This function perform a normalization on the data considering the fact that each shRNA has a defined length so this will not influence the data. Basically is computed the sum for each row and then multiply by 1e6. At the end the data obtained will be Count Per Million.
Arguments
- screenR_Object
The ScreenR object obtained using the
create_screenr_object
Examples
object <- get0("object", envir = asNamespace("ScreenR"))
object <- normalize_data(object)
slot(object, "normalized_count_table")
#> # A tibble: 5,320 × 15
#> Barcode T1 T2 Time3_TRT_A Time3_TRT_B Time3_TRT_C Time3_A Time3_B
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 10208 330. 437. 365. 439. 311. 358. 425.
#> 2 10502 26.2 34.6 20.5 59.5 16.7 49.3 17.4
#> 3 10796 141. 156. 164. 124. 132. 120. 121.
#> 4 11089 356. 424. 336. 424. 375. 388. 346.
#> 5 11382 67.2 65.7 82.6 36.9 26.3 58.4 65.4
#> 6 11675 687. 775. 722. 682. 669. 820. 797.
#> 7 11969 455. 548. 623. 540. 558. 488. 438.
#> 8 12263 144. 119. 112. 138. 95.0 158. 128.
#> 9 12555 406. 413. 459. 518. 483. 408. 425.
#> 10 12847 43.7 42.6 30.2 34.9 29.6 34.6 20.5
#> # ℹ 5,310 more rows
#> # ℹ 7 more variables: Time3_C <dbl>, Time4_TRT_A <dbl>, Time4_TRT_B <dbl>,
#> # Time4_TRT_C <dbl>, Time4_A <dbl>, Time4_B <dbl>, Time4_c <dbl>