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FASDA-FOLD-CHANGE(1) General Commands Manual FASDA-FOLD-CHANGE(1) NAME fasda fold-change - Compute fold-change and P-values from normalized counts SYNOPSIS fasda fold-change [--output file.txt] \ normalized-counts1.tsv normalized-counts2.tsv \ [normalized-counts3.tsv ...] OPTIONS --output file.txt Report fold-change and P-values to file.txt instead of the de- fault stdout. DESCRIPTION fasda fold-change computes fold-change and P-values for two or more conditions. The input is two or more tab-separated value (TSV) files containing normalized counts for all conditions. These files are gen- erally produced by fasda normalize which is turn takes its input from kallisto or fasda abundance output. The Mann-Whitney U-test (A.K.A. Wilcoxon rank sum test) is used to com- pute P-values for a minimum of 8 replicates per condition. Exact P- values are computed for 2 to 4 replicates and near-exact P-values for 5 to 7 replicates (the enormous space of possible count pairs is down- sampled to keep run time within reason). For each pair of conditions, fasda fold-change reports the mean normal- ized counts for each condition, the standard deviation / mean counts for each condition, the percent agreement across replicates as to whether the fold-change is up or down, the fold-change using total counts for each condition, and the P-value for this set of counts across the two conditions. Feature MNC1 MNC2 SD/C1 SD/C2 %Agr FC 1-2 P-val YPL071C_mRNA 34.4 52.5 0.4 0.3 66 1.52 0.220 YLL050C_mRNA 441.9 912.4 0.4 0.3 100 2.06 0.070 YMR172W_mRNA 54.6 111.2 0.4 0.3 100 2.04 0.059 YOR185C_mRNA 59.2 94.8 0.2 0.2 100 1.60 0.044 YLL032C_mRNA 33.2 31.9 0.5 0.4 66 0.96 0.918 YBR225W_mRNA 57.4 103.3 0.4 0.3 66 1.80 0.138 P-values will generally be lower when fold-changes are higher, when mean normalized counts are higher and when standard deviation is lower. We report standard deviation divided by mean normalized counts to pro- vide an immediate sense of how variable the counts are across repli- cates for each feature. E.g. the actual standard deviation for condi- tion 1 in YPL071C_mRNA (using rounded output) would be 34.4 * 0.4 = 13.76. Interpreting Results P-values from any differential analysis tool should never be taken too seriously. There are countless uncontrollable biological variables that can affect the RNA abundance in a cell. There are also numerous sources of experimental error in sample prep and sequencing that can lead to inaccuracy in read counts. Technical replicates (replicates from the same biological sample) and spike-in controls can reveal some of these technical issues, but do not address biological variations. Another problem is that many biology experiments use only 3 replicates. We simply cannot draw high confidence from any statistics based on 3 samples. P-value calculations typically make the same assumptions about all genes. In reality, a 2-fold change in expression could be hugely sig- nificant for one gene under certain conditions and completely meaning- less for a different gene or different conditions. Statistical rou- tines have no knowledge of the biology that determines this. There is huge variability on the computational side as well. Well-es- tablished differential analysis tools commonly report very different sets of genes as differentially expressed. Li, et al (https://doi.org/10.1186/s13059-022-02648-4) reported that 23.71% to 75% of the DEGs identified by DESeq2 were missed by edgeR. In one data set tested, DESeq2 and edgeR had only an 8% overlap in the DEGs they identified. Hence, simply assuming that P-values < 0.05 represent significant changes while others do not would be foolish. Rather than try too hard to produce adjusted P-values that you can take on faith, we provide simple, honest statistics and leave it to you to consider them care- fully. From our preliminary experiments, we have found that P-values between 0.05 and 0.20 are often questionable and may warrant a closer look at the raw data. A quick look at the individual read counts for each replicate in these cases can be enlightening. You will usually see a high variance in counts across individuals, sometimes with up-regula- tion in some individuals and down-regulation in others. For example, consider the following gene, for which Sleuth reported a P-value of 0.0132, while the exact P-value computed by FASDA is 0.116. The kallisto estimated counts show that one replicate was up-regulated almost 4-fold, another almost 2-fold, and the third was slightly down- regulated. A P-value of 0.01 would not likely make anyone suspect this situation. 0.116, on the other hand, tells us that there is a good chance this is significant, but maybe we should take a minute or two to look at the read counts and consider the biology behind them. This is a tiny investment that will help us better decide whether a costly ex- perimental verification is warranted. FASDA Sleuth Feature MNC1 MNC2 FC 1-2 SC1 SC2 SFC SPV ENSMUST00000017610 7620.5 16006.7 2.1 0.116 191.4 433.5 2.3 0.0132 Kallisto estimated counts: R1 R2 R3 MNC1 5382.16 8567.4 6986.43 MNC2 21519.9 16196.7 6307.52 Conversely, Sleuth produced a P-value of 0.12 for the following, which looks like a slam-dunk given the counts. FASDA Sleuth Feature MNC1 MNC2 FC 1-2 SC1 SC2 SFC SPV ENSMUST00000036928 1165.0 4064.3 3.5 0.024 70.5 300.4 4.3 0.1203 R1 R2 R3 MNC1 1045.41 942.707 1220.02 MNC2 2835.7 4167.81 3718.39 The bottom line is, while the 0.05 rule is a good one mathematically, we cannot count on experimental and computational results reflecting the biology with that much accuracy. Give the results of any differen- tial analysis a generous margin of error, and examine the data more closely for anything within that margin. FILES abundance.tsv: Input with normalized counts for all replicates of 1 condition file.txt: Output containing fold-change and P-values SEE ALSO fasda-abundance(1), fasda-normalize(1) BUGS Please report bugs to the author and send patches in unified diff for- mat. (man diff for more information) AUTHOR J. Bacon FASDA-FOLD-CHANGE(1)
NAME | SYNOPSIS | OPTIONS | DESCRIPTION | Interpreting Results | FILES | SEE ALSO | BUGS | AUTHOR
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