Per-gene results of an analysis

scmpResults() collects the per-gene results stored in a ScMaSigPro object in one table with a row per gene. scmpToRowData() writes that table into the rowData() of a SingleCellExperiment, such as the one the analysis started from, so that the results travel with the data.

Usage

scmpResults(scmpObj)

scmpToRowData(sce, scmpObj)

Arguments

scmpObj: A ScMaSigPro object.
sce: A SingleCellExperiment, or another SummarizedExperiment, whose row names are gene names.

Value

scmpResults(): a DataFrame. scmpToRowData(): sce with the new rowData() columns.

Details

scmpResults() has one row per gene of the object (the rows of the Sparse slot), named by gene, and the columns of the steps that have been run:

sc.p.vector(): p_value and adj_p_value, the p-value of the full model and its adjusted value, NA for the genes that were not fitted (too few values or only zero counts); non_flat, TRUE for the genes it selected.
sc.t.fit(): model_p_value and r_squared, the p-value and the R-squared of the model selected by the stepwise regression (the p-value and R-squared columns of showSol()), NA for the genes without a row there; influential, TRUE for the genes with influential observations (the columns of showInflu()).
sc.filter(): one column per gene list, named sig_ followed by the name of the list, TRUE for the genes of the list.
sc.cluster.trend(): cluster, the cluster of each clustered gene, NA for the other genes.

The logical columns are FALSE, never NA, for the genes outside a set.

scmpToRowData() matches the genes by name. It removes the columns of rowData(sce) whose names start with scmp_, as an earlier call writes them, and adds the columns of scmpResults() with that prefix, such as scmp_p_value. The genes of sce that are not in the object get NA in every new column, and the genes of the object that are not in sce are left out. It stops if no gene matches.

Author

Priyansh Srivastava spriyansh29@gmail.com

Examples

data("scmp.ob", package = "scMaSigPro")
res <- scmpResults(scmp.ob)
head(res)
#> DataFrame with 6 rows and 9 columns
#>           p_value adj_p_value  non_flat model_p_value r_squared influential
#>         <numeric>   <numeric> <logical>     <numeric> <numeric>   <logical>
#> Gene1 2.21646e-01 3.46322e-01     FALSE            NA        NA       FALSE
#> Gene2 1.30835e-13 4.84576e-13      TRUE   1.60139e-09  0.726015       FALSE
#> Gene3 2.51249e-24 1.14204e-23      TRUE   3.98589e-20  0.873163       FALSE
#> Gene4 1.96112e-56 1.96112e-55      TRUE   1.23046e-66  0.955362       FALSE
#> Gene5 6.16240e-02 1.04447e-01     FALSE            NA        NA       FALSE
#> Gene6 5.80848e-01 7.21780e-01     FALSE            NA        NA       FALSE
#>       sig_Path2vsPath1 sig_Path1   cluster
#>              <logical> <logical> <integer>
#> Gene1            FALSE     FALSE        NA
#> Gene2             TRUE     FALSE         1
#> Gene3             TRUE      TRUE         2
#> Gene4             TRUE     FALSE         2
#> Gene5            FALSE     FALSE        NA
#> Gene6            FALSE     FALSE        NA
# The genes of the "Path1" list, ordered by R-squared
sig <- res[res$sig_Path1, ]
head(sig[order(sig$r_squared, decreasing = TRUE), ])
#> DataFrame with 6 rows and 9 columns
#>             p_value  adj_p_value  non_flat model_p_value r_squared influential
#>           <numeric>    <numeric> <logical>     <numeric> <numeric>   <logical>
#> Gene10 8.34110e-248 8.34110e-246      TRUE  7.18412e-269  0.989436       FALSE
#> Gene51 5.22783e-176 2.61392e-174      TRUE  5.22783e-176  0.985444       FALSE
#> Gene18  4.03251e-90  8.06502e-89      TRUE   1.78301e-94  0.971161       FALSE
#> Gene86  1.35821e-90  3.39552e-89      TRUE   9.68258e-97  0.970970       FALSE
#> Gene46  1.63491e-80  2.33559e-79      TRUE   1.14734e-86  0.968460       FALSE
#> Gene72  5.81817e-83  9.69695e-82      TRUE   2.55237e-69  0.961039       FALSE
#>        sig_Path2vsPath1 sig_Path1   cluster
#>               <logical> <logical> <integer>
#> Gene10             TRUE      TRUE         2
#> Gene51             TRUE      TRUE         2
#> Gene18             TRUE      TRUE         3
#> Gene86             TRUE      TRUE         3
#> Gene46             TRUE      TRUE         3
#> Gene72             TRUE      TRUE         2

# scmp.ob was made from the bundled splat.sim data set
data("splat.sim", package = "scMaSigPro")
sce <- scmpToRowData(splat.sim, scmp.ob)
head(SummarizedExperiment::rowData(sce)[, c("Gene", "scmp_p_value")])
#> DataFrame with 6 rows and 2 columns
#>              Gene scmp_p_value
#>       <character>    <numeric>
#> Gene1       Gene1  2.21646e-01
#> Gene2       Gene2  1.30835e-13
#> Gene3       Gene3  2.51249e-24
#> Gene4       Gene4  1.96112e-56
#> Gene5       Gene5  6.16240e-02
#> Gene6       Gene6  5.80848e-01