- WBPaper00065623:158
CellRanger, DecontX, Monocle3, Louvain algorithm.
Single-cell RNA-Seq cell group 158 expressed in: CAN.
- WBPaper00061651:CAN_enriched
Genes that pass the Bonferroni threshold for multiple comparisons (q < 0.05) are significantly enriched.
Transcripts enriched in CAN according to single cell RNAseq.
- WBPaper00050496:unc-39(hp701)_regulated
DESeq
Genes regulated by unc-39(hp701). Authors compared genes enriched in FACS sorted GFP+ cells (RID, ALA, AIY, CAN and a sheath cell) vs. whole animal in WT hpIs202[Pceh-10GFP] (WT enriched sets), and genes enriched in FACS sorted GFP+ cells (ALA, AIY, CAN and a sheath cell) vs. whole animal in unc-39(hp701) hpIs202[Pceh-10GFP] (unc-39 enriched sets) at L2 larva stage. This set of genes are WT enriched but not unc-39 enriched, suggesting they are enriched in RID neurons.
- WBPaper00050496:RID_enriched
DESeq
Genes enriched in RID neurons. Authors compared genes enriched in FACS sorted GFP+ cells (RID, ALA, AIY, CAN and a sheath cell) vs. whole animal in WT hpIs202[Pceh-10GFP] (WT enriched sets), and genes enriched in FACS sorted GFP+ cells (ALA, AIY, CAN and a sheath cell) vs. whole animal in unc-39(hp701) hpIs202[Pceh-10GFP] (unc-39 enriched sets) at L2 larva stage. This set of genes are WT enriched but not unc-39 enriched, suggesting they are enriched in RID neurons.
- WBPaper00040821:Au-NP_regulated
Hierarchical clustering was performed in Partek to confirm that the samples match to the treatment groups. Analysis of variance (ANOVA) was used to partition the variance due to treatment from technical and biological noise. The list of differentially expressed genes was generated by identifying the genes showing fold change of more than 1.5 and less than -1.5 at p < 0.05 with and without multiple sample correction, False Discovery Rate (FDR). False discovery correction according to Benjamini and Hochberg produced a list of 37 significant transcripts. FDR was not applied when selecting differential expressed genes, because this approach can increase the type II error and result in elimination of the genes responsive to the treatment.
Genes with differeiential expression after exposed to Au-NP.
- WBPaper00037113:DZN_16C_down-regulated
The Rank Product package was used to identify the differentially expressed genes between controls and treatment in each experiment. Briefly, genes were ranked based on up- or downregulation by the treatment in each experiment. Then, for each gene a combined probability was calculated as a rank product (RP). The RP values were used to rank the genes based on how likely it was to observe them by chance at that particular position on the list of differentially expressed genes. The RP can be interpreted as a p-value. To determine significance levels, the RP method uses a permutation-based estimation procedure to transform the p-value into an e-value that addresses the multiple testing problem derived from testing many genes simultaneously. Genes with a percentage of false-positives (PFP) < 0.05 were considered differentially expressed between treatments and control in each experiment. This method has the advantage to identify genes with a response to the toxicants even when the absolute effect of the response was low. Because authors used sub-lethal concentrations of the toxicants, methods that use thresholds based on absolute fold change would not identify small changes in gene expression. Moreover, RP has proved to be a robust method for comparing microarray data from different sources and experiments.
Down-regulated genes under 1 mg/l DZN treatment at 16 centigrade.
- WBPaper00037113:DZN_16C_up-regulated
The Rank Product package was used to identify the differentially expressed genes between controls and treatment in each experiment. Briefly, genes were ranked based on up- or downregulation by the treatment in each experiment. Then, for each gene a combined probability was calculated as a rank product (RP). The RP values were used to rank the genes based on how likely it was to observe them by chance at that particular position on the list of differentially expressed genes. The RP can be interpreted as a p-value. To determine significance levels, the RP method uses a permutation-based estimation procedure to transform the p-value into an e-value that addresses the multiple testing problem derived from testing many genes simultaneously. Genes with a percentage of false-positives (PFP) < 0.05 were considered differentially expressed between treatments and control in each experiment. This method has the advantage to identify genes with a response to the toxicants even when the absolute effect of the response was low. Because authors used sub-lethal concentrations of the toxicants, methods that use thresholds based on absolute fold change would not identify small changes in gene expression. Moreover, RP has proved to be a robust method for comparing microarray data from different sources and experiments.
Up-regulated genes under 1 mg/l DZN treatment at 16 centigrade