1 |
PC9 | 2.859 | 77.79 | 0.5 | 0.5 | 0.7642 | 0 | 1 |
PC10 | 2.523 | 80.31 | 12.6 | 12.6 | 0.1056 | 0 | 1 |
PC11 | 2.312 | 82.62 | 7.1 | 7.1 | 0.231 | 0 | 1 |
PC12 | 2.074 | 84.69 | 16.4 | 16.4 | 0.0618 | 0 | 1 |
PC13 | 1.995 | 86.69 | 0.4 | 0.4 | 0.771 | 0 | 1 |
PC14 | 1.922 | 88.61 | 4.9 | 4.9 | 0.3211 | 0 | 1 |
PC15 | 1.862 | 90.47 | 6.5 | 6.5 | 0.2533 | 0 | 1 |
PC16 | 1.781 | 92.25 | 0.5 | 0.5 | 0.758 | 0 | 1 |
PC17 | 1.687 | 93.94 | 4.4 | 4.4 | 0.3468 | 0 | 1 |
PC18 | 1.637 | 95.58 | 0.4 | 0.4 | 0.7756 | 0 | 1 |
PC19 | 1.534 | 97.11 | 5.5 | 5.5 | 0.2944 | 0 | 1 |
PC20 | 1.494 | 98.61 | 5.2 | 5.2 | 0.3081 | 0 | 1 |
PC21 | 1.394 | 100 | 1.7 | 1.7 | 0.5658 | 0 | 1 |
PC22 | 5.891e-29 | 100 | 2.6 | 2.6 | 0.4699 | 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