How to check for the presence/absence of unique rows in two different matrices?

Question

[mario.red8976]

Hi to everybody! I'll explain my problem.

I have two matrices with data from illumina 850 methylation chip. The person that performed the analysis, gave me a matrix with beta values with cytosine names for rows and samples for columns.

My problem is that among the cytosines, there are also the control probes that I want to remove.

At the moment, I have two matrices: the one explained above and another one with the annotation, with cytosines for rows and all the metadata for columns. The number of rows of course is different because the control probes are not annotated.

My question is:

Using R, how can I compare the two matrices checking for the presence of unique row names in the beta value matrix, so the control probes, that are not present in annotation matrix?

The aim is to find all the cytosines in the beta values that are not present in annotation so to remove them.

Thank you for your help!

[zx8754]

Something like:

mySubset <- m1[ rownames(m1) %in% rownames(m2), ]

Answer 1

[mario.red8976]

Hi! I think I solved using dplyr and the function "semi_join", which keeps the matching rows from the two matrices and the column of only one.

Thank you for your answers!

Answer 2

[Chirag Parsania]

dplyr::left_join(matrix1 , matrix2 ,by = "common_column_to_be_used_to_join_two_matrix" ) will give all the rows from matrix1 and all the columns from matrix2.

PS : First you need to convert matrix in to dataframe or tibble objects.

========== EDIT==========

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

library(tibble)

mtcars1 <- mtcars %>% rownames_to_column() %>% dplyr::select(1:6)

mtcars2 <- mtcars %>% rownames_to_column() %>% dplyr::select(1,7:12)

mtcars1 %>% left_join( mtcars2)
#> Joining, by = "rowname"
#>                rowname  mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> 1            Mazda RX4 21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> 2        Mazda RX4 Wag 21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> 3           Datsun 710 22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> 4       Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> 5    Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> 6              Valiant 18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> 7           Duster 360 14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> 8            Merc 240D 24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> 9             Merc 230 22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> 10            Merc 280 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> 11           Merc 280C 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> 12          Merc 450SE 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> 13          Merc 450SL 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> 14         Merc 450SLC 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> 15  Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> 16 Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> 17   Chrysler Imperial 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> 18            Fiat 128 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> 19         Honda Civic 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> 20      Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> 21       Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> 22    Dodge Challenger 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> 23         AMC Javelin 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> 24          Camaro Z28 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> 25    Pontiac Firebird 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> 26           Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> 27       Porsche 914-2 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> 28        Lotus Europa 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> 29      Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> 30        Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> 31       Maserati Bora 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> 32          Volvo 142E 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

^{Created on 2020-02-13 by the [reprex package](https://reprex.tidyverse.org) (v0.3.0)}

[mario.red8976]

Thank you for your answer! I tried your code but actually it is not properly fitted for what I need, because it will merge the columns of the two matrices, while my aim is basically to remove some of the rows from the beta value matrix with the control probes (that i want to remove) and that are absent (these rows) from the matrix with the annotated cytosine. Do you have any idea of how to do this?

Thank you again!